code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diff... | 713 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( a : int = 4 ) -> list[list[int]]:
"""simple docstring"""
lowercase_ : Tuple = abs(a ) or 4
return [[1 + x + y * row_size for x in range(a )] for y in range(a ... | 7 | 0 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_to... | 714 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _UpperCAmelCase ( a : Dict , a : Optional[int] , a : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase_ : Any = {
'en':... | 7 | 0 |
from __future__ import annotations
A: str = "Muhammad Umer Farooq"
A: Any = "MIT"
A: int = "1.0.0"
A: Union[str, Any] = "Muhammad Umer Farooq"
A: List[Any] = "contact@muhammadumerfarooq.me"
A: List[str] = "Alpha"
import re
from html.... | 715 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
A: Tuple = l... | 7 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( a : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase_ : List[Any] = []
if len(a ) == 1:
return [nums.copy()]
for _ in range(len(a ) ):
lowercase_ ... | 716 |
'''simple docstring'''
import os
from distutils.util import strtobool
def _UpperCAmelCase ( a : Any , a : int ) -> Any:
"""simple docstring"""
for e in env_keys:
lowercase_ : Optional[Any] = int(os.environ.get(a , -1 ) ... | 7 | 0 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoMo... | 717 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
A: int = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two class... | 7 | 0 |
'''simple docstring'''
import re
def _UpperCAmelCase ( a : str ) -> List[Any]:
"""simple docstring"""
if len(re.findall('[ATCG]' , snake_case_ ) ) != len(snake_case_ ):
raise ValueError('Invalid Strand' )
return dna.transl... | 718 |
'''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
A: Dict = logging.get_logger(__name__)
A: Optional[Any] ... | 7 | 0 |
import os
import sys
import unittest
A: 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: E402
get_model_to_test_mapping,
get_mode... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: int = logging.get_logger(__name__)
A: int = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
cl... | 7 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
A: Any = logging.get_logger(__name__)
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self , ... | 720 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ) -> Optional[Any]:
lowercase_ : ... | 7 | 0 |
'''simple docstring'''
import numpy
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase , _lowercase ) -> None:
lowercase_ : Optional[int] = input_array
# Random initial weights are assigned where... | 721 |
'''simple docstring'''
import argparse
A: List[Any] = "docs/source/_static/js/custom.js"
def _UpperCAmelCase ( a : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(a , encoding='utf-8' , newline='\n' ) as f:
... | 7 | 0 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A: Any = "\\n@misc{chen2021evaluating,\n title={Evaluati... | 700 |
'''simple docstring'''
def _UpperCAmelCase ( a : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
lowercase_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(a ):
if len(a ) <... | 7 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( a : list ) -> list:
"""simple docstring"""
lowercase_ : int = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowercase_ : List[str] = True
... | 701 |
'''simple docstring'''
def _UpperCAmelCase ( a : int , a : int ) -> int:
"""simple docstring"""
while second != 0:
lowercase_ : Any = first & second
first ^= second
lowercase_ : List[str] = c << 1
... | 7 | 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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Ima... | 702 |
'''simple docstring'''
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase ) -> Union[str, Any]:
lowercase_ : Dict = n
lowercase_ : Dict = [None] * self.n
lowercase_ : Tuple ... | 7 | 0 |
'''simple docstring'''
from math import factorial, pi
def _UpperCAmelCase ( a : float , a : int = 3_0 ) -> float:
"""simple docstring"""
if not isinstance(a , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or floa... | 703 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import ... | 7 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( a : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
lowercase_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(a ):
if len(a ) <... | 704 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
A: int = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["e... | 7 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A: Dict = logging.get_logger(__name__)
class __magic_name__ ( UpperCAmelCase... | 705 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
A: Any ... | 7 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A: Dict = logging.get_logger(__name__)
A: ... | 706 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: int = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerCon... | 7 | 0 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklear... | 707 |
'''simple docstring'''
def _UpperCAmelCase ( a : str ) -> str:
"""simple docstring"""
lowercase_ : Dict = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowercase_ : Dict = ''
lowercase_ : Any = ... | 7 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.util... | 708 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __magi... | 7 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A: Dict = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
... | 709 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
... | 7 | 0 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa... | 710 |
'''simple docstring'''
def _UpperCAmelCase ( a : list ) -> list:
"""simple docstring"""
for i in range(len(a ) - 1 , 0 , -1 ):
lowercase_ : Any = False
for j in range(a , 0 , -1 ):
