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 |
|---|---|---|---|---|
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...sched... | 223 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration... | 223 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...... | 542 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler... | 542 | 1 |
"""simple docstring"""
from torch import nn
def snake_case_ ( A_ : int ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
retu... | 83 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A__: Optional[int] = logging.get... | 694 | 0 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( __snake_case : int | str ) -> bool:
"""simple docstring"""
A__ : List[str] =str(__snake_case )
return n == n[::-1]
def __lowerCamelCase ( __snake_case : int = 1_000_000 )... | 687 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
class lowerCamelCase ( lowercase_... | 687 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvai... | 273 |
"""simple docstring"""
def __magic_name__ ( UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , ) -> float:
a__ = [redshift, radiation_density, matter_densit... | 273 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
_lowercase : Optional[int] = r"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be use... | 50 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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... | 50 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _snake_case ):
if not nums:
return 0
UpperCAmelCase__ : Optional[int] = nums[0]
UpperCAmelCase__ : str = 0
for num in nums[1:]:
UpperCAmelCase__ , ... | 110 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForme... | 110 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowerCAmelCase__ = TypeVar('''T''')
class snake_case__(Generic[T] ):
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE ... | 81 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
... | 81 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self , *,
... | 242 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Union[str, Any] ... | 242 | 1 |
'''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
snake_case_ : List[str] = {
# 1536-bit
5: ... | 708 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __a (unittest.TestCase ):
def UpperCAmelCase__ ( self : Dict ) -> Dict:
"""simple docstring"""... | 644 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 10 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require... | 334 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A : Tuple = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Swi... | 330 | '''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging imp... | 330 | 1 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> Dict:
'''simple docstring'''
if not head:
return True
# split the list to two parts
_lowerCamelCase, _lowerCamelCase : List[str] = head.next, head
while fast and fast.next:
_... | 46 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
... | 178 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDepend... | 207 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def SCREAMING_SNAKE_CASE( ) -> int:
a__ : List[str] = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"path": ["test_1.... | 207 | 1 |
# Algorithm for the pigeonhole sorting
def _SCREAMING_SNAKE_CASE ( lowercase : str ):
'''simple docstring'''
lowerCamelCase_ = min(lowercase ) # min() finds the minimum value
lowerCamelCase_ = max(lowercase ) # max() finds the ... | 70 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/c... | 502 | 0 |
from __future__ import annotations
import os
from typing import Any
import requests
__lowerCAmelCase : Any = "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowerCAmelCase : Union[str, Any] = B... | 715 | 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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_ver... | 164 | 0 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, bu... | 104 |
"""simple docstring"""
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 ModelMix... | 104 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
A = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
('time_embed.0... | 46 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_UpperCamelCase : Optional[Any] ='\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and... | 206 |
def a__ (__lowercase :str , __lowercase :str ) -> float:
def get_matched_characters(__lowercase :str , __lowercase :str ) -> str:
_A : Union[str, Any] = []
_A : Dict = min(len(_stra ) , len(_stra ) ) // 2
... | 206 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 574 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
_lowercase : Union[str, Any] =[
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0,... | 574 | 1 |
'''simple docstring'''
class _a :
'''simple docstring'''
def __init__( self ) -> str:
snake_case : Optional[Any] = 0
snake_case : List[str] = 0
snake_case : Union[str, Any] ... | 116 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXCon... | 116 | 1 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : int = 100):
UpperCamelCase = (n * (n + 1) // 2) ** 2
UpperCamelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solution() = }''')
... | 715 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase__ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , low... | 350 | 0 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase ( lowercase_):
"""simple docstring"""
def __init__( self : Optional[Any... | 404 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_te... | 404 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( a_ : Optional[int] , a_ : int ):
__a = int(__lowerCAmelCase )
# Initialize Result
__a = []
# Traverse through all denomination
for denomination in reversed(__lowerCAmelCase ):
#... | 712 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( a_ : int ):
__a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def SCREAMING_SNAKE_CASE ( a_ : int = 100 ):
__a = 1
__a = 2
for i ... | 490 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils ... | 63 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 630 | 0 |
from scipy.stats import pearsonr
import datasets
