code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
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
if is_torch_tpu_available... | 360 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def _a ( lowerCAmelCase )-> float:
return np.dot(lowerCAmelCase , lowerCAmelCase )
class lowercase_ :
def __init__( self : int , *,
... | 360 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils im... | 706 | from string import ascii_uppercase
__SCREAMING_SNAKE_CASE : Any = {char: i for i, char in enumerate(ascii_uppercase)}
__SCREAMING_SNAKE_CASE : str = dict(enumerate(ascii_uppercase))
def snake_case (__lowercase , __lowercase ) -> str:
'''simple docstring'''
... | 580 | 0 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsm... | 519 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {"""configurati... | 512 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers... | 137 |
"""simple docstring"""
import math
def lowerCamelCase__ ( _lowerCamelCase : int ) -> bool:
assert isinstance(_lowerCamelCase , _lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2... | 137 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : Tuple = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
... | 213 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1000 ) -> int:
_lowerCAmelCase : Optional[int] = 2**power
_lowerCAmelCase : str = str(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = list(_lowerCamelCase )
_lowerCAmelC... | 213 | 1 |
from math import isclose, sqrt
def __UpperCAmelCase ( __A , __A , __A ) -> tuple[float, float, float]:
'''simple docstring'''
UpperCAmelCase__ = point_y / 4 / point_x
UpperCAmelCase__ = 2 * normal_gradient / (1 + normal_grad... | 717 |
import csv
import tweepy
# Twitter API credentials
A = ""
A = ""
A = ""
A = ""
def __UpperCAmelCase ( __A ) -> None:
'''simple docstring'''
UpperCAmelCase__ = tweepy.OAuthHandler(__A , __A )
auth... | 277 | 0 |
__A : Tuple = {str(digit): digit**5 for digit in range(1_0)}
def __a ( A__ : int ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A__ ) )
def __a ( ):
return sum(
number
for number in range(1000 , 1000... | 16 |
from numpy import exp, pi, sqrt
def UpperCAmelCase ( a_ , a_ = 0.0 , a_ = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 55 | 0 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = len(lowerCamelCase )
for i in range(lowerCamelCase ):
for j in range(i + 1 , lowerCamelCase ):
if numbers[j] < numbers[i]:
__lowercase , __lowercase = numbers[... | 717 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ... | 53 | 0 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dat... | 71 | 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 ConfigTester
from ...test_modeling_... | 140 | 0 |
class __snake_case :
def __init__( self ) -> List[str]:
'''simple docstring'''
snake_case__ : str = 0
snake_case__ : int = 0
snake_case__ : int = {}
d... | 699 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Any = {'''configuration_xglm''': [''... | 699 | 1 |
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A ( UpperCAmelCase_ ):
'''si... | 15 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,... | 690 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __snake_case :
'''simple docstring'''
_snake_case = 42
_snake_case =... | 15 | import math
def A__ ( __A ):
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not numbe... | 15 | 1 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_... | 481 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : int = logging.get_logger(__name__)
lowerCamelCase__ : str = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main... | 698 | 0 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
"tokenization_m2m_100": ["M2M1... | 586 | 0 |
def A__ ( __A : int , __A : List[str] , __A : Tuple , __A : Dict , __A : int , __A : Any ) ->Optional[int]:
if index == r:
for j in range(__A ):
print(data[j] , end=''' ''' )
... | 184 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( __A : Any , __A : Dict , __A : Optional[... | 184 | 1 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def UpperCamelCase ( __lowerca... | 717 | from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class UpperCAmelCase ( __A ):
'''simple docstring'''
... | 70 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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_params import (
TEXT_GUIDED_IMAGE_INPA... | 81 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_uti... | 106 | 0 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProc... | 711 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> int:
return x if y == 0 else greatest_common_divisor(snake_case , x % y )
def SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> int:
return (x * y) // greatest_common_divisor(snake_cas... | 375 |
def SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> bool:
__lowercase = len(snake_case ) + 1
__lowercase = len(snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with... | 375 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 ,number_of_terms + 1 ) )
if __name__ == "__main__"... | 713 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase : ... | 656 | 0 |
'''simple docstring'''
def _lowercase ( __A ,__A ):
'''simple docstring'''
if not (isinstance(__A ,__A ) and isinstance(__A ,__A )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" )
__UpperCamelCase = len(__A ... | 601 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = ['''torch''', '''scipy''']
def __init__( self , *lowercase , **lowercase ) -> int:
requires_bac... | 601 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class UpperCamelCase__ ( __lowercase ):
_SCRE... | 717 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional impo... | 326 | 0 |
import copy
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
from ..auto import CONFIG_MAPPING
_lowercase: Union[str, Any] = logging.get_logger(__name... | 192 | import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common imp... | 192 | 1 |
'''simple docstring'''
def _snake_case ( A , A , A ) -> float:
return round(float(moles / volume ) * nfactor )
def _snake_case ( A , A , A ) -> float:
return round(float((moles * 0.0_821 * temperature) / (volume) ) )
... | 715 |
'''simple docstring'''
def _snake_case ( A , A ) -> bool:
lowerCAmelCase__ = len(A ) + 1
lowerCAmelCase__ = len(A ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with pr... | 98 | 0 |
'''simple docstring'''
import cmath
import math
