code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A : Dict = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase ( lowerCAmelCase__ ):
... | 57 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test... | 282 | 0 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics a... | 342 | import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import... | 342 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_lowercase : int =TypeVar("T")
class snake_case__ (Generic[T] ):
""... | 170 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationT... | 184 | 0 |
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
UpperCamelCase__ = ""
... | 87 |
def _UpperCamelCase (a__ :str ):
"""simple docstring"""
UpperCamelCase__ = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCamelCase__ = """"""
UpperCamelCase__ = """"""
# append ... | 87 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A = logging.get_logger(__name__)
_A = {
'''salesforce/b... | 122 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__lowerCAmelCase = logging.getLogger()
@unittest.skip('''Temporar... | 271 | 0 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase_ = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and ... | 360 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar('''T''')
UpperCamelCase_ = TypeVar('''U''')
class _snake_case ( Generic[T, U] ):
'''simple docstring'''
def __init__( ... | 59 | 0 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluati... | 317 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructu... | 317 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
neste... | 202 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
f... | 202 | 1 |
'''simple docstring'''
import os
import re
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
lowerCamelCase = logging.get_logger(__name__)
lower... | 166 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowerCamelCase = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.... | 166 | 1 |
"""simple docstring"""
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-month... | 357 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import lo... | 144 | 0 |
from collections.abc import Sequence
from queue import Queue
class A__ :
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase , lowercase=None , lowercase=None) -> Dict:
'''simple docstring'''
a__ :... | 99 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 337 | 0 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__(self : List[Any] , a__ : int = 0 ):
"""simple docstring"""
__snake_case = key
def ... | 370 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
snake_case_ = logging.get_logger(__name__)
snake_case_ = {'vocab_file': 'vocab.js... | 238 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-o... | 153 |
"""simple docstring"""
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = set(_SCREAMING_SNAKE_CASE ), [start]
while stack:
UpperCamelCase = stack.pop()
... | 153 | 1 |
import math
from datetime import datetime, timedelta
def lowerCamelCase_ ( _UpperCamelCase ) -> datetime:
"""simple docstring"""
snake_case_ : str = year % 19
snake_case_ : Tuple = year % 4
snake_case_ : Dict =... | 279 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=1_000 ) -> str:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
snake_case_ ... | 279 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 342 |
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_device
from transformers.utils i... | 342 | 1 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 348 |
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
@... | 348 | 1 |
def lowercase_ ( _lowerCamelCase : int):
if isinstance(_lowerCamelCase , _lowerCamelCase):
raise TypeError("'float' object cannot be interpreted as an integer")
if isinstance(_lowerCamelCase , _lowerCamelCase):
raise TypeError("'str' object cannot be interpreted as an... | 87 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_A... | 87 | 1 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _lowerCAmelCase ( A__: Callable , A__: float , A__: float , A__: float , A__: float ):
'''simple docstring'''
UpperCAmelCase =... | 369 |
# Function to print upper half of diamond (pyramid)
def _lowerCAmelCase ( A__: str ):
'''simple docstring'''
for i in range(0 , A__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
fo... | 152 | 0 |
import logging
from transformers.configuration_utils import PretrainedConfig
__A =logging.getLogger(__name__)
class _SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ = "masked_bert"
def __init__( self , lowercase=30522 , lowercase=768 , lowercase=12 , lo... | 19 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoMod... | 59 | 0 |
def snake_case ( snake_case__ :str , snake_case__ :str) -> list:
_A = len(snake_case__)
_A = []
for i in range(len(snake_case__) - pat_len + 1):
_A = True
for j in range(snake_case__):
... | 81 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDep... | 81 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class a__ ( a_ ):
__lowerCAmelCase = ["""image_processor""", """feature_extractor"""]
__lowerCAmelCase = """TvltImageProcessor"""
__lowerCAmelCase = """TvltFeatureEx... | 202 |
"""simple docstring"""
def __magic_name__ ( __snake_case : list ) -> list:
if len(__snake_case ) < 2:
return collection
def circle_sort_util(__snake_case : list , __snake_case : int , __snake_case : int ) -> bool:
... | 202 | 1 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__lowercase = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
__lowercase = None
def lowerCAmelCase ():
