code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int , lowerCamelCase_ :int , lowerCamelCase_ :list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square(lowerCamelCase_ :int , lowerCamelCase_ :int ) -> int:
... | 366 |
'''simple docstring'''
import re
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[Any] = 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.search(lowe... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Union[str, Any] = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CO... | 367 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCamelCase ( lowercase__ ... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_mode... | 368 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.ut... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __UpperCamelCase ( a__ ):
lowercase : List[str] ... | 369 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 0 |
'''simple docstring'''
from statistics import mean
import numpy as np
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :Tuple , lowerCamelCase_ :Optional[int] , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : Union[str, Any] ... | 370 |
'''simple docstring'''
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
i... | 8 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Any = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder... | 371 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : List[Any] = generate_pascal_triangle(lowerCamelCase_ )
for row_idx in range(lowerCamelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx ... | 8 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def UpperCAmelCase ( lowerCamelCase_ :Optional[Any] ):
'''simple docstring'''
snake_case_ : T... | 350 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__A : Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
... | 351 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list[list[int]] , lowerCamelCase_ :int , lowerCamelCase_ :int , lowerCamelCase_ :set ):
'''simple docstring'''
snake_case_ , snake_case_ : Tuple = len(UpperCAmelCase_ ), len(grid[0] ... | 352 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_model... | 353 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCamelCase ( lowercase__ ):
lowercase : Tuple = ['image_processor', 'tokenizer']
lowercase : Any ... | 354 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 0 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __UpperCamelCase ( _lowerCamelCase ):
lowercase... | 355 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@... | 8 | 0 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __UpperCamelCase :
def a__ ( self :Dict ,_UpperCamelCase :Optional[int] ):
raise NotImplement... | 356 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
... | 357 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cach... | 358 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 0 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A : Dict = logging.get_logger(__name__)
__A : Any = {
"post_extract_proj": "feature_projection.projectio... | 359 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : List[str] = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
t... | 360 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 0 |
'''simple docstring'''
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermar... | 361 |
'''simple docstring'''
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
__A : Tuple = logging.get_logge... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def UpperCAmelCase ( lowerCamelCase_ :list[int] , lowerCamelCase_ :list[int] , lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : Optional[Any] = [0] ... | 362 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return lst
snake_case_ : Union[str, Any] = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 8 | 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 : Optional[Any] = logging.get_logger(__name__)
def Upp... | 363 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 0 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( __lowerCAmelCase ):
lowercase : int = (UnCLIPScheduler,)
def a__ ( self :List[Any] ,**... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : List[Any] = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co... | 365 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : str = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if ... | 366 |
'''simple docstring'''
import re
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[Any] = 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.search(lowe... | 8 | 0 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from... | 367 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCamelCase ( lowercase__ ... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :Dict , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : int = (boundary[1] - boundary[0]) / steps
snake_case_ : Dict = boundary[0]
snake_case_ : Optio... | 368 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.ut... | 8 | 0 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__A : List[Any] = 'src/diffusers'
# Matches is_xxx_available()
__A : List[Any] = re... | 369 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 0 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def UpperCAmelCase ( lowerCamelCase_ :str = "laptop" ):
'''simple docstring'''
snake_case_ : str = F'''https://www.amazon.in/l... | 370 |
'''simple docstring'''
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
i... | 8 | 0 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__A : Dict = '\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n... | 371 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : List[Any] = generate_pascal_triangle(lowerCamelCase_ )
for row_idx in range(lowerCamelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx ... | 8 | 0 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_util... | 350 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 8 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__A : str = (720, 1_280) # Height, Width
