code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_u... | 52 | '''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_: Optional[int] =Lock()
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : Dict , snake_case_... | 1 | 0 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from ac... | 150 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ = str(bin(snake_cas... | 1 | 0 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case ( lowercase__ , lowercase__ , lowercase_... | 96 | '''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case_ : list , snake_case_ : int | None = None , snake_case_ : int | None = None ) -> None:
'''simple docstring'''
if start is None:
UpperCAmelCase_... | 1 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condi... | 179 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( UpperCamelCase__ ):
a__ : Optio... | 1 | 0 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from... | 300 | '''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condition... | 1 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 239 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__(self : int , *__a : Dict , *... | 1 | 0 |
import math
def UpperCamelCase ( ) -> None:
"""simple docstring"""
lowercase__ = input("""Enter message: """ )
lowercase__ = int(input(f'''Enter key [2-{len(snake_case_ ) - 1}]: ''' ) )
lowercase__ = input("""Encryption/Decryption [e... | 305 | '''simple docstring'''
from __future__ import annotations
import queue
class __A :
def __init__(self : Optional[Any] , __a : str ):
UpperCAmelCase_ = data
UpperCAmelCase_ = None
UpperCAmelCase_ = None
def l... | 1 | 0 |
"""simple docstring"""
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __magic_na... | 291 | '''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import s... | 1 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__a ... | 337 | '''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTo... | 1 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class A__ ... | 145 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_: Dict =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Tuple ={}
class __A ( UpperCamelCase__ ):
a__ : int = """llama"""
a__ ... | 1 | 0 |
'''simple docstring'''
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
"stable diffusion controlnet",
"0.22.0",
"Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.fla... | 47 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from d... | 1 | 0 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_c... | 52 | '''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __A ( UpperCame... | 1 | 0 |
"""simple docstring"""
import os
from math import logaa
def lowerCAmelCase__ ( _UpperCamelCase : str = "base_exp.txt" ) -> int:
"""simple docstring"""
snake_case = 0
snake_case = 0
for i, line in enumerate(open(os.path.join(os.path.... | 150 | '''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
SCREAMING_SNAKE_CASE_: Optional[Any] ='\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n ... | 1 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
fro... | 96 | '''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelera... | 1 | 0 |
"""simple docstring"""
from __future__ import annotations
a_ = 1.6_0_2_1e-1_9 # units = C
def __lowercase ( snake_case_ : float ,snake_case_ : float ,snake_case_ : float ,) ->tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mobilit... | 179 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(snake_case_ , x % y )
def lowerCAmelCase_ ( snake_case_ : int , ... | 1 | 0 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 300 | '''simple docstring'''
import os
from math import logaa
def lowerCAmelCase_ ( snake_case_ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path... | 1 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Optional[Any] = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mobile... | 239 | '''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_... | 1 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : ... | 305 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_ten... | 1 | 0 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig... | 291 | '''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_REC... | 1 | 0 |
import pprint
import requests
__a = 'https://zenquotes.io/api'
def __lowercase ( ) ->list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def __lowercase ( ) ->list:
"""simple docstring"""
return requests.get... | 337 | '''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
SCREAMING_SNAKE_CASE_: Optional[int] =3_00 # TEMPERATURE (unit = K)
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float , snake_case_ : float , ... | 1 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RE... | 145 | '''simple docstring'''
import math
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
UpperCAmelCase_ = input("Enter message: " )
UpperCAmelCase_ = int(input(f"""Enter key [2-{len(snake_case_ ) - 1}]: """ ) )
UpperCAmelCase_ ... | 1 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Union[str, Any] = {
'configuration_rembe... | 47 | '''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 1 | 0 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__lowerCamelCase : Optional[Any] = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav K... | 52 | '''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_: Optional[int] =Lock()
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : Dict , snake_case_... | 1 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
... | 150 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ = str(bin(snake_cas... | 1 | 0 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 96 | '''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case_ : list , snake_case_ : int | None = None , snake_case_ : int | None = None ) -> None:
'''simple docstring'''
if start is None:
UpperCAmelCase_... | 1 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
a_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is the re... | 179 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( UpperCamelCase__ ):
