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"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_av... | 16 |
"""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
lowerCAmelCase_ = logging.get_logger(__name__)
... | 16 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] ... | 340 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax imp... | 340 | 1 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTest... | 12 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUM... | 12 | 1 |
'''simple docstring'''
from __future__ import annotations
class lowerCamelCase :
def __init__( self, lowercase_ = 0 ) -> Optional[int]:
snake_case = key
def _lowerCamelCase ( self, lowercase_, lowercase_ ) -> list[str]:
assert isinstance(lowercase_, lower... | 364 |
'''simple docstring'''
from pathlib import Path
import fire
def __magic_name__ ( A , A , A ) -> Union[str, Any]:
snake_case = Path(A )
snake_case = Path(A )
dest_dir.mkdir(exist_ok=A )
for path in src_dir.iterdir():
snake_case = [... | 332 | 0 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __lowerCAmelCase (lowercase_ , lowercase_ ):
... | 2 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
lowercase :List[str] = ""
for word_or_phrase in separated:
if not isinstance(lowerCamelCase, lowerCamelCase ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separa... | 236 | 0 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def... | 211 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCamelCase_ ( _a , _a=1_000 ):
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowerCAmelCase__ : int =... | 211 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : str = 'Speech2TextFeatureExtractor'
__UpperCAmelCase : int = 'Speech2TextTokenizer'
def __init__(self : ... | 65 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from .... | 207 | 0 |
"""simple docstring"""
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 impo... | 357 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common i... | 100 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/ma... | 340 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
a_ = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
'''scale_grad_by_... | 340 | 1 |
"""simple docstring"""
# Copyright 2023 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
#
# U... | 73 |
"""simple docstring"""
import qiskit
def lowerCAmelCase_( lowercase_ : int , lowercase_ : int ) -> qiskit.result.counts.Counts:
_lowerCamelCase = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
... | 73 | 1 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCamelCase__ = Lock()
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ,... | 86 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[str] = logging.get_logger(__name__)
def lowercase__ ( snake_case_ :Union[tf.Tensor, np.ndarray] ):
if isins... | 332 | 0 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase: List[Any] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def __SCREAMING_SNAKE_CASE ( ):
_lowercase : str = os.path.dirname(os.path.realpath(__UpperCA... | 336 |
"""simple docstring"""
import pprint
import requests
UpperCAmelCase: Tuple = """https://zenquotes.io/api"""
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __SCREAMING_SNAKE_CASE ( ):
return requests.ge... | 336 | 1 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def lowerCAmelCase (__A = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''').lower()).values()) < 2
def lowerCAmelCase (__A = ""):
"""simple doc... | 211 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependency... | 211 | 1 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> None:
'''simple docstring'''
if (direction ... | 358 |
def _a ( SCREAMING_SNAKE_CASE__ : str ) -> str:
'''simple docstring'''
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise V... | 191 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqla... | 49 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 100 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common im... | 249 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common impor... | 249 | 1 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a =logging.get_logger(__name__)
a ={
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,112 total “codepoints”
a =1114112
# Below: Constants defini... | 73 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueError('Cash flows list cannot be empty' )
__lowerCamelCase : int = sum(
cash_... | 73 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ = 4 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = abs(lowerCAmelCase_ ) or 4
return [[1 + x + y * row_size for x in range(lowerCAmelCase_ )] for... | 195 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
... | 195 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
_lowerCamelCase : Any = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a__ ( ) -> Any:
UpperCAmelCase : List[Any] = os.path.dirname(os.path.realpath(UpperCAmelCase ) )
UpperCAmelCase ... | 336 |
# 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 applica... | 336 | 1 |
from math import pi, sqrt, tan
def _snake_case( SCREAMING_SNAKE_CASE__ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
de... | 355 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_... | 282 | 0 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ..... | 241 |
"""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
lowerCamelCase_ = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", ... | 191 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats... | 208 |
def _SCREAMING_SNAKE_CASE ( lowercase : Tuple , lowercase : Dict , lowercase : List[str] , lowercase : Dict , lowercase : Dict , lowercase : List[str] ):
'''simple docstring'''
if index == r:... | 208 | 1 |
"""simple docstring"""
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'... | 249 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch... | 249 | 1 |
lowerCAmelCase__ : List[Any] ={'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
lowerCAmelCase__ : str =['a', 'b', 'c', 'd', 'e']
