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 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_A : List[Any] ='''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def SCREAMING_SNAKE_CASE_ (Uppe... | 41 | '''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 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampl... | 42 | '''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 |
from scipy.stats import spearmanr
import datasets
__lowercase = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that... | 43 | '''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 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... | 44 | '''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 importlib import import_module
from .logging import get_logger
lowercase_ = get_logger(__name__)
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self , _a , _a=None ):
__a = attrs or []
... | 45 | '''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 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str = "cpu" , SCREAMING_SNAKE_CASE : Union[str, None] = None ):
... | 46 | '''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'''
def _lowerCAmelCase ( _UpperCamelCase : list , _UpperCamelCase : int = 0 ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =length or len(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =False
for i i... | 47 | '''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 |
def A ( _SCREAMING_SNAKE_CASE ) -> int:
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
raise TypeError("Input value must be a 'int' type" )
re... | 48 | '''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 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils ... | 49 | '''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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 50 | '''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 |
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 TokenizerTesterMixin
... | 51 | '''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 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 52 | '''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 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules... | 53 | '''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 warnings
warnings.warn(
'''memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '''
'''`from accelerate import find_executable_batch_size` to avoid this warning.''',
FutureWarning,
)
| 54 | '''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 hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simpl... | 55 | '''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'''
a : Optional[Any] = {
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U':... | 56 | '''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 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , ... | 57 | '''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 math
def lowerCamelCase ( __lowerCamelCase : int ) ->bool:
_SCREAMING_SNAKE_CASE = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__lowerCamelCase )
def lowerCamelCase ( __lowerCa... | 58 | '''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 inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # ... | 59 | '''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 os
import string
import sys
snake_case__ : Optional[int] = 1 << 8
snake_case__ : Union[str, Any] = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
... | 60 | '''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 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class A_ :
'''simple docstring'''
def __init__( self , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : list[dict] = []
self.adlist.append(
{"value": ""... | 61 | '''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 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLo... | 62 | '''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 logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedToken... | 63 | '''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 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class lowercase:
'''simple docstring'''
def __init__( self: Union[str, Any], a_: Optional[Any], a_: int, a_: int ):
'''sim... | 64 | '''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 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class A ( UpperCAmelCase_ ):
def __init__(self : Any , *__UpperCAmelCase : Any , ... | 65 | '''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"""
def A_ ( _lowercase ):
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(_lowercase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 66 | '''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
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , ) -> tuple[int, float, str]:
__lowerCamelCase = cipher_alphabet or [chr(UpperCamelCase__ ) for i ... | 67 | '''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 tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available... | 68 | '''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 __future__ import annotations
from collections.abc import Generator
def UpperCAmelCase ( ) -> Generator[int, None, None]:
snake_case_ = {}
snake_case_ = 2
while True:
snake_case_ = factor_map.pop... | 69 | '''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 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test... | 70 | '''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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ :List[str] = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenizat... | 71 | '''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 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __snake_cas... | 72 | '''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 |
from math import isqrt
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowerCamelCase__ ) + 1 ) )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ = 1_0**6 ) -> int:
__lowerCam... | 73 | '''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_tf_available, is_torch_available
_lowercase = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'... | 74 | '''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 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 75 | '''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 |
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, logging
if is_sentencepiece_a... | 76 | '''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"""
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ( _a):
def __init__( self , a , a ) -> List[Any]:
super().__in... | 77 | '''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 ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_e... | 78 | '''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 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __lowercase ( __lowercase ) -> Dict:
'''simple docstring'''
_A = [
"encoder.version",
... | 79 | '''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'''
from __future__ import annotations
def _UpperCamelCase ( __A , __A , __A ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument m... | 80 | '''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 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..... | 81 | '''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 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _UpperCAmelCase ( snake_case ):
... | 82 | '''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 |
'''simple docstring'''
from collections.abc import Generator
def A__ ( ):
_UpperCamelCase , _UpperCamelCase : Tuple = 0, 1
while True:
_UpperCamelCase , _UpperCamelCase : Union[str, Any] = b, a + b
yield b
def A__ ( UpperC... | 83 | '''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 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
__UpperCAmelCase ... | 84 | '''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 pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
... | 85 | '''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 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstan... | 86 | '''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 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onn... | 87 | '''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 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
... | 88 | '''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'''
def __lowerCamelCase ( lowerCAmelCase_ = 3 , lowerCAmelCase_ = 7 , lowerCAmelCase_ = 1000000 ) -> int:
_a : Tuple = 0
_a : List[Any] = 1
for current_denominator in range(1 , limit + 1 ):
_a ... | 89 | '''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 |
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = n
