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 |
|---|---|---|---|---|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIP... | 62 |
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowerCamelCase__ ( ):
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ... | 62 | 1 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common im... | 707 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',... | 56 | 0 |
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 D... | 367 |
'''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
#
# Un... | 365 | 0 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session')
def __UpperCAmelCase ... | 708 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipel... | 6 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_process... | 333 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_co... | 333 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : str = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCT... | 91 |
"""simple docstring"""
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCamelCase__ ( unittest.TestCase ):
def __a ( self : Dict ):
A = [
'safety_checker/pytorch_model.bin',
'... | 91 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
... | 633 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def _SCREAMING_SNAKE_CASE ( _lowercase : list[int] , _lowercase : list[int] , _lowercase : int ) ->list[int]:
'''simple docstring... | 633 | 1 |
from collections.abc import Iterable
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowerCamelCase = None ):
snake_case__ = value
snake_case__ = None # Added in order to delete a node easier
snake_case__ = None
... | 530 |
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase , __lowerCAmelCase ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(100, 0.2_5) = }''')
print(F'''{price_plus_tax(1_2_5.5_0, 0.0_5) = }''')
| 530 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
'facebook/convnextv2-tiny-1k-224': 'https://huggingface... | 383 |
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 _a ( __lowercase ) -> List[A... | 383 | 1 |
"""simple docstring"""
import numpy
# List of input, output pairs
A_ : Tuple = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
A_ : List[Any] = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
A_ : int = ... | 696 |
"""simple docstring"""
import importlib
import inspect
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_config_docstrings.py
A_ : Dict = "src/transformers"
# This is to make sure the transformers ... | 696 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.... | 165 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowercase ( unittest.TestCase ):
"""simple docstring"... | 165 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( __snake_case : Any, __snake_case : str, __snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError("""days_between_payments mus... | 707 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decode... | 687 | 0 |
__SCREAMING_SNAKE_CASE = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__SCREAMING_SNAKE_CASE = frozenset(... | 220 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Tuple ,lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : Optional[Any]=1024 ... | 220 | 1 |
from __future__ import annotations
from typing import Any
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
if not postfix_notation:
return 0
snake_case__ : List[str] = {"""+""", """-""", """*""", """/"""}
snake_case__ : list[Any]... | 127 |
from __future__ import annotations
from typing import Any
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
if not postfix_notation:
return 0
snake_case__ : List[str] = {"""+""", """-""", """*""", """/"""}
snake_case__ : list[Any]... | 127 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int ) -> Tuple:
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 92 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a__ = """"""
a__ = """"""
a__ = """"""
a__ = 1 # (0 is vertical, 1 is horizontal)
def _UpperCAmelCase ( ):
snake_case__ , snake_case__ = get_dataset(a , a )
print("""P... | 654 | 0 |
'''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_retribert import RetriBertTokenizer
UpperCamelCase__ : ... | 385 |
'''simple docstring'''
import numpy as np
UpperCamelCase__ : Optional[int] = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class _lowerCAmelCase :
... | 385 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class A__ ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def _UpperCamelCase( self : List[str] ... | 37 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# 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
SCREA... | 582 | 0 |
# 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
#
# Unless required by app... | 714 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 55 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ = logging.get_logger(__name__)
A_ = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class l... | 270 |
'''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 ac... | 270 | 1 |
'''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
#
... | 691 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
f... | 691 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowerCamelCase_ ( lowerCAmelCase__ : List[str] ) -> Tuple:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 106 |
__snake_case :Any =256
# Modulus to hash a string
__snake_case :Optional[int] =1000003
def lowerCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
A = len(lowerCAmelCase__ )
... | 106 | 1 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
UpperCAmelCase : Optional[int] = 'us-east-1' # defaults region
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
__a = 42
__a = """arn:aws:iam::558105141721:rol... | 299 |
"""simple docstring"""
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase__ ( A ):
"""simple docstring"""
def __init__( self : Any , UpperCam... | 299 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
__snake_case :int ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
__snake_case ... | 106 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIG... | 412 | 0 |
def snake_case_ ( ) -> int:
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(snake_case , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
pri... | 718 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__lowerCAmelCase = logging.get_logger(__name__)
class __a ( __UpperCamelCase ):
__lowercase : Union[str, Any] ... | 335 | 0 |
'''simple docstring'''
from math import pi
def UpperCamelCase__ ( __magic_name__ : int , __magic_name__ : int ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 38 |
'''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... | 38 | 1 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