"""simple docstring"... | 359 | """simple docstring"""
def lowerCAmelCase (__UpperCamelCase : int = 1_0_0_0 ):
"""simple docstring"""
__UpperCamelCase =-1
__UpperCamelCase =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
... | 85 | 0 |
def lowerCAmelCase_ ( snake_case_ ):
return "".join([hex(snake_case_ )[2:].zfill(2 ).upper() for byte in list(snake_case_ )] )
def lowerCAmelCase_ ( snake_case_ ):
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
... | 26 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class lowercase__ ( unittest.TestCase ):
_UpperCAmelCase :Union[str, Any] = JukeboxTokenizer
_UpperCAmelCase :List[Any] = {
"artis... | 144 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Cond... | 357 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .sche... | 298 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : Optional[Any] = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/r... | 224 |
"""simple docstring"""
from __future__ import annotations
def snake_case__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
if b == 0:
return (1, 0)
((lowerCamelCase__) , (lowerCamelCase__)) : Any =extended_euclid(__lowerCamelCas... | 238 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_util... | 350 | '''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Co... | 214 | 0 |
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.... | 279 |
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'''):
lowerCAmelCase_ = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
... | 279 | 1 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_... | 128 |
"""simple docstring"""
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_dens... | 128 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 248 |
__snake_case : Dict = 6_55_21
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : List[str] = 1
a_ : Any = 0
for plain_chr in plain_text:
a_ : Tuple = (a + ord(a__)) % MOD_ADLER
a_ : Union[str, Any] ... | 248 | 1 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = 'sshleifer/bart-tiny-random'
__a = 'patr... | 363 |
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_format,
)
f... | 235 | 0 |
"""simple docstring"""
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'''pipelines_utils''',
'''0.22.0''',
'''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is depreca... | 160 |
'''simple docstring'''
def _a( UpperCamelCase__ : int = 1_0_0_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =2**power
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
while n:
SCREAMIN... | 152 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
if not is_torch_availabl... | 192 | import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase ( __A ):
'''simple docstring'''
def __init__( self , *lowercase , **lowe... | 192 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings... | 81 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number | (1 << position)
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number & ~(1 << position)
def _A ... | 81 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"""asapp/sew-d-tiny-100k""": """https://h... | 370 |
from __future__ import annotations
import queue
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : Dict) ->Any:
'''simple docstring'''
A__ = data
A__ = None
... | 231 | 0 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def lowerCamelCase ( __lowerCamelCase : Optional[int] ) ->Dict:
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp <... | 58 |
'''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,
)
_SCREAMING_SNAKE_CASE : Optional[Any... | 85 | 0 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ ) -> Optional[int]:
_a : Any = 1
_a : str = 2
while i * i <= n:
_a : int = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
... | 107 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''dis... | 107 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__n... | 48 |
'''simple docstring'''
from maths.prime_check import is_prime
def __lowerCAmelCase ( snake_case__ ):
if not isinstance(snake_case__ , snake_case__ ):
__UpperCamelCase : Optional[int] = F"Input value of [number={number}] must be an intege... | 298 | 0 |
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 ...uti... | 328 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SC... | 328 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torc... | 339 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""... | 339 | 1 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( __A, __A ... | 350 | # 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... | 143 | 0 |
def _lowerCAmelCase (_lowerCAmelCase):
if len(_lowerCAmelCase) <= 1:
return lst
UpperCamelCase_ = 1
while i < len(_lowerCAmelCase):
if lst[i - 1] <= lst[i]:
i += 1
else:
UpperCamelCase_ , UpperCamelCase_ ... | 128 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaFo... | 128 | 1 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 350 |
import pytest
import datasets
# Import fixture modules as plugins
__A = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def __a ( lowerCAmelCase_ : Optional[Any] ,lowerCAmelCase_ : Any ) -> Tuple:
'''simple docstring'''... | 277 | 0 |
from manim import *
class __lowerCAmelCase ( lowerCAmelCase):
def SCREAMING_SNAKE_CASE ( self: Tuple ):
lowercase :Tuple = Rectangle(height=0.5 , width=0.5 )
lowercase :Tuple = Rectangle(height=0.46 , width=0.46 ).set_str... | 236 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : List[str] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 236 | 1 |
import math
class __SCREAMING_SNAKE_CASE :
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