__UpperCamelCase : Tuple = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumpt... | 712 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Union[str, Any] = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAIN... | 458 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Dict:
'''simple docstring'''
return ConvertCommand(
args.model_type, args.tf_checkpoint, args.pytorch... | 343 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClass... | 343 | 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
l... | 37 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _snake_case ( lowercase__):
def A__ ( self : Optional[Any], __lowercase : str ):
with open(__lowercase, encoding="utf-8" ) as ... | 37 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) ... | 75 | """simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 599 | 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
lowerCAmelCase : Tuple = logging.get_logger(__name__)
def lowerCAmelCase ( UpperCamelCase__ : List[Any] ) -> ... | 146 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a ( __lowercase ):
SCREAMING_SNAKE_CASE__ ... | 146 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
res... | 391 |
"""simple docstring"""
from manim import *
class __lowerCAmelCase ( UpperCAmelCase ):
'''simple docstring'''
def UpperCamelCase__ ( self: int ):
UpperCamelCase_ =Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_ =Rectangle(... | 391 | 1 |
'''simple docstring'''
import math
def _snake_case ( lowercase , lowercase ) -> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 ... | 719 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 130 |
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_device
fro... | 130 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.js... | 718 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...tes... | 544 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 227 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = OrderedDict(
[
# Base mod... | 326 | 0 |
def __lowercase ( _UpperCAmelCase ) -> bool:
'''simple docstring'''
return str(lowerCamelCase__ ) == str(lowerCamelCase__ )[::-1]
def __lowercase ( _UpperCAmelCase ) -> int:
'''simple docstring'''
return int(lowerCamelCase__ ) + int(str(lowerCamelCase__ )[::-1]... | 710 | import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __snake_case ):
"""simple docstring"""
__lowerCAmelCase = (DDIMParallelScheduler,)
__lowerCAmelCase = (("""eta""", 0.0), ("""num_inference_steps"""... | 576 | 0 |
import gc
import threading
import time
import psutil
import torch
class __a :
def __init__( self : Dict ):
'''simple docstring'''
UpperCamelCase__ : Any = psutil.Process()
UpperCamelCase__ : int ... | 228 |
lowerCamelCase : dict[str, float] ={
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_9344,
"knot": 1.852,
}
lowerCamelCase : dict[str, float] ={
"km/h": 1.0,
"m/s": 0.2_7777_7778,
"mph": 0.6_2137_1192,
"knot": 0.5_3995_6803,
}
de... | 228 | 1 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_a : Optional[int] = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
... | 111 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils im... | 111 | 1 |
'''simple docstring'''
# Imports
import numpy as np
class _snake_case :
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
... | 284 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'YituTech/conv-bert-base': 'https://huggingface.co/YituT... | 132 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_to... | 706 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_to... | 71 | 0 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __UpperCamelCase ( unittest.TestCase ):
def a__ ( self :Dict ):
snake_case_ : List[str] = get_activation("""swi... | 334 |
'''simple docstring'''
__A : List[Any] = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def UpperCAmelCase ( lowerCamelCase_ :float ... | 334 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ : List[Any] = logging.get_logger(__name__)
lowerCAmelCase__ : ... | 502 |
'''simple docstring'''
from collections.abc import Generator
def _a ( ):
"""simple docstring"""
snake_case__ , snake_case__ : List[Any] = 0, 1
while True:
snake_case__ , snake_case__ : str = b, a + b
yield b
def ... | 502 | 1 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
snake_case = logging... | 67 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
... | 294 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.ut... | 505 |
"""simple docstring"""
def _snake_case ( _snake_case : bytes ) -> str:
'''simple docstring'''
return "".join([hex(_snake_case )[2:].zfill(2 ).upper() for byte in list(_snake_case )] )
def _snake_case ( _snake_case : ... | 505 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase ( ) -> Optional[int]:
from torch.utils.cpp_extension import load
__magic_name__ = Path(__a ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
__magic_name__ = [
root / filename
for filen... | 490 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__lowerCAmelCase = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (... | 229 | 0 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowerCamelCase__ ( _A ):
a : Tuple = {}
a : List[Any] = job['started_at']
a : str = job['completed_at']
a : Dict ... | 195 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 195 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""facebook/data2vec-text-base""": """https://huggi... | 658 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase ... | 84 | 0 |
'''simple docstring'''
def snake_case_ ( lowercase__ : str ):
'''simple docstring'''
_lowerCAmelCase =0
for ch in input_str:
_lowerCAmelCase =ord(lowercase__ )
_lowerCAmelCase =pow(2 , lowercase__ )
# If we alre... | 717 |
import math
def snake_case_ ( lowercase__ : int ):
'''simple docstring'''
_lowerCAmelCase =[True] * n
_lowerCAmelCase =False
_lowerCAmelCase =False
_lowerCAmelCase =True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
... | 149 | 0 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes... | 12 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : List[Any] = logging.get_logger(__name__)
lowerCamelCase__ : Union[str, Any] = {
"... | 12 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def UpperCamelCase_( __magic_name__ : str , __magic_name__ : str , __magic_name__ : Optional[str] = None ):
