code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowercase : Optional[Any] = logging.getLogger()
@unittest.skip('Tempo... | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
def __init__( self :Any , a :str , a :Tuple , a :Dict )... | 232 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Bert... | 232 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 | 1 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
... | 232 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase : List[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 check_copies # noqa: E402... | 232 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 | 1 |
import heapq
import sys
import numpy as np
lowercase : List[Any] = tuple[int, int]
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Any ) -> List[str]:
__UpperCamelCase : str = []
__UpperCamelC... | 232 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import Seq... | 232 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accele... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning ... | 232 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 1 |
from __future__ import annotations
import pandas as pd
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[int] , _lowerCamelCase : list[int] , _lowerCamelCase : int) -> list[int]:
'''simple docstring'''
__UpperCamelCase : Optional[Any]... | 232 |
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_tensor
if is_fl... | 232 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if i... | 232 |
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
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str , _lowerCamelCase : str) -> float:
'''simple docstring'''
def get_matched_characters(_lowerCamelCase : str , _lowerCamelCase : str) -> str:
__UpperCamelCase : Optional[int] = ... | 232 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
lowercase : ... | 232 | 1 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : str = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all C... | 232 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : bool = False) -> bool:
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7,... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Union[str, Any] = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',... | 232 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> int:
'''simple docstring'''
if not head:
return True
# split the list to two parts
__UpperCamelCase , __UpperCamelCase : List[Any] = head.next, head
while... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Optional[int] , a :Collection[float] | None = None ) -> None:
... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> Optional[int]:
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__UpperCamelCase : Any = len(_lowerCamelCase)
... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[str] , _lowerCamelCase : Union[str, Any]) -> Optional[int]:
'''simple docstring'''
print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
for i in range(_lowerCamelCase):
... | 232 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : List[Any] = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.j... | 232 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 1 |
from collections.abc import Sequence
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Sequence[int] | None = None) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
__Up... | 232 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> list[list[int]]:
'''simple docstring'''
__UpperCamelCase : list[list[int]] = []
create_all_state(1 , _lowerCam... | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffus... | 232 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Tuple = logging.get_logger(__name__)
lowercase : Tuple = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://hug... | 232 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :List[Any] , a :Any ) -> int:
__UpperCamelCase : Any = data
__UpperCa... | 232 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 | 1 |
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
lowercase : Optional[Any] = '▁'
lowercase : Any = {'vo... | 232 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 | 1 |
lowercase : List[Any] = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float) -> float:
'''simple docstring'''
if moles < 0 or kel... | 232 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 1 |
import os
from distutils.util import strtobool
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict) -> int:
'''simple docstring'''
for e in env_keys:
__UpperCamelCase : str = int(os.enviro... | 232 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.uti... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
from manim import *
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
def _lowerCamelCase ( self :Dict ) -> List[Any]:
__UpperCamelCase : Dict = Rectangle(height=0.5 , width=0.5 )
__UpperCamelCase : Union[str... | 232 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 1 |
from __future__ import annotations
lowercase : Tuple = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]] , _lowerCamelCase : list[int] , _lowerCamelCase : li... | 232 |
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_tensor
if is_fl... | 232 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLAS... | 232 |
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
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelO... | 232 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
lowercase : ... | 232 | 1 |
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_simplify,
require_tf,
req... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import Tokeniz... | 232 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbed... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1_000 - i , -1_000 - i , -1)) for i in range(1_000)]
lowercase : Optional[int] = generate_large_matrix()
lowercase : List[Any] ... | 232 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 | 1 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 | 1 |
import re
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> bool:
'''simple docstring'''
__UpperCamelCase : List[str] = re.compile(
R"^(?:0|94|\+94|0{2}94)" R"7(0|1|2|4|5|6|7|8)" R"(-| |)" R"\d{7}$")
return bool(re.sear... | 232 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 | 1 |
import math
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float) -> float:
'''simple docstring'''
if (
not isinstance(_lowerCamelCase , (int, float))
or power_factor < -1
or power_... | 232 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Dict = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/r... | 232 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> int:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase):
__UpperCamelCase : Tuple = F'Input value of [number={number}] must be an integer'
... | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowercase : List[str] = 'src/transformers'
# Matches is_xxx_available()
lowercase : Union[str, Any] = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _impor... | 232 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowercase : List[Any] = logging.get_logger(... | 232 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 | 1 |
from __future__ import annotations
from collections import deque
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Optional[Any] , a :list[str] ) -> int:
__UpperCamelCase : list[dict] = []
self.adlist.append(
... | 232 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 | 1 |
import numpy as np
import datasets
lowercase : List[str] = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was... | 232 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowerCamelCase__ ( __lowercase):
... | 232 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 1 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : List[Any] = [
'word... | 232 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import Genera... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[Any] = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTrans... | 232 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 |
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_tensor
if is_fl... | 232 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-new... | 232 |