__A : List[str] = (0.4, 0.6) # if height or width lower than this scale, drop it.
__A : Op... | 351 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 8 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __UpperCamelCase ( nn.Module ):
lowercase : int
lowercase : int... | 352 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():... | 353 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 | 0 |
'''simple docstring'''
__A : List[str] = [
(1_000, 'M'),
(900, 'CM'),
(500, 'D'),
(400, 'CD'),
(100, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def UpperCAmelCase ( lowerCamel... | 354 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 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,
... | 355 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@... | 8 | 0 |
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__A : str = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
lowerc... | 356 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 | 0 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : str ... | 357 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 0 |
'''simple docstring'''
import math
def UpperCAmelCase ( lowerCamelCase_ :Optional[int] ):
'''simple docstring'''
assert isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 ar... | 358 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling... | 359 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 0 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCAmelCase ( lowerCamelCase_ :dict ... | 360 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 0 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=13_37 , num_examples=42 ,... | 361 |
'''simple docstring'''
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
__A : Tuple = logging.get_logge... | 8 | 0 |
'''simple docstring'''
__A : Tuple = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownl... | 362 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return lst
snake_case_ : Union[str, Any] = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
__A : int = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0... | 363 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 0 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def UpperCAmelCase ( lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :Tuple=None , **lowerCamelCase_ :Union[str, Any] ):
'''simple docstring''... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
__A : List[str] = 10
def UpperCAmelCase_ ( lowerCamelCase_ :list[int] ):
'''simple docstring'''
snake_case_ : Union[str, Any] = 1
snake_case_ : Tuple = max(lowerCAm... | 365 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__A : Any = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__A : List[str] = [ord(letter)... | 366 |
'''simple docstring'''
import re
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[Any] = 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.search(lowe... | 8 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.k... | 367 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCamelCase ( lowercase__ ... | 8 | 0 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def UpperCAmelCase ( lowerCamelCase_ :Any , lowerCamelCase_ :int , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :Tuple ):
'''simple docstring'''
snake_case_ : Tup... | 368 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.ut... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def UpperCAmelCase ( lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :Any , lowerCamelCase_ :Any ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise... | 369 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 0 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__A : Optional[int] = {
# 1536-bit
5: {
... | 370 |
'''simple docstring'''
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
i... | 8 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester... | 371 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : List[Any] = generate_pascal_triangle(lowerCamelCase_ )
for row_idx in range(lowerCamelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx ... | 8 | 0 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgument... | 350 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
if num < 0:
return False
snake_case_ : int = num
snake_case_ : int = 0
while num > 0:
snake_case_ : List[Any] = rev_nu... | 351 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 8 | 0 |
'''simple docstring'''
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
__A : Tuple = logging.get_logge... | 352 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCamelCase ( lowercase__ , unittest.T... | 353 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : int = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all V... | 354 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 0 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class __UpperCamelCase :
@property
def a__ ... | 355 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@... | 8 | 0 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def UpperCAmelCase ( lowerCamelCase_ :np.ndarray ):
'''simple docstring'''
snake_case_ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_989 * r + 0.5_... | 356 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __UpperCamelCase ( lowercase__ ):
lowerca... | 357 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __UpperCamelCase :
def __init__( self :Any ,_UpperCamelCase :Any ):
snake_case_ : Any = data
snake_case_ :... | 358 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCond... | 359 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
lowercase : int
lowercase : int
class __UpperCamelCase :
def __... | 360 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 0 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class __UpperCamelCase :
def __init__( self :Any ,_UpperCamelCase :Optional[Any] ):
... | 361 |
'''simple docstring'''
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
__A : Tuple = logging.get_logge... | 8 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from ... | 362 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return lst
snake_case_ : Union[str, Any] = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 8 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count... | 363 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
__A : Any = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer... | 365 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 0 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if... | 366 |
'''simple docstring'''
import re
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[Any] = 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.search(lowe... | 8 | 0 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __U... | 367 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCamelCase ( lowercase__ ... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int , lowerCamelCase_ :int ):