def _A ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ):
'''simple docstring'''
A__ = math.radians(UpperCAmelCase )
A__ = math.radians(UpperCAmelCase )
... | 531 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A( UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
Upp... | 70 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class _UpperCamelCase ( nn.Module ):
UpperCAmelCase_ = 42
UpperCAmelCase_ = jnp.floataa
def UpperCAmelCase_ ( self :Optional[int] ) -> int:
UpperCAmelCase__ = nn.Conv(
... | 364 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class _UpperCamelCase ( lowerCAmelCase ):
# to overwrite at feature extractactor specific tests
... | 364 | 1 |
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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_... | 85 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstri... | 104 | 0 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowercase__ ( ):
with offline(OfflineSimulationMode.CONNECTION_T... | 713 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Tuple = logging.get_logger(__name__)
_lowercase : Optional[int] = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.... | 397 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCAmelCase)
class lowerCAmelCase ( __lowerCAmelCase):
# `task` is not a ClassVar since we ... | 24 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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
#
# Unle... | 683 | 0 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ... | 583 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise ... | 583 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTCo... | 60 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFl... | 568 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
... | 153 |
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,
DPMSo... | 153 | 1 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer ... | 152 | '''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
... | 152 | 1 |
from itertools import permutations
def lowerCamelCase__ (_UpperCAmelCase):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
SCREAMING_SNAKE_CASE = [7, 11, 13, 17]
for i, test in enumerate(SCR... | 703 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a_ : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a_ : list[int] = [ord(letter) for letter in string.ascii_lowe... | 444 | 0 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lower... | 613 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a : List[str] = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MA... | 613 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(__name__)
lowerCAmelCase : Union[str, Any] = {
"""xlm-r... | 146 |
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 ... | 146 | 1 |
'''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_bac... | 466 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE_ ):
snake_case__ = 42
snake_case__ = 42
def _UpperCAmelCase ( __A : str ):
... | 466 | 1 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python ... | 706 |
'''simple docstring'''
from __future__ import annotations
def _a ( lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None ):
"""simple docstring"""
if start is None:
_snake_case : Optional[Any] = 0
if end is None:
... | 47 | 0 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__lowerCamelCase = False
class UpperCAmelCa... | 467 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a__ ( UpperCamelCase_ : str, UpperCamelCase_ : str ):
UpperCAmelCase__ :Any = list(UpperCamelCase_ )
UpperCAmelCase__ :O... | 467 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__lowerCamelCase : List[Any] = "src/diffusers"
# Matches is_xxx_available()
__lowerCamelCase : List[Any] ... | 716 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __magic_name... | 457 | 0 |
'''simple docstring'''
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, r... | 378 |
'''simple docstring'''
import math
import sys
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
_UpperCAmelCase = ""
try:
with open(__lowercase , "rb" ) as binary_file:
_UpperCAmelCase = binary_file.read... | 236 | 0 |
from __future__ import annotations
def lowercase_ ( A__ ) -> str:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(UpperCAmelCase__ ) ):
matr... | 714 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoMod... | 294 | 0 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCamelCase__( UpperCamelCase__ : Any )->Union[str, Any]:
A__ = FileLock(str(tmpdir / '''foo.lock''' ) )
A__ = FileLock(str(tmpdir / '''foo.lock''' ... | 190 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 0 |
"""simple docstring"""
import math
def lowercase_ ( ):
"""simple docstring"""
A_ : Dict = input('''Enter message: ''' )
A_ : Optional[int] = int(input(f"""Enter key [2-{len(_UpperCAmelCase ) - 1}]: """ ) )
A_ : str = input('''Encryption/Decrypt... | 361 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : str = {
... | 361 | 1 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : Optional[Any] , a_ : str ):
""... | 69 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
de... | 647 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Any = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InformerConfig"""... | 151 |
def UpperCamelCase_ ( __a = 3 , __a = 7 , __a = 1_000_000 ) -> int:
a__ : List[Any] = 0
a__ : int = 1
for current_denominator in range(1 , limit + 1 ):
a__ : Optional[Any] = current_denominator * numerator /... | 151 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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_params import ... | 18 |
import numpy as np
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> np.array:
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
import math
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase = 0 , _UpperCAmelCase = 0 ) -> list:
lowerCamelCase =end or len(_UpperCAmelCase )
for i in range(_UpperCAmelCase , _UpperCAmelCase ):
lowerCamelCase =i
lowerCamelCase =a... | 269 |
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_INDEX_NAME, shard_checkpoint
UpperCAmelCase__ ... | 269 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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
#
#... | 152 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Optional[int] = 0
lowercase__ : int = len(UpperCAmelCase )
for i in range(n - 1 ):
for j in range(i + 1 , UpperCAmelCase ):
if arr[i] > arr[j]:
