code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
),
# See all Speech2Text models... | 650 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr':... | 650 | 1 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
fr... | 650 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wava... | 650 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__ve... | 650 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.j... | 650 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 650 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 650 | 1 |
from collections.abc import Callable
class UpperCamelCase__ :
def __init__( self : List[Any] , UpperCamelCase__ : Callable | None = None ):
'''simple docstring'''
lowercase_ = []
# Stores indexes of each ite... | 650 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 650 | 1 |
import sys
a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
... | 650 |
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()
except OptionalDependencyNotAvailable:
fr... | 650 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a ... | 650 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 650 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def UpperCAmelCase_ ( ):
raise RuntimeError("""CUDA out of memory.""" )
class UpperCamelCase__ ( nn.Modu... | 650 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
... | 650 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoT... | 700 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPM... | 650 | 0 |
import pytest
import datasets
# Import fixture modules as plugins
a = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
# Mark tests as "unit" by default if not marked as "integration" (or al... | 701 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_... | 650 | 0 |
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__ ( snake_case__ ):
__SCREAMING_SNAKE_CASE ... | 702 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError("""Input value must be an 'int' type""" )
lowercase_ = 0
while number:
position += 1
number >>= 1
return position
... | 650 | 0 |
import re
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if len(re.findall("""[ATCG]""" , UpperCAmelCase__ ) ) != len(UpperCAmelCase__ ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" , """TAGC""" ) )
if __name__ == ... | 703 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase__ ( __mag... | 650 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a = logging.get_logger(__name__)
a = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See all DPT models at https:/... | 704 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a = TypeVar('T')
class UpperCamelCase__ ( Generic[T] ):
__SCREAMING_SNAKE_CASE : deque[T] # Cache store of keys
__SCREAMING_SNAKE_CASE : set[T] # Ref... | 650 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if (
(cp >= 0X4e_00 and cp <= 0X9f_ff)
or (cp >= 0X34_00 and cp <= 0X4d_bf) #
or (cp >= 0X2_00_00 and cp <= 0... | 705 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 650 | 0 |
a = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'):
raise ImportWarning(
'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
)
i... | 706 |
def UpperCAmelCase_ ( UpperCAmelCase__=2_8_1_2_3 ):
lowercase_ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
lower... | 650 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
a = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
"albert-large-v1": "https://huggingface.co/albert-large-v1/r... | 707 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 650 | 0 |
import argparse
import os
import re
a = 'src/diffusers'
# Pattern that looks at the indentation in a line.
a = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
a = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and pu... | 708 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_uti... | 650 | 0 |
import collections
import os
import re
from pathlib import Path
a = 'src/transformers'
# Matches is_xxx_available()
a = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
a = re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a... | 709 |
import cva
import numpy as np
class UpperCamelCase__ :
def __init__( self : List[str] , UpperCamelCase__ : float , UpperCamelCase__ : int ):
'''simple docstring'''
if k in (0.04, 0.06):
lowercas... | 650 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EfficientFor... | 710 |
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'):
a = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
... | 650 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_lowerCAmelCase , _lowerCAmelCase ) ) )
def ... | 711 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( __magic_name__ ):
__SCREAMING_SNAKE_CASE : str = (UnCLIPScheduler,)
def UpperCAmelCase__ ( self : int , **UpperCamelC... | 650 | 0 |
class UpperCamelCase__ :
def __init__( self : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : int ):
'''simple docstring'''
lowercase_ = name
... | 712 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transf... | 650 | 0 |
from __future__ import annotations
from collections import Counter
from random import random
class UpperCamelCase__ :
def __init__( self : Optional[Any] ):
'''simple docstring'''
lowercase_ = {}
def UpperCAmelCase__ ( ... | 713 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 650 | 0 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ):
lowercase_ , lowercase_ = grid.shape
lowercas... | 714 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr':... | 650 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class UpperCamelCase__ ( ... | 715 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wava... | 650 | 0 |
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_configuration_common... | 716 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.j... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""Input value must be an 'int' type""" )
lowercase_ = 0
while number:
position += 1
number >>= 1
return position
i... | 717 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 650 | 0 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
a = namedtuple(
'_TestCommandArgs',
[
'dataset',
'name',
... | 718 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 650 | 0 |
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 .transformer_engine import c... | 719 |
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()
except OptionalDependencyNotAvailable:
fr... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
lowercase_ = sum(UpperCAmelCase__ ) / len(UpperCAmelCase__ ) # Calculate the average
return sum(abs(x - average ) for x in nums ... | 720 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 650 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffu... | 721 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ = 2_0_0_0_0_0_0 ):
lowercase_ = [0 for i in range(n + 1 )]
lowercase_ = 1
lowercase_ = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 700 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPM... | 650 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
a = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 701 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_... | 650 | 0 |