... | 7 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( UpperCAmelCase_ ... | 711 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = ['transformers', 'torch', 'note_seq']
def __init__( self , *_lowercase ... | 7 | 0 |
A: Optional[Any] = tuple[float, float, float]
A: Union[str, Any] = tuple[float, float, float]
def _UpperCAmelCase ( a : Pointad , a : Pointad ) -> Vectorad:
"""simple docstring"""
lowercase_ : int = end_pointa[0] - end_point... | 712 |
'''simple docstring'''
def _UpperCAmelCase ( a : str , a : str ) -> float:
"""simple docstring"""
def get_matched_characters(a : str , a : str ) -> str:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple ... | 7 | 0 |
def _UpperCAmelCase ( a : int ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
lowercase_ : str = 4
lowercase_ : Union[str, Any] =... | 713 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( a : int = 4 ) -> list[list[int]]:
"""simple docstring"""
lowercase_ : Tuple = abs(a ) or 4
return [[1 + x + y * row_size for x in range(a )] for y in range(a ... | 7 | 0 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A: ... | 714 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _UpperCAmelCase ( a : Dict , a : Optional[int] , a : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase_ : Any = {
'en':... | 7 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet im... | 715 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
A: Tuple = l... | 7 | 0 |
'''simple docstring'''
A: List[Any] = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.co... | 716 |
'''simple docstring'''
import os
from distutils.util import strtobool
def _UpperCAmelCase ( a : Any , a : int ) -> Any:
"""simple docstring"""
for e in env_keys:
lowercase_ : Optional[Any] = int(os.environ.get(a , -1 ) ... | 7 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: Optional[int] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise Optio... | 717 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
A: int = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two class... | 7 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A: int = {
"configuration_squeezebert": [
"SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SqueezeBertConfig",
... | 718 |
'''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
A: Dict = logging.get_logger(__name__)
A: Optional[Any] ... | 7 | 0 |
import os
from pathlib import Path
def _UpperCAmelCase ( a : Dict , a : Optional[int] , a : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase_ : Any = {
'en': 'Machine learning is great, isn\'t it?',
'ru': 'Машинн... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: int = logging.get_logger(__name__)
A: int = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
cl... | 7 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: List[str] = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_availab... | 720 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ) -> Optional[Any]:
lowercase_ : ... | 7 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgum... | 721 |
'''simple docstring'''
import argparse
A: List[Any] = "docs/source/_static/js/custom.js"
def _UpperCAmelCase ( a : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(a , encoding='utf-8' , newline='\n' ) as f:
... | 7 | 0 |
'''simple docstring'''
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
A: str = logging.get_logger(__name__)
def _UpperCAmelCase ( a : List[str] , a :... | 700 |
'''simple docstring'''
def _UpperCAmelCase ( a : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
lowercase_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(a ):
if len(a ) <... | 7 | 0 |
'''simple docstring'''
from collections.abc import Callable
def _UpperCAmelCase ( a : Callable[[float], float] , a : float , a : float ) -> float:
"""simple docstring"""
lowercase_ : float = a
lowercase_ : float = b
... | 701 |
'''simple docstring'''
def _UpperCAmelCase ( a : int , a : int ) -> int:
"""simple docstring"""
while second != 0:
lowercase_ : Any = first & second
first ^= second
lowercase_ : List[str] = c << 1
... | 7 | 0 |
'''simple docstring'''
from typing import 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... | 702 |
'''simple docstring'''
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase ) -> Union[str, Any]:
lowercase_ : Dict = n
lowercase_ : Dict = [None] * self.n
lowercase_ : Tuple ... | 7 | 0 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils ... | 703 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import ... | 7 | 0 |
'''simple docstring'''
import requests
A: Tuple = "YOUR API KEY"
def _UpperCAmelCase ( a : str , a : str = giphy_api_key ) -> list:
"""simple docstring"""
lowercase_ : Dict = '+'.join(query.split() )
lowercase_ : str... | 704 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
A: int = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["e... | 7 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( a : list ) -> list:
"""simple docstring"""
for i in range(len(a ) - 1 , 0 , -1 ):
lowercase_ : Any = False
for j in range(a , 0 , -1 ):
... | 705 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
A: Any ... | 7 | 0 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( a : str , a : str ) -> bool:
"""simple docstring"""
lowercase_ : Union[str, Any] = get_failure_array(a )
# 2) Step through text searching for pattern
... | 706 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: int = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerCon... | 7 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transfor... | 707 |
'''simple docstring'''
def _UpperCAmelCase ( a : str ) -> str:
"""simple docstring"""
lowercase_ : Dict = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowercase_ : Dict = ''
lowercase_ : Any = ... | 7 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.... | 708 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __magi... | 7 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A: Dict = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