"""simple docstring"""
... | 382 |
from __future__ import annotations
from math import pow, sqrt
def UpperCamelCase_( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
... | 382 | 1 |
"""simple docstring"""
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
a = logging.get_logger(__name__)... | 7 |
"""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_... | 607 | 0 |
from __future__ import annotations
def UpperCamelCase__ ( UpperCAmelCase ) -> Optional[int]:
"""simple docstring"""
return len(set(UpperCAmelCase ) ) == len(UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 718 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_util... | 307 | 0 |
'''simple docstring'''
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_av... | 476 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : str = {
'google/pix2struct-textcaps-base': (
'ht... | 476 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
return 10 - x * x
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# Bolzano theory in order to find if there is a root between a and b
if equation(SCREAMING_SNAKE... | 438 | '''simple docstring'''
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_avai... | 438 | 1 |
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 DDPMScheduler
from ...utils... | 40 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ ... | 426 | 0 |
import comet # From: unbabel-comet
import torch
import datasets
__snake_case = datasets.logging.get_logger(__name__)
__snake_case = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel's ... | 715 |
"""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 TokenizerTesterMix... | 117 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acceler... | 47 |
import math
from datetime import datetime, timedelta
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
__a : Union[str, Any] = year % 1_9
__a : int = year % 4
__a : Optional[int] = year % 7
__a : Dict... | 47 | 1 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase ( _a ):
snake_case_ : int = """SpeechT5FeatureExtractor"""
snake_case_ : Tuple = """SpeechT5Tokenizer"""
def __init__( self : Any , SCREAMING_SNAKE_CASE : str... | 707 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 473 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn... | 279 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE_ ( _UpperCamelCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : Any , lowerCAmelCase : str ) -> ... | 279 | 1 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ... | 701 |
def lowercase__( A ):
return " ".join(
''.join(word[::-1] ) if len(A ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('Hey wollef sroirraw'))
| 303 | 0 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 82 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowerCamelCase = False
class lowercase_... | 82 | 1 |
'''simple docstring'''
from copy import deepcopy
class __snake_case :
def __init__( self : Any , _UpperCAmelCase : list[int] | None = None , _UpperCAmelCase : int | None = None ) -> None:
'''simple docstring'''
if arr is None and size i... | 704 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_config... | 196 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipel... | 99 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wav... | 193 | 0 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_... | 637 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 637 | 1 |
# 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 thi... | 317 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( __snake_case , __snake_case , __snake_case ) -> str:
# Initialise PyTorch mo... | 317 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}... | 49 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_av... | 49 | 1 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'The converted tokenize... | 219 |
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 __A( unittest.TestCas... | 219 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = (IPNDMScheduler,)
_lowercas... | 162 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def A (__lowerCamelCase :Any ):
_lowerCAmelCase = np.max(__lowerCamelCase , axis=-1 , keepdims=__lowerCamelCase )
_lowerCAmelCase = np.exp(outputs - maxes )
return shifted_exp / shifted... | 162 | 1 |
"""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_subpro... | 177 | """simple docstring"""
from math import sqrt
def lowerCamelCase_ ( __lowerCAmelCase ) -> bool:
'''simple docstring'''
assert isinstance(__lowerCAmelCase , __lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
... | 530 | 0 |
from __future__ import annotations
from collections.abc import Callable
__lowerCamelCase : int = list[list[float | int]]
def A__ ( _a : Matrix , _a : Matrix ):
'''simple docstring'''
snake_case__ : int =len(_a )
snake_case__ : Mat... | 448 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCamelCase : Dict = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popovi{\'c}, Maja\",
booktitle = \"... | 448 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOut... | 558 |
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 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __SCREAMING_SNAKE_CASE ( UpperCamelCase_ ):
... | 717 | '''simple docstring'''
import qiskit
def _lowerCamelCase ( lowerCamelCase_ : int = 2 ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = qubits
# Using Aer's simulator
UpperCAmelCase_ : str = qiskit.Aer.get_backend('aer_simu... | 389 | 0 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import i... | 322 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase__ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 322 | 1 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE... | 704 |
"""simple docstring"""
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
_A = logging.getLogger(__name__)
if is_torch_tpu_available(che... | 507 | 0 |
'''simple docstring'''
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import ... | 577 |
'''simple docstring'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate... | 577 | 1 |
def lowerCAmelCase__ ( a__ ) ->str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 701 | 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_simplify,