def a__ ( A__, A__, A__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = start
# add current... | 162 |
lowerCAmelCase__ : Dict ='ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def a__ ( ):
SCREAMING_SNAKE_CASE_ : Tuple = input('Enter message: ' )
SCREAMING_SNAKE_CASE_ : Any = input('Enter key [alphanumeric]: ' )
SCREAMING_SNAKE_CASE_ : ... | 162 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isol... | 195 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 195 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingface/i... | 352 | """simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config... | 149 | 0 |
"""simple docstring"""
import argparse
import struct
import unittest
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase ):
_lowerCamelCase : List[Any] = data
# Initialize hash values
... | 96 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_token... | 282 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
if num < 0:
return False
__magic_name__ : int = num
__magic_name__ : int = 0
while num > 0:
__magic_name__ : List[Any] = rev_num * 10 + (num... | 370 |
'''simple docstring'''
import socket
def lowerCamelCase ( ):
"""simple docstring"""
__magic_name__ : List[str] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__magic_name__ : Union[str, Any] = socket.gethostname()
__magic_name__ : int ... | 275 | 0 |
'''simple docstring'''
class lowerCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] , _a : Dict ) -> Optional[Any]:
# we need a list not a string, so do something to change the type
__lowerCamelCase : List[Any] ... | 208 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
E... | 208 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 359 |
from math import ceil, sqrt
def __lowerCamelCase ( lowerCamelCase__ = 1_000_000 ):
"""simple docstring"""
lowercase__ : int = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowercase__ : List[str]... | 121 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase = {
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PR... | 162 |
'''simple docstring'''
import math
import unittest
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
assert isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 ar... | 162 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase :Optional[int] = logging.get_logger(__name__)
lowerCA... | 367 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
__magic_name__ : int = f'Input value of [number={number}] must be an integer'
raise TypeError(lowerCAmelCase )
if nu... | 275 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : str = len(A_ ) + 1
__snake_case : Dict = len(A_ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix s... | 123 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCAmelCase_ ( A_ ,A_ ,A_):
UpperCamelCase__: Dict = ("dense.weight", "attention.self.query", "attention.self.key", "attention.se... | 149 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, t... | 371 |
from math import factorial
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : str ) -> Union[str, Any]:
__lowerCAmelCase = real
if isinstance(lowerCAmelCase_ , lowerCA... | 207 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __lowerCAmelCase ( _UpperCAmelCase ):
... | 324 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import ena... | 275 | 0 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : List[str] ):
"""simple docstring"""
_a , _a = [], []
while len(_lowerCAmelCase ) > 1:
_a , _a = min(_lowerCAmelCase ), max(_lowerCAmelCase )
start.append(_lowerCAmelCase ... | 369 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _lowerCAmelCase : Dict, _lowerCAmelCase : List[str], _lowerCAm... | 153 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Union[str, Any] = {
"configuration_deberta": ["DEB... | 28 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCamelCase__ ( a , a=10_00 ) -> Optional[int]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
_A: List[Any] = n - 1
_A: Dict = 0
while d % 2 ==... | 121 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case__ ( _a ):
_snak... | 370 | """simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:Dict = logg... | 268 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_... | 46 |
def _lowercase ( lowercase__ , lowercase__ ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":... | 275 | 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 : List[str] = {
'''configuration_electra''': ['''ELE... | 227 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaa... | 227 | 1 |
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_... | 230 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_commo... | 207 | 0 |
import argparse
import datetime
def __magic_name__ ( __a : str ):
'''simple docstring'''
UpperCamelCase__ = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursda... | 178 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils impo... | 178 | 1 |
"""simple docstring"""
import functools
def _snake_case ( lowercase__ : int , lowercase__ : Any ) -> List[Any]:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not all(isinstance(_SCREAM... | 84 |
"""simple docstring"""
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = set(_SCREAMING_SNAKE_CASE ), [start]
while stack:
UpperCamelCase = stack.pop()
... | 153 | 0 |
import numpy
# List of input, output pairs
UpperCamelCase__ : int = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCamelCase__ : Optional[int] = (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCamelCas... | 330 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. A... | 330 | 1 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-month... | 35 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def snake_case ( A__ ):
UpperCAmelCase_ : Dict = SwinConfig(image_size=1_92 )
if "base" in mod... | 268 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, Ten... | 157 |
"""simple docstring"""
import argparse
import os
import re
_SCREAMING_SNAKE_CASE : List[str] = """src/diffusers"""