... | 641 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def snake_case_ ( ):
UpperCAmelCase_ : str = HfArgumentParser(__lowercase )
UpperCAmelCase_ : Optional[Any] = parser.parse_args_into_dataclasses()[0]
UpperC... | 641 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
low... | 511 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase : List[Any] = {
'xlm-mlm-en-2048': 'https://hu... | 511 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 717 |
from __future__ import annotations
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = str(snake_case )
return n == n[::-1]
def a__ ( snake_case = 1_000_000 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE ... | 131 | 0 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> Optional[Any]:
_lowerCamelCase = len(A_ )
_lowerCamelCase = len(A_ )
_lowerCamelCase = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_lowerC... | 661 |
'''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/LICEN... | 316 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCamelCase__ = 1.054571817e-34 # unit of ℏ : J * s
UpperCamelCase__ = 3e8 # unit of c : m * s^-1
def __SCREAMING_SNAKE_... | 718 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
UpperCamelCase__... | 640 | 0 |
"""simple docstring"""
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class snake_case__ ( unittest.TestCase ):
def a__ ( self ):
__a = [
"safety_checker/pytorch_model.bin",
"safety_checker/mod... | 528 | """simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
SCREAMING_SNAKE_CASE__:str = HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str] = parser.parse... | 528 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( a : int ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect num... | 720 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self ) -> Optional[Any]:
lowercase_ : ... | 7 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available()... | 521 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu... | 521 | 1 |
'''simple docstring'''
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 = ... | 318 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
... | 318 | 1 |
from collections import defaultdict
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Any = first_str.lower().strip()
UpperCAmelCase__: Tuple = second_str.lower().strip()
# Remove whitespace
UpperCAmelCase__: List[Any] = first_str.replace(" " ,"" ... | 113 |
import fire
from utils import calculate_rouge, save_json
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE=None ,**SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = [x.strip() for x in open(SCREAMING_SNAKE_CASE ).readlines()]
UpperCAmelCase__: Dict = [x.... | 113 | 1 |
'''simple docstring'''
# Copyright (c) 2021-, NVIDIA CORPORATION. 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.... | 257 | '''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__a = ... | 257 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, ... | 316 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 0 |
from __future__ import annotations
from math import pi
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One an... | 177 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_lowerCamelCase : List[str] = (720, 1280) # Height, Width
_lowerCamelCase : Optional[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
_lower... | 177 | 1 |
'''simple docstring'''
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) f... | 51 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_efficientformer": [
"EFFICIENTFORMER... | 510 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
a_ = logging.get_logger(__name__)
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
warnings.warn(
'... | 622 |
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
a_ = False
class __lowerCAmelCase ( unittest.TestCase ):
pass
@nightly
@re... | 622 | 1 |
from __future__ import annotations
__a = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__a = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[Any... | 30 |
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_transforme... | 548 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def UpperCAmelCase__ ( lowerCAmelCase__... | 197 | """simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_... | 197 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested... | 474 |
'''simple docstring'''
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class _UpperCamelCase ( A , unittest.TestCase ):
'''simple docstring'''
lowerCAm... | 474 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : int = 10_00 ) -> int:
_snake_case = -1
_snake_case = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_snake_case = (n * n - 2 ... | 430 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : float , __lowerCamelCase : float ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"{price_plus_tax(100, 0.25) = }")
print(F"{price_plus_tax(1_25.50, 0.05) = }")
| 430 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_A : str = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if not is_to... | 100 |
import torch
from torch import nn
class __snake_case ( nn.Module ):
'''simple docstring'''
def __init__( self , A_ , A_ , A_ , A_ , A_=1 , A_=False ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE_... | 100 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
... | 705 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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_commo... | 53 | 0 |
from __future__ import annotations
import math
def snake_case (UpperCamelCase : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of... | 165 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase : List[str] = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_torch_av... | 557 | 0 |
'''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.m... | 514 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_avai... | 514 | 1 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translatio... | 661 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_prop... | 661 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a= logging.get_logger(__name__)
a= {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See all Vivit models a... | 702 | '''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 287 | 0 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__a: Optional[Any] = namedtuple(
'''_TestCommandArgs''',
[
... | 108 |
from __future__ import annotations
from random import random
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , __UpperCamelCase = None ) -> Any:
'''simple docstring'''
__UpperCamelCase : Tuple = val... | 327 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,... | 343 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase : str = logging.get_logger(__name__)
# TODO: upload to AWS
lowercase : Optional[Any] = {