require_tf,
... | 82 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCAmelCase ( ):
lowerCamelCase : List[str] = {
"""repo_name""": ["""test_repo1"""... | 320 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ... | 336 | 0 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 560 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __A (_SCREAMING_SNAKE_CASE ) ->float:
"""simple docstring"""
return np.dot(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
class ... | 560 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : typing.Counter[int] = Counter()
for base in range(1, max_perimeter + 1 ):
for perpendicular in range(A__, max_perimeter ... | 101 |
import argparse
import json
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 acc... | 376 | 0 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
A_ = datasets.logging.get_logger(__name__)
A_ = "\\n@InProceedings{moosavi... | 465 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
A_ = datasets.logging.get_logger(__name__)
A_ = "\\n@InProceedings{moosavi... | 465 | 1 |
A : Optional[int] = {}
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Any ) -> Optional[int]:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible... | 287 |
"""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 accele... | 180 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class UpperCA... | 702 |
def a ( _UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] ):
'''simple docstring'''
__UpperCAmelCase : Dict = len(_UpperCAmelCase )
print('''The following activities are selected:''' )
# The first activity is always... | 241 | 0 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : Any = logging.get_logger(__name__)
a : List[str] = {
"""nielsr/canine-s""": 2_048,
}
# Unicode defines 1,114,112... | 63 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_forma... | 423 | 0 |
from __future__ import annotations
from typing import Any
class _lowerCAmelCase :
def __init__( self , _UpperCamelCase ) -> None:
lowerCAmelCase_ = num_of_nodes
lowerCAmelCase_ = []
lowerCAmelCase_ = {}
def __a... | 709 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class _lowerCAmelCase ( __a ):
_lowercase ='''transfo-xl'''
_lo... | 279 | 0 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : List[Any] , _lowerCAmelCase... | 474 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : Optional[int] , _lowerCAmelCase : Tuple="" , _lowerCAmelCase : List[str]="train"):
... | 474 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational i... | 568 |
'''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
... | 568 | 1 |
from __future__ import annotations
import time
import numpy as np
UpperCamelCase = [8, 5, 9, 7]
UpperCamelCase = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
UpperCamelCase = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5,... | 61 |
# 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
#
# Unles... | 61 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowercase ... | 452 | import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impor... | 452 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class a ( lowerCAmelCase_ ):
__lowerCAmelCase : Li... | 252 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( __a, __a ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = len(__a ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
SCREAM... | 628 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def ... | 628 | 1 |
import qiskit
def a__ ( A__, A__ ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
SCREAMING_SNAKE_CASE_ : List[str] = qiskit.QuantumCircuit(A_, A_ ... | 101 |
'''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 d... | 577 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> Tuple:
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , ... | 311 |
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... | 311 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_doc... | 272 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig'... | 272 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 647 | def __lowerCamelCase (UpperCAmelCase__ : list[int] ):
if not numbers:
return 0
if not isinstance(UpperCAmelCase__ , (list, tuple) ) or not all(
isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for number in numbers ):
raise ValueError("numbers... | 647 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Dict = logging.get_logger(__name__)
A__ : Optional[int] = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class lowercase ( _A ):
__a = """c... | 233 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 529 | 0 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowerCamelCase_ ( A : Dict , A : List[Any]=() , A : List[Any]=None , A : str="no" , A : Optional[Any]=... | 704 |
def lowerCamelCase_ ( ):
"""simple docstring"""
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(A , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f'''{solutio... | 413 | 0 |
'''simple docstring'''
import os
__lowerCAmelCase = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}
def __UpperCamelCase ( lowercase_ : str ):
"""simple docstring"""
a_ = 0
a_ = 0
while in... | 536 |
'''simple docstring'''
def __UpperCamelCase ( lowercase_ : list[int] , lowercase_ : list[int] , lowercase_ : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, ne... | 536 | 1 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenera... | 65 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_ten... | 65 | 1 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
a__ = numpy.array([0, 0])
a__ = numpy.array([0.5, 0.8660254])
a__ = numpy.array([1, 0])
a__ = [VECTOR_1, VEC... | 14 | 1 |
'''simple docstring'''
import operator as op
def SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE ):
lowerCAmelCase_ : Union[str, Any] =[]
lowerCAmelCase_ : Tuple =lambda _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : int(x / y ) ... | 305 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__lowercase = logging.get_logger(__name__)
class _snake_case ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Union[str... | 305 | 1 |
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