a__ : Optio... | 1 | 0 |
def __snake_case ( _lowerCAmelCase : int , _lowerCAmelCase : int ) -> str:
return "\n".join(
f"{number} * {i} = {number * i}" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 300 | '''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condition... | 1 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __magic_name__ (... | 239 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__(self : int , *__a : Dict , *... | 1 | 0 |
from collections.abc import Generator
def UpperCamelCase ( ) -> Generator[int, None, None]:
"""simple docstring"""
lowercase__ , lowercase__ = 0, 1
while True:
lowercase__ , lowercase__ = b, a + b
yield b
def UpperCamelCase ( __... | 305 | '''simple docstring'''
from __future__ import annotations
import queue
class __A :
def __init__(self : Optional[Any] , __a : str ):
UpperCAmelCase_ = data
UpperCAmelCase_ = None
UpperCAmelCase_ = None
def l... | 1 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(__name__)
lowerCAmelCase : Optiona... | 291 | '''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import s... | 1 | 0 |
__a = 2_56
# Modulus to hash a string
__a = 1_00_00_03
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
lowercase : Any = len(snake_case_ )
lowercase : int = len(snake_case_ )
... | 337 | '''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTo... | 1 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSche... | 145 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_: Dict =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Tuple ={}
class __A ( UpperCamelCase__ ):
a__ : int = """llama"""
a__ ... | 1 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase : Dict = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer... | 47 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from d... | 1 | 0 |
from datetime import datetime as dt
import os
from github import Github
__lowerCamelCase : Optional[Any] = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def A_ ( ) -> Union[str, Any]:
UpperCa... | 52 | '''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __A ( UpperCame... | 1 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import Tokenizer... | 150 | '''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
SCREAMING_SNAKE_CASE_: Optional[Any] ='\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n ... | 1 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def _snake_case ( lowercase__ ):
return sum... | 96 | '''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelera... | 1 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, l... | 179 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(snake_case_ , x % y )
def lowerCAmelCase_ ( snake_case_ : int , ... | 1 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
fro... | 300 | '''simple docstring'''
import os
from math import logaa
def lowerCAmelCase_ ( snake_case_ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path... | 1 | 0 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCamelCase ( UpperCAmelCase__ : str = "isbn/0140328726" ) -> dict:
lowercase_ : List[Any] = olid.strip().strip("""/""" )... | 239 | '''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_... | 1 | 0 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
... | 305 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_ten... | 1 | 0 |
"""simple docstring"""
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Tuple = ... | 291 | '''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_REC... | 1 | 0 |
def __lowercase ( _UpperCamelCase ) ->bool:
"""simple docstring"""
if not isinstance(snake_case_, snake_case_ ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(snake_case_ ) == 0:
raise ValueError('''Input list must... | 337 | '''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
SCREAMING_SNAKE_CASE_: Optional[int] =3_00 # TEMPERATURE (unit = K)
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float , snake_case_ : float , ... | 1 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCAmelCase ( a_: int )... | 145 | '''simple docstring'''
import math
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
UpperCAmelCase_ = input("Enter message: " )
UpperCAmelCase_ = int(input(f"""Enter key [2-{len(snake_case_ ) - 1}]: """ ) )
UpperCAmelCase_ ... | 1 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging... | 47 | '''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 1 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DU... | 52 | '''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_: Optional[int] =Lock()
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : Dict , snake_case_... | 1 | 0 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self , lowerCAmelCase ):... | 150 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ = str(bin(snake_cas... | 1 | 0 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmu... | 96 | '''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case_ : list , snake_case_ : int | None = None , snake_case_ : int | None = None ) -> None:
'''simple docstring'''
if start is None:
UpperCAmelCase_... | 1 | 0 |
"""simple docstring"""
def __lowercase ( ) ->int:
'''simple docstring'''
return 1
def __lowercase ( snake_case_ : int ) ->int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __lowercase ( ... | 179 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( UpperCamelCase__ ):
a__ : Optio... | 1 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __snake_case ( _lowerCAmelCase : ... | 300 | '''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condition... | 1 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaS... | 239 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__(self : int , *__a : Dict , *... | 1 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def UpperCamelCase ( __magic_name__ : jnp.ndarray , __magic_name__ : int , __magic_name__ : float = 1 , __magic_name__ : float = 1 , __magic_name__ : float = ... | 305 | '''simple docstring'''
from __future__ import annotations
import queue
class __A :
def __init__(self : Optional[Any] , __a : str ):
UpperCAmelCase_ = data