# Pattern that looks at the indentation in a line.
_SCREAMING_SNAKE_CASE : Optional[int] = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in grou... | 157 | 1 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __init__(self , lowerCamelCase_ , lowerCamelCase_ ):
""... | 227 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 227 | 1 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizer... | 358 |
"""simple docstring"""
from string import ascii_uppercase
__magic_name__ = {str(ord(c) - 55): c for c in ascii_uppercase}
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise TypeError("""int() can't convert no... | 255 | 0 |
from __future__ import annotations
lowercase = 1.6_021e-19 # units = C
def __UpperCAmelCase ( a_ , a_ , a_ , ):
if (conductivity, electron_conc, mobility).count(0) != 1:
raise ValueError('You cannot supply more or less than 2 value... | 178 |
from __future__ import annotations
def __UpperCAmelCase ( a_ , a_ , a_ , a_): # noqa: E741
while r - l > 1:
snake_case_ = (l + r) // 2
if v[m] >= key:
snake_case_ = m
else:
... | 178 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
__lowerCAmelCase = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and... | 359 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if not (isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )):
raise ValueError("""longest_comm... | 270 | 0 |
import numpy
# List of input, output pairs
a_ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
a_ = (((515, 22, 13), 555), ((61, 35, 49), 150))
a_ = [2, 4, 1, 5]
a_ = len(train_data)
a_ = 0.0_09
... | 330 |
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 330 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_im... | 371 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__UpperCamelCase : Dict = logging.... | 309 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__ ) -> Tuple:
__UpperCAmelCase : Tuple = ("dense.weight", "attention.self.query... | 157 | import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .be... | 157 | 1 |
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = len(UpperCAmelCase_ )
while cur > 1:
# Find the maximum number in arr
lowercase__ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
lowercase__ = ar... | 353 |
def _a ( SCREAMING_SNAKE_CASE = 10_00 ):
"""simple docstring"""
return sum(e for e in range(3 , SCREAMING_SNAKE_CASE ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 93 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowerCAmelCase__ = """__DUMMY_TRANSFORMERS_USER__"""
lowerCAmelCase__ = """Dummy User"""
lowerCAmelCase__ = """hf_hZEmno... | 68 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTra... | 255 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQ... | 339 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2... | 90 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : List[Any] = TypeVar("T")
def __magic_name__ ( __lowerCAmelCase : int ) -> int:
return (position - 1) // 2
def __magic_name__ ( __lowerCAmelCase : ... | 270 | 0 |
import argparse
import struct
import unittest
class SCREAMING_SNAKE_CASE :
def __init__( self : str , lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = ... | 357 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=a_ )
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowerCamelCase : s... | 139 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_p... | 35 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : int = 1_00 ) -> int:
_lowerCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
_lowerCAmelCase : str = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
... | 309 | 0 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase : List[str]=None , UpperCamelCase : int=None ):
'''simple docstring... | 320 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 320 | 1 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class a__ :
def __init__( self : str , a : Dict ):
"""simple docstring"""
__lowerCamelCase = data
__lowerCamelCase = [0x67452301... | 67 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Union[str, Any] = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE... | 93 | 0 |
'''simple docstring'''
def __a ( _UpperCamelCase: int , _UpperCamelCase: int ) -> str:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_UpperCamelCase , int(b / 2 ) ) * actual_power(_UpperCamelCas... | 142 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
fro... | 142 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, D... | 339 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, re... | 339 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: float, a_: float ):
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__m... | 17 | '''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
... | 17 | 1 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 70 |
'''simple docstring'''
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... | 139 | 0 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_deter... | 38 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCamelCase = models.Se... | 38 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def A_ ( _lowerCAmelCase : float, _lowerCAmelCase : float ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
... | 320 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell p... | 320 | 1 |
class _UpperCAmelCase :
def __init__( self : Optional[Any] ):
snake_case_ : int = """"""
snake_case_ : str = """"""
snake_case_ : Dict = []
def _snake_case ( self : Tuple , lowercase_ : int , lowercase_ : int ):
if m =... | 371 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_CO... | 155 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
"""simple do... | 142 |
from typing import Any
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , A : Any ) ->Optional[int]:
lowerCamelCase__ : Optional[int] = data
lowerCamelCase__ : Any = None
class __SCREAMING_SNAK... | 142 | 1 |
'''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
__lowercase : Any = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
... | 358 |
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 294 | 0 |
"""simple docstring"""
def _A ( UpperCamelCase_ : float, UpperCamelCase_ : float) -> float:
'''simple docstring'''
if density <= 0:
raise ValueError("Impossible fluid density")
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus")
return (bulk... | 17 |
"""simple docstring"""
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 = 'hf_hZEmnoOEYISjraJtbySaKCNnSuYAvukaT... | 17 | 1 |
"""simple docstring"""
class snake_case :
def __init__( self : List[str] , a__ : int , a__ : Any=None , a__ : Dict=None ) -> str:
'''simple docstring'''
_A = data
_A = previous
... | 163 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from... | 163 | 1 |