__lowerCamelCase = [None] * self.n
__lowerCamelCase ... | 90 | '''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 dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class lowerCAmelCase__ ( UpperCAmelCa... | 91 | '''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 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCamelCase__ = 1.054571817E-34 # unit of ℏ : J * s
UpperCamelCase__ = 3E8 # unit of c : m * s^-1
def _a ( SCREAMING_SNAK... | 92 | '''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'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_ut... | 93 | '''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 math import factorial
def __lowerCamelCase ( UpperCAmelCase_ : int = 100 ):
"""simple docstring"""
return sum(map(UpperCAmelCase_ , str(factorial(UpperCAmelCase_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ... | 94 | '''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 |
UpperCAmelCase : Tuple = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCAmelCase : Optional[int] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _A ( SCREAMING_SNAKE_CASE : dict[int, list[int]] , SCREAMING_SNAKE_CASE : int ... | 95 | '''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 os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingI... | 96 | '''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 |
'''simple docstring'''
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
fr... | 97 | '''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"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won... | 98 | '''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 |
lowercase : List[str] = tuple[float, float, float]
lowercase : int = tuple[float, float, float]
def A_ ( A__ , A__ ) -> Vectorad:
a__ : Optional[int] = end_pointa[0] - end_pointa[0]
a__ : str = end_pointa[1] - end_pointa[1]
... | 99 | '''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 json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagem... | 100 | '''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 |
from __future__ import annotations
import math
import random
from typing import Any
class lowercase :
def __init__( self):
lowercase = []
lowercase = 0
lowercase = 0
def A__ ( self):
return s... | 101 | '''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 contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_t... | 102 | '''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 warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 103 | '''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 __future__ import annotations
import time
import numpy as np
lowerCAmelCase__ = [8, 5, 9, 7]
lowerCAmelCase__ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCAmelCase__ = [
[3, 2, 1, 4],
[0... | 104 | '''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 |
"""simple docstring"""
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class __UpperCamelCase ( a__ , unittest.TestCase ):
low... | 105 | '''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"""
__UpperCamelCase : Dict = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.... | 106 | '''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 |
import math
import unittest
def __magic_name__ ( A : int ):
'''simple docstring'''
assert isinstance(A, A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number... | 107 | '''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 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def a__ ( SCREAMING_SNAKE_CASE : int = 8 ):
'''simple docstring'''
lowerCAmelCase : List[str] = ascii_le... | 108 | '''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 __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available,... | 109 | '''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 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_... | 110 | '''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 math
def __lowercase ( snake_case_ : int ) ->list[int]:
'''simple docstring'''
if num <= 0:
__A : Dict = F"""{num}: Invalid input, please enter a positive integer."""
ra... | 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 pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __snake_case ( _lowerCAmelCase : List[str] ) -> List[str]:
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def __snak... | 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 __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 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 transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
A : int = get_tests_dir('fixtures... | 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"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase : Tuple = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
],
'processing... | 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 itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class A__ ( datasets.BuilderConfig ):
"""simple docstring"""
Uppe... | 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 numpy as np
lowerCamelCase : Dict = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class A__ :
def __init__( self : Tuple ) -> L... | 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 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCamelCase : List[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 pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preproc... | 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"""
def _snake_case ( lowercase__ ):
return 10 - x * x
def _snake_case ( lowercase__ , lowercase__ ):
if equation(snake_case_ ) * equation(snake_case_ ) >= 0:
raise ValueError('Wrong space!' )
_l... | 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 unittest
import numpy as np
from transformers import DistilBertConfig, 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():
i... | 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : D... | 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 gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNe... | 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 warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 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 torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( UpperCamelCase__ ):
'''simple docstring'''
__UpperCamelCase = (UnCLIPScheduler,)
def _lowerCAmelCase ... | 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
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->list[str]:
"""simple docstring"""
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueE... | 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 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,
RobertaT... | 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 argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCamelCase : Optional[int] ... | 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_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Dict = {
'configuration_albert':... | 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 gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffu... | 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 json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAme... | 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"""
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(snake_case_ ,x % y )
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ... | 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 PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import ... | 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'''
def lowerCamelCase ( UpperCAmelCase__ : list , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 0 , UpperCAmelCase__ : int = 0 ) -> int:
lowercase_ : List[str] = right or len(... | 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 |
from __future__ import annotations
A : List[str] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class A :
'''simple docstring'''
def __init... | 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"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> None:
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
... | 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 __future__ import annotations
__a = []
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
for i in range(len(snake_case_ ) ):
if board[row][i] == 1:
return False
for i in range... | 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 tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...te... | 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 requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( _UpperCamelCase : int ) -> List[str]:
"""simple docstring"""
if "img_encoder.pos_em... | 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 |
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