'''simple docstring'''
while second != 0:
snake_case_ : List[str] = first & second
first ^= second
snake_case_ : List[str] = c << 1
r... | 368 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.ut... | 8 | 0 |
'''simple docstring'''
from math import factorial
def UpperCAmelCase ( lowerCamelCase_ :int = 1_00 ):
'''simple docstring'''
return sum(int(lowerCamelCase_ ) for x in str(factorial(lowerCamelCase_ ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Numb... | 369 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 0 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__A : List[str] = {
# 1536-bit
5: {
'pri... | 370 |
'''simple docstring'''
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
i... | 8 | 0 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def UpperCAmelCase ( lowerCamelCase_ :Dict , lowerCamelCase_ :float = 0.0 , lowerCamelCase_ :float = 1.0 ):
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if... | 371 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : List[Any] = generate_pascal_triangle(lowerCamelCase_ )
for row_idx in range(lowerCamelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx ... | 8 | 0 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 350 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 8 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optim... | 351 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 8 | 0 |
'''simple docstring'''
__A : List[str] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_... | 352 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 | 0 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tra... | 353 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Optional[Any] = {
'configuration_blenderbot_small': [... | 354 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : Optional[int] = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
... | 355 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@... | 8 | 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 ModelMixi... | 356 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__A : Any = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig... | 357 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : List[Any] = {
'junny... | 358 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 0 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : int = ... | 359 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int = 2_00 ):
'''simple docstring'''
snake_case_ : Dict = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
snake_case_ : Optional[Any] = [0] * (pence + 1)
snake_case_ : Optional[int] ... | 360 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE mode... | 8 | 0 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __UpperCamelCase ( lowercase__ ):
lower... | 361 |
'''simple docstring'''
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
__A : Tuple = logging.get_logge... | 8 | 0 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def UpperCAmelCase ( lowerCamelCase_ :Tuple ):
'''simple docstring'''
snake_case_ : List[str] = np.max(lowerCamelCase_ , axis=-1 , keepdims=lowerCamelCase_ )
snake_case_ :... | 362 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) <= 1:
return lst
snake_case_ : Union[str, Any] = 1
while i < len(lowerCamelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 8 | 0 |
'''simple docstring'''
__A : Tuple = 65_521
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : Optional[Any] = 1
snake_case_ : List[Any] = 0
for plain_chr in plain... | 363 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 8 | 0 |
'''simple docstring'''
import math
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[float], float] , lowerCamelCase_ :float , lowerCamelCase_ :float ):
'''simple docstring'''
snake_case_ : float = xa
snake_c... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 0 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( lowerCamelCase_ :int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of... | 365 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()... | 366 |
'''simple docstring'''
import re
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[Any] = 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.search(lowe... | 8 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A : str = l... | 367 |
'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCamelCase ( lowercase__ ... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__A : Union[str, Any] = TypeVar('T')
class __UpperCamelCase ( Generic[T] ):
lowercase : deque[T] # Cache store of keys
lowerca... | 368 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.ut... | 8 | 0 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def UpperCAmelCase ( lowerCamelCase_ :List[Any] , lowerCamelCase_ :int , lowerCamelCase_ :Any=None , **lowerCamelCase_ :List[Any] ):
'''simple docstring'''
snake_case_ : ... | 369 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 370 |
'''simple docstring'''
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
i... | 8 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 371 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : List[Any] = generate_pascal_triangle(lowerCamelCase_ )
for row_idx in range(lowerCamelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx ... | 8 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Dict ... | 350 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@requi... | 8 | 0 |
'''simple docstring'''
import random
class __UpperCamelCase :
@staticmethod
def a__ ( _UpperCamelCase :str ):
snake_case_ : Any = [ord(_UpperCamelCase ) for i in text]
snake_case_ : Dict = []
snak... | 351 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[int | float], int | float] , lowerCamelCase_ :int | float , lowerCamelCase_ :int | float , lowerCamelCase_ :int = 1_00 , ):
... | 8 | 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 PreTrainedTokenizer
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : List[Any]... | 352 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, to... | 8 | 0 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
return x + 2
class __UpperCamelCase ( unittest.Te... | 353 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Tuple = logging.get... | 8 | 0 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights... | 354 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__A : Dict = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-large_pyto... | 8 | 0 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 355 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@... | 8 | 0 |
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