UpperCAmelCase_ = None
UpperCAmelCase_ = None
def l... | 1 | 0 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load... | 291 | '''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import s... | 1 | 0 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__a = numpy.array([0, 0])
__a = numpy.array([0.5, 0.8_6_6_0_2_5_4])
__a = numpy.array([1, 0])
__a = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
... | 337 | '''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTo... | 1 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transfor... | 145 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_: Dict =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Tuple ={}
class __A ( UpperCamelCase__ ):
a__ : int = """llama"""
a__ ... | 1 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowerCamelCase : Any = ... | 47 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from d... | 1 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : List[Any] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
r... | 52 | '''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __A ( UpperCame... | 1 | 0 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
... | 150 | '''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
SCREAMING_SNAKE_CASE_: Optional[Any] ='\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n ... | 1 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
... | 96 | '''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelera... | 1 | 0 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
a_ = Lock()
def __lowercase ( snake_case_ : Dict ,snake_case_ : Dict ,snake_case_ : Optional[int] ,snake_case_ : Tuple ,sna... | 179 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(snake_case_ , x % y )
def lowerCAmelCase_ ( snake_case_ : int , ... | 1 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 300 | '''simple docstring'''
import os
from math import logaa
def lowerCAmelCase_ ( snake_case_ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path... | 1 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tens... | 239 | '''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_... | 1 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 305 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_ten... | 1 | 0 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def a__ ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) =... | 291 | '''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_REC... | 1 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}
class __SCREAMING_SN... | 337 | '''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
SCREAMING_SNAKE_CASE_: Optional[int] =3_00 # TEMPERATURE (unit = K)
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float , snake_case_ : float , ... | 1 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : Optional[Any] = 0
_UpperCAmelCase : List[Any] = len(snake_case_ )
for i in range(n - 1 ):
for j in range(i + 1, snake_case_ ):
if arr[i] > arr[j]:... | 145 | '''simple docstring'''
import math
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
UpperCAmelCase_ = input("Enter message: " )
UpperCAmelCase_ = int(input(f"""Enter key [2-{len(snake_case_ ) - 1}]: """ ) )
UpperCAmelCase_ ... | 1 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.... | 47 | '''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 1 | 0 |
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A__ ( UpperCamelCase__ ):
_UpperCAmelCase :Any ... | 52 | '''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_: Optional[int] =Lock()
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : Dict , snake_case_... | 1 | 0 |
"""simple docstring"""
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE__ = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # ... | 150 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ = str(bin(snake_cas... | 1 | 0 |
"""simple docstring"""
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _snake_case ( lowercase__ ):
_lowerCamelCase : Any = [
'decoder.version',
... | 96 | '''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case_ : list , snake_case_ : int | None = None , snake_case_ : int | None = None ) -> None:
'''simple docstring'''
if start is None:
UpperCAmelCase_... | 1 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_... | 179 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( UpperCamelCase__ ):
a__ : Optio... | 1 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __magic_name__ ( unittest.TestCase , UpperCamelCase__ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple doc... | 300 | '''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condition... | 1 | 0 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import It... | 239 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__(self : int , *__a : Dict , *... | 1 | 0 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Token... | 305 | '''simple docstring'''
from __future__ import annotations
import queue
class __A :
def __init__(self : Optional[Any] , __a : str ):
UpperCAmelCase_ = data
UpperCAmelCase_ = None
UpperCAmelCase_ = None
def l... | 1 | 0 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __magic_name__ ( nn.Module ):
'''simple docstring'''
def __init__( self , _a = 16 , _a = 88 , _a = None ... | 291 | '''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import s... | 1 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATIO... | 337 | '''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTo... | 1 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase__ ):
"""simple docstring"""
def __init__( self : int , *lower... | 145 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_: Dict =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Tuple ={}
class __A ( UpperCamelCase__ ):
a__ : int = """llama"""
a__ ... | 1 | 0 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def _lowerCAmelCase ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : bool = False ) ... | 47 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from d... | 1 | 0 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