'yjernite/retribert-base-uncased': (
'https://... | 343 | 1 |
__a = 'Tobias Carryer'
from time import time
class lowercase__:
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMI... | 97 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_r... | 99 | 0 |
"""simple docstring"""
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 DPRCo... | 485 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class _UpperCAmelCase ( tf.keras.layers.Layer):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : Tuple... | 485 | 1 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
snake_case_ : Optional[int] = pd.read_csv('sample_data.csv', ... | 195 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case_ : Union[str, Any] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
't... | 195 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : List[Any] = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_... | 423 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
lowercase : int = logging.get_logger(__name__)
... | 423 | 1 |
'''simple docstring'''
class __snake_case :
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase : Tuple , lowerCamelCase : List[str] ) -> List[str]:
lowerCAmelCase_ : List[Any] = name
... | 275 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
_... | 40 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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_available():
... | 707 |
# 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
#
# Unless required ... | 557 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCod... | 369 |
'''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 _low... | 369 | 1 |
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,
resize,
to_channel... | 719 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
... | 638 | 0 |
'''simple docstring'''
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : int = 1000 ):
"""simple docstring"""
__A= 3
__A= 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__... | 186 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 186 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 200 ) ->int:
'''simple docstring'''
a : Dict = [1, 2, 5, 10, 20, 50, 100, 200]
a : Optional[Any] = [0] * (pence + 1)
a : List[Any] = 1 # base case: ... | 31 |
"""simple docstring"""
# 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/lic... | 31 | 1 |
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = ['image_processor', 'tokenizer']
lowerCAmelCase_ = 'AutoImageProcessor'
lowerCAmelC... | 44 |
'''simple docstring'''
from __future__ import annotations
def __a ( A__ , A__ = None , A__ = None , A__ = False , ) -> tuple[int, float, str]:
lowerCAmelCase = cipher_alphabet or [chr(A__ ) for i in range(97 , 123 ... | 649 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json""... | 712 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowerCAmelCase ( ):
lowercase__ = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
lowercase__ = parser.parse_args_into_dataclasses()[0]
lowercase__ = TensorFlowBenchmark(args=SCREA... | 37 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_de... | 486 | def lowerCAmelCase_ ( __A, __A ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ = [1]
for i in range(2, __A ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
... | 486 | 1 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_a : List[str] = ... | 571 |
def UpperCamelCase__ ( _A: Tuple , _A: List[str] , _A: Tuple , _A: Dict , _A: int , _A: List[str] ):
'''simple docstring'''
if index == r:
for j in range(_A ):
... | 571 | 1 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class A ( __UpperCAmelCase , ... | 325 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ... | 325 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ni... | 718 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Ra... | 321 | 0 |
'''simple docstring'''
from statistics import mean, stdev
def __a ( lowerCAmelCase__ : list , lowerCAmelCase__ : int = 3 ):
a__ : List[Any] = min(lowerCAmelCase__ )
a__ : Optional[int] = max(lowerCAmelCase__ )
# normalize data... | 688 |
'''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
__SCREAMING_SNAKE_CASE = logging.get_lo... | 688 | 1 |
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_params import TEXT_GUIDED_IM... | 701 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ =logging.get_logger(__name__)
UpperCAmelCase_ ={
"""vocab_file""": """vocab.txt""",
"""... | 33 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowercase : List[str] =logging.get_logger(__name__)
__lowercase : Dict ={
"""shi-labs/dinat-mini-i... | 54 |
def UpperCAmelCase_ ( ) -> int:
"""simple docstring"""
return 1
def UpperCAmelCase_ ( snake_case__ ) -> int:
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCAmelCase_ ( snake_case__ )... | 193 | 0 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
A : int = get_tests_dir("""fixtures/test_sentencepiec... | 707 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataColl... | 136 | 0 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _UpperCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCAmelCase__ = FileLock(str(tmpd... | 644 | """simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCAmelCase : Dict = TypeVar("KT")
__lowerCAmelCase : Optional[Any] = TypeVar("VT")
class a_ ( Generic[KT, VT] ):
def __init__( s... | 644 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def A ( lowercase , lowe... | 721 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def A ( lowercase , lowe... | 3 | 0 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCAmelCase ( _lowercase : int ) -> str:
"""simple docstring"""
if not isinstance(a_ , a_ ):
raise TypeError('''Undefined for non-integers''' )
elif precis... | 552 |
"""simple docstring"""
UpperCAmelCase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def lowerCam... | 677 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowercase ( _snake_case : Optional[Any] ) ->Any:
"""simple docstring"""
return x + 2
class _UpperCAmelCase ( unittest.T... | 229 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common imp... | 229 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransfor... | 437 |
# Function to print upper half of diamond (pyramid)
def __UpperCamelCase ( _A ):
for i in range(0 , _A ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
for _ in range(0 , i + 1 ):... | 431 | 0 |
"""simple docstring"""
from __future__ import annotations
_lowercase : Dict = tuple[int, int, int]
_lowercase : Optional[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
_lowercase : Tuple = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
#... | 625 |
"""simple docstring"""
_lowercase : str = 0 # The first color of the flag.