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 FlaxTimestepEmbedding, Fla... | 702 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError("""Input value must be an 'int' type""" )
lowercase_ = 0
while number:
position += 1
number >>= 1
return position
... | 650 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.te... | 703 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase__ ( __mag... | 650 | 0 |
import operator as op
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ = []
lowercase_ = lambda UpperCAmelCase__ , UpperCAmelCase__ : int(x / y ) # noqa: E731 integer division operation
lowercase_ = {
"^": op.pow,
"*": op.... | 704 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a = TypeVar('T')
class UpperCamelCase__ ( Generic[T] ):
__SCREAMING_SNAKE_CASE : deque[T] # Cache store of keys
__SCREAMING_SNAKE_CASE : set[T] # Ref... | 650 | 0 |
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.utils impo... | 705 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(snake_case__ , snake_case__ ) or not number >= 1... | 706 |
def UpperCAmelCase_ ( UpperCAmelCase__=2_8_1_2_3 ):
lowercase_ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
lower... | 650 | 0 |
a = '\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
a = [{'type': 'code', 'content': INSTALL_CONTENT}]
a = {
'{processor_class}': 'FakeProcess... | 707 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 650 | 0 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from... | 708 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_uti... | 650 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
a = '''1'''
a = '''0'''
a = '''1'''
a = ort.SessionOptions()
a = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
a = ['''TensorrtExecutionProvider''', '''CU... | 709 |
import cva
import numpy as np
class UpperCamelCase__ :
def __init__( self : List[str] , UpperCamelCase__ : float , UpperCamelCase__ : int ):
'''simple docstring'''
if k in (0.04, 0.06):
lowercas... | 650 | 0 |
import numpy as np
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
return np.where(vector > 0 , UpperCAmelCase__ , (alpha * (np.exp(UpperCAmelCase__ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 710 |
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'):
a = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
... | 650 | 0 |
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,
cached_file,
get_fil... | 711 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( __magic_name__ ):
__SCREAMING_SNAKE_CASE : str = (UnCLIPScheduler,)
def UpperCAmelCase__ ( self : int , **UpperCamelC... | 650 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 712 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transf... | 650 | 0 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 713 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 650 | 0 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager impo... | 714 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr':... | 650 | 0 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffuse... | 715 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wava... | 650 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybrid... | 716 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.j... | 650 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a = '''__DUMMY_TRANSFORMERS_USER__'''
a = '''Dummy User'''
a = '''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'''
a = '''htt... | 717 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows list cannot be empty""" )
lowercase_ = sum(
cas... | 718 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
lowercase_ =... | 719 |
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()
except OptionalDependencyNotAvailable:
fr... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ = False ):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_0_6_4_6_... | 720 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 650 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():... | 721 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
... | 650 | 0 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class UpperCamelCase__ :
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : List[str] = False
__SCREAMING_SNAKE_CASE ... | 700 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPM... | 650 | 0 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a ... | 701 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_... | 650 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 702 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError("""Input value must be an 'int' type""" )
lowercase_ = 0
while number:
position += 1
number >>= 1
return position
... | 650 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ ... | 703 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase__ ( __mag... | 650 | 0 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 704 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a = TypeVar('T')
class UpperCamelCase__ ( Generic[T] ):
__SCREAMING_SNAKE_CASE : deque[T] # Cache store of keys
__SCREAMING_SNAKE_CASE : set[T] # Ref... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ = len(a_ )
lowercase_ = len(a_ )
lowercase_ = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
lowercase_ = True
for i in range(a_ ):
... | 705 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 650 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( __magic_name__ ):
__SCREAMING_SNAKE_CASE : Dict = (EulerDiscreteScheduler,)
__SCREAMING_SNAKE_CASE ... | 706 |
def UpperCAmelCase_ ( UpperCAmelCase__=2_8_1_2_3 ):
lowercase_ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
lower... | 650 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a = logging.get_logger(__name__)
a = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-min... | 707 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 650 | 0 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 708 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_uti... | 650 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a = logging.get_logger(__name__)
class UpperCamelCase... | 709 |
import cva
import numpy as np
class UpperCamelCase__ :
def __init__( self : List[str] , UpperCamelCase__ : float , UpperCamelCase__ : int ):
'''simple docstring'''
if k in (0.04, 0.06):
lowercas... | 650 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ = ... | 710 |
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'):
a = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowercase_ = set()
return any(
node not in visited and depth_first_search(__UpperCamelCase , __UpperCam... | 711 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( __magic_name__ ):
__SCREAMING_SNAKE_CASE : str = (UnCLIPScheduler,)
def UpperCAmelCase__ ( self : int , **UpperCamelC... | 650 | 0 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ = True , UpperCAmelCase__ = math.inf , UpperCAmelCase__ = -math.inf , UpperCAmelCase__ = math.inf , ... | 712 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transf... | 650 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?filter=vit... | 713 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 650 | 0 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ = F'''{sampling_rate}'''