num_inversions += 1
ret... | 152 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType,... | 709 | 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,... | 699 | 0 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __lowercase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 377 |
'''simple docstring'''
import json
import sys
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int ) -> Tuple:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8" ) as f:
UpperCAmelCase_ : ... | 71 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = [0] * len(lowercase_ )
for i in range(1 , len(lowercase_ ) ):
# use last results for better performance - dynamic programming
__SCREA... | 718 |
"""simple docstring"""
from math import isqrt
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowercase_ ) + 1 ) )
def lowerCAmelCase_ ( lowercase_ : int = 10**6 ):
'''... | 401 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {"""vocab_file""": """spie... | 77 | """simple docstring"""
def lowerCAmelCase_ (_SCREAMING_SNAKE_CASE :str ) -> int:
assert column_title.isupper()
a_ : int = 0
a_ : Tuple = len(_SCREAMING_SNAKE_CASE ) - 1
a_ : Union[str, Any] = 0
while index >= 0:
... | 473 | 0 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowerCamelCase : Union[str, Any] = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf ori... | 168 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase : Tuple = {"""configuration_deit""": ["""DEIT_PRE... | 168 | 1 |
"""simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase_ = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'a... | 560 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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/l... | 560 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_a = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 29 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __A ... | 29 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __UpperCAmelCase ( lowerCamelCase_ : dict ) -> tu... | 105 |
from math import pow, sqrt
def _A( *UpperCamelCase__ : float ) -> bool:
'''simple docstring'''
__lowercase = len(UpperCamelCase__ ) > 0 and all(value > 0.0 for value in values )
return result
def _A( UpperCamelCase__ : f... | 332 | 0 |
'''simple docstring'''
def A__ ( A_ ) -> Dict:
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
... | 706 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class Upp... | 602 | 0 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowerCAmelCase : Optional[int] = [
# tf -> hf
("""/""", """."""),
("""... | 46 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
from timeit import timeit
def UpperCamelCase ( snake_case__ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""the value of input must not be negative""" )
__snake_case :List[str] = 0
while... | 291 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 291 | 1 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__lowercase : Union[str, Any] = {'''UserAgent''': UserAgent().random}
def lowercase ( __A : Optional[Any] ) -> dict:
'''simple docstri... | 36 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/reso... | 420 | 0 |
def _lowerCAmelCase ( UpperCamelCase__: List[str] , UpperCamelCase__: Any ) -> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multipli... | 713 |
from sklearn.metrics import recall_score
import datasets
_lowercase : Any = "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negativ... | 546 | 0 |
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase = {
# 1536-bit
5: {
'''prime''': ... | 677 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_lowerCAmelCase = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
... | 161 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random... | 477 | """simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowercase__( ):
lowercase_ : List[Any] = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
lowercase_ : int = parser.add_su... | 477 | 1 |
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : int ):
UpperCamelCase_ : Optional[int] = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
UpperCamelCase_ : List[Any] = key - 1
if key <= 0:
raise ValueError("""Height ... | 635 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelera... | 273 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _a ( metaclass=lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *UpperCAmelCase_ , **Up... | 708 |
"""simple docstring"""
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 diffuse... | 120 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__snake_case :Tuple =HfArgumentParser(InitializationArguments)
__snake_case :Optional[int] =parser.parse_args()
# Load codeparrot tok... | 106 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
f... | 369 | 0 |
from math import sqrt
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
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 are not primes
return False
# All primes number are in forma... | 707 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logg... | 655 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int , A__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase_ ( ):
'''simple docstring'''
assert or_g... | 275 | '''simple docstring'''
def UpperCamelCase__ ( _lowercase : List[Any] ) -> Dict:
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
... | 523 | 0 |
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase = 0 ):
A : List[str] = length or len(_lowerCamelCase )
A : str = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
A , A ... | 17 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import Gra... | 17 | 1 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as or... | 198 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class... | 301 | 0 |
UpperCAmelCase__ = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingfa... | 718 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiec... | 362 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase_ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __A :
'''simple docstring'''
... | 11 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutp... | 6 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...te... | 713 |
"""simple docstring"""
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... | 628 | 0 |
import fire
from utils import calculate_rouge, save_json