_lowercase : Dict = 1 # The second color of the flag.
_lowercase : Tuple = 2 # The third color of the flag.
_lowercase : Optional[int] = (red, white, blue)
def snake_case__ ( _... | 625 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except O... | 119 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 119 | 1 |
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
a_ = logging.get_logger(__name__)
a_ = {
"""microsoft/ta... | 622 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __lowerCAmelCase ( lowerCAmelCase__ , unittest... | 622 | 1 |
"""simple docstring"""
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__ ( UpperCAmelCase_ , UpperCAmelCase_ ... | 554 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae im... | 554 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_f... | 28 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : str ):
"""simple docstring"""
_snake_case : str = int(snake_case__ )
# Initialize Result
_snake_case : str = []
# Traverse through ... | 28 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from ... | 367 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between che... | 367 | 1 |
'''simple docstring'''
def A_ ( snake_case = 1000 ):
SCREAMING_SNAKE_CASE:List[str] = 3
SCREAMING_SNAKE_CASE:Tuple = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name... | 465 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
A_ = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL... | 465 | 1 |
from __future__ import annotations
import math
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if not scores:
... | 141 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
SCREAMING_SNAKE_CASE : List[str] = numpy.array([0, 0])
SCREAMING_SNAKE_CASE : Tuple = numpy.array([0.5, 0.8660254])
SCREAMING_SNAKE_CASE : Tup... | 141 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __snake_case ( lowerCAmelCase__ , lowerCAmelCase__ ):
@register_to_config
def __init__( self , *,
_A = 4 , _A = 768 , _A ... | 620 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Any = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenization_mvp": ["MvpTokenizer"]... | 620 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
snake_case : List[str] = 50_000
snake_case : Tuple = 5_000
snake_case , snake_case : Any = os.path.split(__file__)
snake_case : Any = os... | 566 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def lowercase__ ( *__UpperCamelCase : Optional[Any] ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ):... | 566 | 1 |
'''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, V... | 6 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase__ = 300 # TEMPERATURE (unit = K)
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ... | 6 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : list[list[int]] = [[0 for _ in range(_lowerCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_lowerCamelCase : ... | 46 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate... | 285 | 0 |
from pathlib import Path
import fire
from tqdm import tqdm
def A ( lowercase="ro" , lowercase="en" , lowercase="wmt16" , lowercase=None ) -> Any:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('run pip install d... | 711 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"vocab_file": ... | 3 | 0 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _UpperCamelCase ( ) -> tuple[list[int], int]:
lowerCamelCase_ = [randint(-10_00 ,10_00 ) for i in range(10 )]
lowerCamelC... | 42 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
e... | 72 | 0 |
"""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 (
... | 720 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
'''configuration_roberta... | 24 | 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.fsmt.configurati... | 548 |
import re
def lowerCamelCase_ ( lowerCamelCase__ ):
return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]" , str_ )]
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = split_input(str_ )
return "".join(
... | 463 | 0 |
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__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""facebook/levit-128S""": ""... | 707 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 547 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _A ( _lowerCAmelCase ):
"""simple docstring"""
create_state_space_tree(_lowerCAmelCase , [] , 0 )
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
... | 474 |
'''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, torc... | 474 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, r... | 144 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''microsoft/focalnet-tiny''': '''https://huggin... | 144 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca im... | 55 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE = True , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ):
if not is_tqdm_available():
ra... | 590 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def UpperCamelCase( ) -> Optional[int]:
'''simple docstring'''
snake_case_ = 9
snake_case_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
... | 714 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCamelCase_ = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws"... | 161 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : Union[str, Any] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvN... | 15 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=__UpperCAmelCase ):
"""simple docstring"""
_a : Optional[int] = ['''note_seq''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__... | 200 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
... | 549 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json"... | 549 | 1 |
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