# using dfs for finding eulerian path traversal
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str , __magic_name__ : Union[str, Any] , __magic_name__ : Any , __magic_name__ : int=None ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase :Any ... | 38 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase_ : Dict = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
UpperCAmelCase_ : Any = '''
Args:
... | 38 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class ... | 364 |
"""simple docstring"""
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def a__ ( snake_case__ ) -> List[str]:
lowerCamelCase = [
"""decoder.version""",
"""decoder.output_projecti... | 168 | 0 |
# Copyright 2021 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 applicabl... | 18 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def lowercase () -> Dict:
'''simple docstring'''
lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=s... | 155 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(a ) , """Tatoeba directo... | 206 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
''... | 206 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , ... | 136 |
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_I... | 294 | 0 |
'''simple docstring'''
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
... | 371 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common i... | 55 | 0 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLIC... | 163 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( UpperCamelCase__ ):
return [ord(UpperCamelCase__ ) - 9_6 for elem in plain]
def _UpperCamelCase ( UpperCamelCase__ ):
return "".join(chr(elem + 9_6 ) for... | 163 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
Aut... | 364 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
"""kakaobrain/align-base""": """http... | 35 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowercase_ ( _lowerCamelCase : Union[dict, list, tuple, torch.Tensor]):
... | 87 |
'''simple docstring'''
import numpy as np
class a :
def __init__( self ) -> List[str]:
_a = (0, 0)
_a = None
_a = 0
_a = 0
_a = 0
def __eq__( self , __magic_name__... | 168 | 0 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( _lowerCamelCase: Optional[int] , _lowerCamelCase: List[Any] , _lowerCamelCase: O... | 64 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https:/... | 64 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase :Union[str, Any] = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 206 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowerCamelCase :str = TypeVar('''T''')
class _lowerCAmelCase (... | 206 | 1 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : Any =logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] ... | 362 |
'''simple docstring'''
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 (
... | 123 | 0 |
def UpperCamelCase (lowercase_: list ) -> Tuple:
if len(UpperCAmelCase_ ) < 2:
return collection
def circle_sort_util(lowercase_: list , lowercase_: int , lowercase_: int ) -> bool:
A__ : Dict = False
if low == high:
return swapped
A__ ... | 192 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils impo... | 55 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
snake_case_ = [8, 5, 9, 7]
snake_case_ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
snake_case_ = [
[3, 2, 1, 4],
[0... | 369 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
snake_case_ = logging.get_logger(__name__)
def _lowerCAmelCase ( lowercase_ ):
Uppe... | 181 | 0 |
"""simple docstring"""
import math
def lowercase ( _snake_case : int ) ->bool:
"""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 3 ar... | 102 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __snake_case( _lowerCAmelCase ) -> Any:
for i in range(0 , _lowerCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(""" """ , end="""""" )
... | 35 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE : Dict = logging.g... | 252 |
from __future__ import annotations
from random import random
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCamelCase_ = None ):
lowercase_ :Tuple = value
lowercase_ :Tuple = r... | 252 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
fr... | 64 |
"""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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :list , snake_case_ :int , snake_case_ :int , snake_case_ :int ):
__UpperCAmelCase = []
__UpperCAmelCase , __UpperCAmelCase = input_li... | 353 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowercase : int = logging.get_logger(__name__)
_lowercase : Tuple ... | 86 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
_UpperCamelCase = False
_UpperCamelCase = True
_UpperCamelCase = False
if __name__ == "__main_... | 254 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase_ ( __lowerCamelCase ... | 123 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENT... | 182 |
"""simple docstring"""
from __future__ import annotations
def __lowerCAmelCase (_UpperCamelCase ):
__lowerCAmelCase : List[str] = str(_UpperCamelCase )
return len(_UpperCamelCase ) == 9 and set(_UpperCamelCase ) == set('123456789' )
def __lowerCAmelCase ():
for base_n... | 182 | 1 |
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE :List[str] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv prepri... | 15 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --u... | 181 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case__ : Union[str, Any] = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokeniz... | 356 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 250 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIO... | 252 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowerCamelCase ( lowerCamelCase__ : List[str] , lowerCamelCase__ : Optional[Any... | 252 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = [
'encoder.version',
... | 355 |
from __future__ import annotations
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = []
create_all_state(1 , UpperCamelCase__ , UpperCamelCase__ , [] , ... | 200 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
... | 95 |
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Conf... | 86 | 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 ... | 198 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def UpperCamelCase ( _A : Tuple )-> Dict:
... | 198 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
... | 182 | import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
... | 182 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConf... | 30 | """simple docstring"""
# Copyright 2021 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
#
# Unl... | 30 | 1 |
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