def SCREAMING_SNAKE_CASE_ ( _snake_case :Dict , _snake_case :Tuple , _snake_case :List[str]=None , **_snake_case :Tuple ) -> List[str]:
_A = [x.strip() for x in open(_snake_case ).readlines()]
_A = ... | 2 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def _A ( SCREAMING_SNAKE_CASE ):
return input_array.reshape((input_array.size, 1) )
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SC... | 113 | 0 |
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
a_ : Optional[Any] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( snake_case_ : List[Any] ):
__magic_name__ ... | 709 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ : Union[str, Any] = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_to... | 678 | 0 |
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_image, load_numpy, slow, torch_... | 321 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/conf... | 321 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
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_copies # noqa: E402
# ... | 708 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
UpperCamelCase_ = [
'kernels/rwkv/wkv_cuda.cu',
'kernels/rwkv/wkv_op.cpp',
'kernels/deformable_detr/ms_deform_attn.h',
'kernels/deformable_detr/cuda/ms_deform_im2col_... | 510 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCamelCase__ ( UpperCAmelCase_ ) -> List[Any]:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise TypeError('''Undefined for non-integers''' ... | 322 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.get... | 54 | 0 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : List[Any] = logging.get_logger(__name__)
def lowerCamelCase__ ( _lowerCamelCase , _lowe... | 592 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAK... | 592 | 1 |
"""simple docstring"""
def lowercase_ ( _snake_case ):
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]
SC... | 223 |
"""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
UpperCAmelCase__ : Any = logging.get_logger(__n... | 223 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Union[str, Any] = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderD... | 719 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from .... | 263 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class _a ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : s... | 520 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase = {
'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
'tokenization_ctrl': ['CTRLTokenizer'],
}
try:
i... | 520 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase_ : Dict = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""tokeniz... | 709 | '''simple docstring'''
def __A ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase=False ) -> Optional[int]:
'''simple docstring'''
if isinstance(UpperCAmelCase ,UpperCAmelCase ) and isinstance(UpperCAmelCase ,UpperCAmelCase ):
... | 204 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowerCAmelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias")... | 10 | from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( __snake_case , __snake_case ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(__snake_case , __snake_case ) ) )
def _snake_case ( __snake_cas... | 10 | 1 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCame... | 371 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_=() , UpperCamelCase_=None , UpperCamelCase_="no" , ... | 371 | 1 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE( A ):
... | 498 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from .... | 498 | 1 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity... | 218 |
from __future__ import annotations
def snake_case_ (__A : list[int] , __A : int ) -> list[int]:
__lowerCAmelCase : List[Any] = 0
__lowerCAmelCase : Optional[Any] = len(__A ) - 1
while i < j:
if nums[i] + nums[j] == target... | 218 | 1 |
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 (
IM... | 15 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
A : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedin... | 15 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, lo... | 714 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distributed... | 232 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : Any, UpperCamelCase__ : List[str] ):
'''simple docstring'''
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
r... | 296 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for T... | 621 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStr... | 195 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_... | 195 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ) -> str:
assert x is not None
assert y is not None
__lowerCamelCase : List[str] = len(SCREAMING_SNAKE_CASE_ )
__lowerCamelC... | 13 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all PEGASUS models at https://h... | 514 | 0 |
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 ConfigTeste... | 25 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : Tuple = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAIN... | 25 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def UpperCAmelCase ( UpperCAmelCase__ : str):
lowerCamelCase , lowerCamelCase : Tuple = analyze_text(UpperCA... | 320 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diff... | 320 | 1 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_toke... | 703 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPM... | 663 | 0 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning th... | 486 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-large': 'https://huggingfa... | 486 | 1 |
from ...configuration_utils import PretrainedConfig
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
lowerCAmelCase_ : Dict = 'bert-generation'
def __init__( self , lowerCAmelCase=5_0358 , lowerCAmelCase=1024 , lowerCAmelCase=24 ... | 23 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ... | 23 | 1 |
"""simple docstring"""
from math import sqrt
def lowercase ( lowerCAmelCase__ : int ) -> Any:
assert isinstance(snake_case__ , snake_case__ ) and (
number >= 0
), "'number' must been an int and positive"
__a = True
# 0 and 1 are none prime... | 695 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLM... | 91 | 0 |
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 __lowerCAmelCase ( A ... | 639 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase = 0
_UpperCAmelCase = [0]
_UpperCAme... | 639 | 1 |
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