A: ... | 709 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
... | 7 | 0 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 710 |
'''simple docstring'''
def _UpperCAmelCase ( a : list ) -> list:
"""simple docstring"""
for i in range(len(a ) - 1 , 0 , -1 ):
lowercase_ : Any = False
for j in range(a , 0 , -1 ):
... | 7 | 0 |
'''simple docstring'''
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxrun... | 711 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = ['transformers', 'torch', 'note_seq']
def __init__( self , *_lowercase ... | 7 | 0 |
def _UpperCAmelCase ( a : Tuple ) -> str:
"""simple docstring"""
lowercase_ : Tuple = len(a )
for i in range(length - 1 ):
lowercase_ : str = i
for k in range(i + 1 , a ):
if ... | 712 |
'''simple docstring'''
def _UpperCAmelCase ( a : str , a : str ) -> float:
"""simple docstring"""
def get_matched_characters(a : str , a : str ) -> str:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple ... | 7 | 0 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
A: Dict = logging.get_logger(__name__)
A: Optional[int] = {"vocab_file": "vocab.txt"}
A: Opti... | 713 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( a : int = 4 ) -> list[list[int]]:
"""simple docstring"""
lowercase_ : Tuple = abs(a ) or 4
return [[1 + x + y * row_size for x in range(a )] for y in range(a ... | 7 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
... | 714 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _UpperCAmelCase ( a : Dict , a : Optional[int] , a : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase_ : Any = {
'en':... | 7 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: Tuple = logging.get_logger(__name__)
A: Optional[int] = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class __magic_name__ ( UpperCAm... | 715 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
A: Tuple = l... | 7 | 0 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
... | 716 |
'''simple docstring'''
import os
from distutils.util import strtobool
def _UpperCAmelCase ( a : Any , a : int ) -> Any:
"""simple docstring"""
for e in env_keys:
lowercase_ : Optional[Any] = int(os.environ.get(a , -1 ) ... | 7 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( a : str , a : int ) -> str:
"""simple docstring"""
lowercase_ : list[list[str]] = [[] for _ in range(a )]
lowercase_ : Dict = key - 1
if key <= 0:
... | 717 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
A: int = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two class... | 7 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __magic_name__ ( UpperCAmelCas... | 718 |
'''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
A: Dict = logging.get_logger(__name__)
A: Optional[Any] ... | 7 | 0 |
A: Union[str, Any] = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
1_0: "a",
1_1: "b",
1_2: "c",
1_3: "d",
1_4: "e",
1_5: "f",
}
def _UpperCAmelCase ( a : float ) -> str:
... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: int = logging.get_logger(__name__)
A: int = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
cl... | 7 | 0 |
'''simple docstring'''
import random
def _UpperCAmelCase ( a : int ) -> bool:
"""simple docstring"""
lowercase_ : List[str] = num - 1
lowercase_ : Dict = 0
while s % 2 == 0:
lowercase_ : Union[str, Any] ... | 720 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ) -> Optional[Any]:
lowercase_ : ... | 7 | 0 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configur... | 721 |
'''simple docstring'''
import argparse
A: List[Any] = "docs/source/_static/js/custom.js"
def _UpperCAmelCase ( a : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(a , encoding='utf-8' , newline='\n' ) as f:
... | 7 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A: List[Any] = logging.get_logger(__name__)
A: Union[str, Any] = {
"vocab_file": "vocab.json",
... | 700 |
'''simple docstring'''
def _UpperCAmelCase ( a : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
lowercase_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(a ):
if len(a ) <... | 7 | 0 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_con... | 701 |
'''simple docstring'''
def _UpperCAmelCase ( a : int , a : int ) -> int:
"""simple docstring"""
while second != 0:
lowercase_ : Any = first & second
first ^= second
lowercase_ : List[str] = c << 1
... | 7 | 0 |
'''simple docstring'''
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_... | 702 |
'''simple docstring'''
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase ) -> Union[str, Any]:
lowercase_ : Dict = n
lowercase_ : Dict = [None] * self.n
lowercase_ : Tuple ... | 7 | 0 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
A: int = collections.namedtuple("_Datasets"... | 703 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import ... | 7 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: int = logging.get_logger(__name__)
A: Tuple = {
"BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.jso... | 704 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
A: int = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["e... | 7 | 0 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A: int = logging.getLogger(__name__)
@dataclass
class __magic_name__ ( UpperCAmelCa... | 705 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
A: Any ... | 7 | 0 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
A: Tuple = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "... | 706 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: int = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerCon... | 7 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbone... | 707 |
'''simple docstring'''
def _UpperCAmelCase ( a : str ) -> str:
"""simple docstring"""
lowercase_ : Dict = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowercase_ : Dict = ''
lowercase_ : Any = ... | 7 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A: List[Any] = logging.get_logger(__name__)