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
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowercase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCamelCase_... | 232 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
lowercase : ... | 232 | 1 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == ... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[int]) -> int:
'''simple docstring'''
if not numbers:
return 0
if not isinstance(_lowerCamelCase , (list, tuple)) or not all(
isinstance(_lowerCamelCase , _lowerCamel... | 232 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase : int = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokenizer'],
}
... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowercase : Lis... | 232 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 | 1 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
import argparse
import json
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( ) -> int:
'''simple docstring'''
__UpperCamelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_p... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multipl... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
fr... | 232 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCamelCase__ ( datasets.BeamBasedBuilder):
'''simple docstring'''
def... | 232 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestC... | 232 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokeniz... | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
def __init__( self :Any ... | 232 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : str = {
'bert... | 232 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowercase : Optional[Any] = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < versi... | 232 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Optional[int] = {
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
'https://huggingfa... | 232 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 | 1 |
from manim import *
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
def _lowerCamelCase ( self :List[str] ) -> str:
__UpperCamelCase : Dict = Rectangle(height=0.5 , width=0.5 )
__UpperCamelCase : Tuple ... | 232 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
_A = 'Speech2TextFeatureExtractor'
_A = 'Speech2TextTokenizer'
def __ini... | 232 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 | 1 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
... | 232 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 1 |
import torch
from torch import nn
class lowerCamelCase__ ( nn.Module):
'''simple docstring'''
def __init__( self :List[str] , a :Dict , a :List[Any] , a :Any , a :str , a :Union[str, Any]=1 , a :Tuple=False ) -> str:
... | 232 |
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_tensor
if is_fl... | 232 | 1 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from t... | 232 |
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
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
import operator as op
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict) -> Tuple:
'''simple docstring'''
__UpperCamelCase : Dict = []
__UpperCamelCase : List[str] = lambda _lowerCamelCase , _lowerCamelCase: int(x / y) ... | 232 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
lowercase : ... | 232 | 1 |
import argparse
import os
import re
lowercase : List[str] = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowercase : Dict = re.compile(R'[A-Z... | 232 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCas... | 232 | 1 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_s... | 232 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Tuple = logging.get_logger(__name__)
lowercase : str = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json'... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 232 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Pr... | 232 | 1 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : NDArray[floataa] , _lowerCamelCase : NDArray[floataa] , _lowerCamelCase : list[int] , _lowerCamelCase : int ... | 232 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 1 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int | float]]) -> int:
'''simple docstring'''
__UpperCamelCase : str = len(_lowerCamelCase)
__UpperCamelCase : str = len(matrix[0])
__UpperCamelCase : Union[str, Any] ... | 232 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalD... | 232 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] , _lowerCamelCase : List[str]=1_000) -> Dict:
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :int ) -> Dict:
__UpperCamelCase : Union[str, Any] = {}
def _lowerCamelCase ( self :str ) -> None:
print(self.vertex )
for i in self.vert... | 232 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : ... | 232 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowercase : Optional[int] = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation}... | 232 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 1 |
# Lint as: python3
import itertools
import os
import re
lowercase : str = re.compile(R'([A-Z]+)([A-Z][a-z])')
lowercase : Union[str, Any] = re.compile(R'([a-z\d])([A-Z])')
lowercase : str = re.compile(R'(?<!_)_(?!_)')
lowercase : L... | 232 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 1 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_... | 232 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Any = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# S... | 232 | 1 |
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
... | 232 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 232 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str , _lowerCamelCase : ... | 232 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageR... | 232 | 1 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxr... | 232 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
lowercase : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 232 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[int] , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : Optional[int]) -> int: # noqa: E741
'''simple docstring'''
... | 232 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Any) -> Dict:
'''simple docstring'''
__UpperCamelCase : Op... | 232 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowercase : str = logging.get_logger(__name__)
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
... | 232 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__U... | 232 | 1 |
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
pass
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
pass
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Any ... | 232 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 232 | 1 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
lowercase : Any = 6_3_7_8_1_3_7.0
lowercase : Tuple = 6_3_5_6_7_5_2.3_1_4_2_4_5
lowercase : str = 6378137
def _SCREAMING_SNAKE_CASE ... | 232 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCamelCase__ ( __lowercase):
... | 232 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase : int = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamplin... | 232 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int) -> float:
'''simple docstring'''
return base * power(_lowerCamelCase , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of e... | 232 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : List[str] = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetune... | 232 |
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_tensor
if is_fl... | 232 | 1 |
from torch import nn
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any]) -> int:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif a... | 232 |
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
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
class lowerCamelCase__ : # Public class to implement a graph
'''simple docstring'''
def __init__( self :Tuple , a :int , a :int , a :list[list[bool]] ) -> None:
__UpperCamelCase : int = row
__UpperCamelCase : List[str] = ... | 232 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
lowercase : ... | 232 | 1 |
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