defau... | 52 | '''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __A ( UpperCame... | 1 | 0 |
"""simple docstring"""
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _co... | 150 | '''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
SCREAMING_SNAKE_CASE_: Optional[Any] ='\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n ... | 1 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 96 | '''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelera... | 1 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, fl... | 2 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-c... | 2 | 1 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCamelCase : List[Any] = '<<<<<<< This should probably be modified because it mentions: '
... | 2 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : int = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}... | 2 | 1 |
'''simple docstring'''
lowerCamelCase : List[str] = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
... | 2 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase : List[str] ... | 2 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A ) -> list:
"""simple docstring"""
lowercase__ = [0] * len(A )
for i in range(1 , len(A ) ):
# use last results for better performance - dynamic programming
... | 2 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _SCREAMING_SNAKE_CASE (A ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = [
'''encoder.vers... | 2 | 1 |
'''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE (A ) -> int:
"""simple docstring"""
if not isinstance(A , A ):
lowercase__ = f"Input value of [number={number}] must be an integer"
raise TypeError(A )
... | 2 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCamelCase : List[Any] = logging.getLogger(__name__)
class __lowerCAmelCase (lowercase_ ):
... | 2 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Dict = logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class ... | 2 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Dict = logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class ... | 2 | 1 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _SCREAMING_SNAKE_CASE (A , A , A = None ) -> str:
"""simple docstring"""
if version.parse(hfh.__version__ ).re... | 2 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowerCamelCase : Any = re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase : str = re.compile(R'([a-z\d])([A-Z])')
lowerCamelCase : Optional[int] = re.compile(R'(?<!_)_(?!_)')
lowerCamelCase... | 2 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json'... | 2 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 2 | 1 |
'''simple docstring'''
import sys
lowerCamelCase : Optional[Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
... | 2 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A ) -> int:
"""simple docstring"""
if not isinstance(A , A ):
raise TypeError('''only integers accepted as input''' )
else:
lowercase__ = str(abs(A ) ... | 2 | 1 |
'''simple docstring'''
import os
import sys
import unittest
lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E4... | 2 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowerCamelCase : str = Mapping[str, np.ndarray]
lowerCamelCase : List[Any] = Mapping... | 2 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
def __init_... | 2 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (A , A ) -> list[list[int]]:
"""simple docstring"""
lowercase__ = []
create_all_state(1 , A , A , [] , A )
return result
def _SCREAMING_SNAKE_CASE ... | 2 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 2 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCamelCase : Optional[Any] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5... | 2 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
lowerCAmelCase__ : List[Any] = ["""image_processor""", """tok... | 2 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowerCamelCase : List[str] = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned... | 2 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A = 1_000 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , A ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 2 |
'''simple docstring'''
from ....utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
def __init__(self : Optional[int] , UpperCamelCase : Union[str, Any] ... | 2 | 1 |
'''simple docstring'''
from ....utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
def __init__(self : Optional[int] , UpperCamelCase : Union[str, Any] ... | 2 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Tuple = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# S... | 2 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requir... | 2 |
'''simple docstring'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
lowerCamelCase : Any = models.Sequential(... | 2 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
lowerCamelCase : Dic... | 2 |
'''simple docstring'''
class __lowerCAmelCase : # Public class to implement a graph
'''simple docstring'''
def __init__(self : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : list[list[bool]] ):
'''simple ... | 2 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : List[str] = logging.get_logger(__name__)
lowerCamel... | 2 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
lowerCamelCase : Tuple = 'naver-clova-ix/donut-base'
class __lowerCAmelCase (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ (self : int ):
'... | 2 | 1 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
def UpperCamelCase__ (self : Union[str, Any] , UpperCamelCase : int=None , UpperCamelCase : ... | 2 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (A ) -> bool:
"""simple docstring"""
return len(set(A ) ) == len(A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 | 1 |
'''simple docstring'''
import numpy as np
def _SCREAMING_SNAKE_CASE (A , A ) -> np.ndarray:
"""simple docstring"""
return np.where(vector > 0 , A , (alpha * (np.exp(A ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import Ta... | 2 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.