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( s... | 708 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __magi... | 7 | 0 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str]... | 709 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
... | 7 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: int = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerC... | 710 |
'''simple docstring'''
def _UpperCAmelCase ( a : list ) -> list:
"""simple docstring"""
for i in range(len(a ) - 1 , 0 , -1 ):
lowercase_ : Any = False
for j in range(a , 0 , -1 ):
... | 7 | 0 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import l... | 711 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = ['transformers', 'torch', 'note_seq']
def __init__( self , *_lowercase ... | 7 | 0 |
def _UpperCAmelCase ( a : Optional[Any] , a : Any , a : List[str]=False ) -> str:
"""simple docstring"""
if isinstance(a , a ) and isinstance(a , a ):
lowercase_ : Dict = len(set_a.intersection(a ) ... | 712 |
'''simple docstring'''
def _UpperCAmelCase ( a : str , a : str ) -> float:
"""simple docstring"""
def get_matched_characters(a : str , a : str ) -> str:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple ... | 7 | 0 |
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase ) -> Union[str, Any]:
lowercase_ : Dict = n
lowercase_ : Dict = [None] * self.n
lowercase_ : Tuple = 0 # index of the ... | 713 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( a : int = 4 ) -> list[list[int]]:
"""simple docstring"""
lowercase_ : Tuple = abs(a ) or 4
return [[1 + x + y * row_size for x in range(a )] for y in range(a ... | 7 | 0 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def _UpperCAmelCase ( a : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError('math domain error' )
return quad(a , 0 , a ... | 714 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _UpperCAmelCase ( a : Dict , a : Optional[int] , a : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase_ : Any = {
'en':... | 7 | 0 |
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
A: Dict = logging.get_logger(__name__)
A: Optional[Any] = {
"google/vit-b... | 715 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
A: Tuple = l... | 7 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
A: str = logging.getLogger(__name__)
class __magic_name__ ( UpperCAmelCase_ ):
"""si... | 716 |
'''simple docstring'''
import os
from distutils.util import strtobool
def _UpperCAmelCase ( a : Any , a : int ) -> Any:
"""simple docstring"""
for e in env_keys:
lowercase_ : Optional[Any] = int(os.environ.get(a , -1 ) ... | 7 | 0 |
'''simple docstring'''
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase , _lowercase , _lowercase ) -> Union[str, Any]:
lowercase_ : str = None
lowercase_ : Dict ... | 717 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
A: int = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two class... | 7 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
... | 718 |
'''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
A: Dict = logging.get_logger(__name__)
A: Optional[Any] ... | 7 | 0 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import repl... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: int = logging.get_logger(__name__)
A: int = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
cl... | 7 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( a : int ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect num... | 720 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ) -> Optional[Any]:
lowercase_ : ... | 7 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: Dict = logging.get_logger(__name__)
A: Optional[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/... | 721 |
'''simple docstring'''
import argparse
A: List[Any] = "docs/source/_static/js/custom.js"
def _UpperCAmelCase ( a : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(a , encoding='utf-8' , newline='\n' ) as f:
... | 7 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __magic_name__ :
"""simple docstring"""
SCR... | 700 |
'''simple docstring'''
def _UpperCAmelCase ( a : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
lowercase_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(a ):
if len(a ) <... | 7 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = ['transformers', 'torch', 'note_seq']
def __init__( self , *_lowercase ... | 701 |
'''simple docstring'''
def _UpperCAmelCase ( a : int , a : int ) -> int:
"""simple docstring"""
while second != 0:
lowercase_ : Any = first & second
first ^= second
lowercase_ : List[str] = c << 1
... | 7 | 0 |
'''simple docstring'''
A: Any = 8.314_462 # Unit - J mol-1 K-1
def _UpperCAmelCase ( a : float , a : float , a : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. E... | 702 |
'''simple docstring'''
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase ) -> Union[str, Any]:
lowercase_ : Dict = n
lowercase_ : Dict = [None] * self.n
lowercase_ : Tuple ... | 7 | 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, lo... | 703 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import ... | 7 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( a : str ) -> str:
"""simple docstring"""
return " ".join(
''.join(word[::-1] ) if len(a ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 704 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
A: int = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["e... | 7 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( a : int = 1_0_0 ) -> int:
"""simple docstring"""
lowercase_ : Dict = n * (n + 1) * (2 * n + 1) / 6
lowercase_ : Tuple = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares ... | 705 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
A: Any ... | 7 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A: Optional[Any] = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst o... | 706 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: int = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerCon... | 7 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A: int = logging.get_logger(__name__)