lowercase_ = """1"""
... | 714 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr':... | 650 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import loggin... | 715 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wava... | 650 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
a = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])
... | 716 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.j... | 650 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAN... | 717 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 650 | 0 |
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'):
a = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
... | 718 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
lowercase_ = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCAmelCase_ ( ):
print(su... | 719 |
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()
except OptionalDependencyNotAvailable:
fr... | 650 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable(... | 720 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 650 | 0 |
'''simple docstring'''
from functools import reduce
a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'6689664895... | 721 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
... | 650 | 0 |
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment #... | 700 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPM... | 650 | 0 |
import copy
import random
from transformers import CLIPTokenizer
class UpperCamelCase__ ( snake_case_ ):
def __init__( self : List[Any] , *UpperCamelCase__ : int , **UpperCamelCase__ : List[Any] ):
'''simple docstring'''
... | 701 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_... | 650 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ):
lowercase_ = coefficient_matri... | 702 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError("""Input value must be an 'int' type""" )
lowercase_ = 0
while number:
position += 1
number >>= 1
return position
... | 650 | 0 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a = logging.get_logger(__name__)
a = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_fil... | 703 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase__ ( __mag... | 650 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCamelCase__ :
def UpperCAmelCase__ ( self : Tuple , UpperCamelCase__ : List[Any] ):
'''sim... | 704 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a = TypeVar('T')
class UpperCamelCase__ ( Generic[T] ):
__SCREAMING_SNAKE_CASE : deque[T] # Cache store of keys
__SCREAMING_SNAKE_CASE : set[T] # Ref... | 650 | 0 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
re... | 705 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 650 | 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... | 706 |
def UpperCAmelCase_ ( UpperCAmelCase__=2_8_1_2_3 ):
lowercase_ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
lower... | 650 | 0 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 707 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 650 | 0 |
a = '\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'
a = [{'type': 'code', 'content': INSTA... | 708 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_uti... | 650 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
],
}
try:
if not is_torch... | 709 |
import cva
import numpy as np
class UpperCamelCase__ :
def __init__( self : List[str] , UpperCamelCase__ : float , UpperCamelCase__ : int ):
'''simple docstring'''
if k in (0.04, 0.06):
lowercas... | 650 | 0 |
from typing import List
import numpy as np
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ = {key: len(A_ ) for key, value in gen_kwargs.items() if isinstance(A_ , A_ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
... | 710 |
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'):
a = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
lowercase_ = []
lowercase_ = []
lowercase_ = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priority of each operator
lowercase_ = len(U... | 711 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( __magic_name__ ):
__SCREAMING_SNAKE_CASE : str = (UnCLIPScheduler,)
def UpperCAmelCase__ ( self : int , **UpperCamelC... | 650 | 0 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.... | 712 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transf... | 650 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/lu... | 713 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 650 | 0 |
'''simple docstring'''
import os
from collections.abc import Iterator
def UpperCAmelCase_ ( UpperCAmelCase__ = "." ):
for dir_path, dir_names, filenames in os.walk(A__ ):
lowercase_ = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for f... | 714 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr':... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
return str(lowerCamelCase__ ) == str(lowerCamelCase__ )[::-1]
def UpperCAmelCase_ ( UpperCAmelCase__ ):
return int(lowerCamelCase__ ) + int(str(lowerCamelCase__ )[::-1] )
def UpperCAmelCase_ ( UpperCAmelCase__ ... | 715 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wava... | 650 | 0 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeling_tf_auto impor... | 716 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.j... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ = 1_0_0_0_0_0_0 ):
lowercase_ = set(range(3 , snake_case__ , 2 ) )
primes.add(2 )
for p in range(3 , snake_case__ , 2 ):
if p not in primes:
continue
primes.d... | 717 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 650 | 0 |
from sklearn.metrics import fa_score
import datasets
a = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
a = '\nArgs:\n predictions (`list` of `int`): Predicted labels.\n re... | 718 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 650 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ = 1_0_0 ):
lowercase_ = set()
lowercase_ = 0
lowercase_ = n + 1 # maximum limit
for a in range(2 , UpperCAmelCase__ ):
for b in range(2 , UpperCAmelCase__ ):
lower... | 719 |
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()
except OptionalDependencyNotAvailable:
fr... | 650 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See all TrOCR models at http... | 720 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 650 | 0 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ... | 721 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
... | 650 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transform... | 700 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPM... | 650 | 0 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingface.co/facebook/mask2former-swin-small-coco-instanc... | 701 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_... | 650 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird im... | 702 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError("""Input value must be an 'int' type""" )
lowercase_ = 0
while number:
position += 1
number >>= 1
return position
... | 650 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_... | 703 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCamelCase__ ( __mag... | 650 | 0 |
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