A: Optional[int] = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory... | 707 |
'''simple docstring'''
def _UpperCAmelCase ( a : str ) -> str:
"""simple docstring"""
lowercase_ : Dict = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowercase_ : Dict = ''
lowercase_ : Any = ... | 7 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_p... | 708 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __magi... | 7 | 0 |
'''simple docstring'''
from manim import *
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstring"""
def lowerCamelCase__ ( self ) -> Optional[Any]:
lowercase_ : List[Any] = Rectangle(height=0.5 , width=0.5 )
... | 709 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
... | 7 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a: Tuple = logging.get_logger(__name__)
__a: List[Any] = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all Vi... | 710 |
'''simple docstring'''
def _UpperCAmelCase ( a : list ) -> list:
"""simple docstring"""
for i in range(len(a ) - 1 , 0 , -1 ):
lowercase_ : Any = False
for j in range(a , 0 , -1 ):
... | 7 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A: Tuple = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP... | 711 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = ['transformers', 'torch', 'note_seq']
def __init__( self , *_lowercase ... | 7 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A: Tuple = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnn... | 712 |
'''simple docstring'''
def _UpperCAmelCase ( a : str , a : str ) -> float:
"""simple docstring"""
def get_matched_characters(a : str , a : str ) -> str:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple ... | 7 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: str = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
... | 713 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( a : int = 4 ) -> list[list[int]]:
"""simple docstring"""
lowercase_ : Tuple = abs(a ) or 4
return [[1 + x + y * row_size for x in range(a )] for y in range(a ... | 7 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, 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 .tokeni... | 714 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _UpperCAmelCase ( a : Dict , a : Optional[int] , a : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase_ : Any = {
'en':... | 7 | 0 |
from collections.abc import Sequence
def _UpperCAmelCase ( a : Sequence[float] , a : bool = False ) -> float:
"""simple docstring"""
if not arr:
return 0
lowercase_ : Dict = 0 if allow_empty_subarrays else float('-inf' )
... | 715 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
A: Tuple = l... | 7 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
A: int = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two class... | 716 |
'''simple docstring'''
import os
from distutils.util import strtobool
def _UpperCAmelCase ( a : Any , a : int ) -> Any:
"""simple docstring"""
for e in env_keys:
lowercase_ : Optional[Any] = int(os.environ.get(a , -1 ) ... | 7 | 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()
A: Union[str, Any] ... | 717 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
A: int = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two class... | 7 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 'bert-generation'
def __init__( self , _lowercase=5_0358 , _lo... | 718 |
'''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
A: Dict = logging.get_logger(__name__)
A: Optional[Any] ... | 7 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A: Optional[int] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pas... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: int = logging.get_logger(__name__)
A: int = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
cl... | 7 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
A: Tuple = logging.get_logger(__name__)
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstri... | 720 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ) -> Optional[Any]:
lowercase_ : ... | 7 | 0 |
'''simple docstring'''
from collections import deque
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase , _lowercase , _lowercase ) -> None:
lowercase_ : List[str] = process_name # process name
... | 721 |
'''simple docstring'''
import argparse
A: List[Any] = "docs/source/_static/js/custom.js"
def _UpperCAmelCase ( a : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(a , encoding='utf-8' , newline='\n' ) as f:
... | 7 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( UpperCAmelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = (PNDMScheduler,)
SCRE... | 700 |
'''simple docstring'''
def _UpperCAmelCase ( a : list[list[float]] ) -> list[list[float]]:
"""simple docstring"""
lowercase_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(a ):
if len(a ) <... | 7 | 0 |
'''simple docstring'''
import unittest
import numpy as np
def _UpperCAmelCase ( a : np.ndarray , a : np.ndarray , a : np.ndarray , a : np.ndarray | None = None , ) -> np.ndarray:
"""simple docstring"""
lowercase_ : str = np.shape(a... | 701 |
'''simple docstring'''
def _UpperCAmelCase ( a : int , a : int ) -> int:
"""simple docstring"""
while second != 0:
lowercase_ : Any = first & second
first ^= second
lowercase_ : List[str] = c << 1
... | 7 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( a : int , a : int ) -> int:
"""simple docstring"""
while second != 0:
lowercase_ : Any = first & second
first ^= second
lowercase_ : List[str] = c << 1
... | 702 |
'''simple docstring'''
class __magic_name__ :
"""simple docstring"""
def __init__( self , _lowercase ) -> Union[str, Any]:
lowercase_ : Dict = n
lowercase_ : Dict = [None] * self.n
lowercase_ : Tuple ... | 7 | 0 |
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