lowercase : Union[str, Any] = 0.0
lowercase : Tuple = 0.0
for i in ... | 173 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
C... | 173 | 1 |
from __future__ import annotations
def UpperCamelCase (lowercase_: dict , lowercase_: str ) -> set[str]:
A__ , A__ : Optional[Any] = set(lowercase_ ), [start]
while stack:
A__ : str = stack.pop()
explored.add(lowercase_ )
# Differe... | 192 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""... | 192 | 1 |
'''simple docstring'''
import string
def _snake_case ( _SCREAMING_SNAKE_CASE : str ) -> None:
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
lowerCAmelCase = """"""
for symbol in message:
if symbol... | 187 |
'''simple docstring'''
from __future__ import annotations
class __snake_case:
'''simple docstring'''
def __init__( self , A_ = 0 ) -> Dict:
lowerCAmelCase = key
def __snake_case ( self , A_ , A_ ) -> list[str]:
... | 187 | 1 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MO... | 22 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""camembert-base""": """https://huggingface.co/camembert... | 287 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __A :
"""simple docstring"""
__lowerCAmelCase = 42
__lowerCAmelCase = None
__lowerCAmelCase ... | 362 |
"""simple docstring"""
def _A ( lowercase = 2_00_00_00 ):
"""simple docstring"""
a =[0 for i in range(n + 1 )]
a =1
a =1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 215 | 0 |
from __future__ import annotations
import math
def __magic_name__ ( A : float, A : int ):
'''simple docstring'''
a = u
for i in range(1, A ):
a = temp * (u - i)
return temp
def __magic_name__ ( ):
'''simple docstring'''
... | 107 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __magic_name__ ( A : NDArray[floataa], A : NDArray[floataa], A : list[int], A : int, ):
'''simple docstring'''
a , a = coefficient_matr... | 107 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_d... | 166 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCamelCase ( tf.keras.layers.Layer ):
'''simple docstri... | 166 | 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 im... | 328 |
from __future__ import annotations
from collections import deque
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ )-> int:
'''simple docstring'''
__UpperCamelCase = []
... | 328 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCamelCase__ : Lis... | 164 |
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,... | 164 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE (metaclass=lowerCamelCase_ ):
"""simple docstring"""
__a =['torch']
def __init__( self : Tuple , *__a : Tuple , **__a :... | 63 | 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... | 143 | 0 |
import math
from collections.abc import Iterator
from itertools import takewhile
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or numbe... | 330 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"""snap-research/efficientformer-l1-300""": (
"""https:/... | 330 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase_ )
class lowercase__( lowerCAmelCase_ ):
"""simple docstring"""
a :List[Any] = field(defa... | 30 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ :int = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}
try:
if not is_torch_available():
rais... | 277 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowerCamelCase ( lowerCamelCase__ : Tuple , lowerCamelCas... | 66 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
Upper... | 66 | 1 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules imp... | 173 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def __magic_name__ ( lowercase ):
return np.maximum(0 , lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 173 | 1 |
"""simple docstring"""
from __future__ import annotations
import requests
lowercase__ :Optional[int] = set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked co... | 361 |
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
if is_tor... | 97 | 0 |
import unittest
from knapsack import knapsack as k
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
sna... | 187 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from... | 187 | 1 |
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 __lowerCamelCase ( ... | 352 | import numpy as np
def __lowerCamelCase ( lowerCAmelCase__ ):
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( lowerCAmelCase__ ):
return vector * sigmoid(lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 119 | 0 |
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
if is_t... | 21 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowercase ... | 215 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {
"""configuration_rember... | 181 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
snake_case_ = logging.get_logger(__name__)
def _lowerCAmelCase ( lowercase_ ):
Uppe... | 181 | 1 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cub... | 215 |
"""simple docstring"""
from math import ceil
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = list(range(0 , snake_case__ ) )
SCREAMING_SNAKE_CASE__ = [item for sublist in list(device_map.value... | 165 | 0 |
"""simple docstring"""
lowercase : Tuple = [0, 2, 4, 6, 8]
lowercase : List[str] = [1, 3, 5, 7, 9]
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[int] , _lowerCamelCase : ... | 371 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
_A = 42
_A = 42
class lowerCamelCase__ ... | 151 | 0 |
'''simple docstring'''
import os
def _A ( ):
lowercase__ = os.path.dirname(os.path.realpath(lowercase__ ) )
lowercase__ = os.path.join(lowercase__ , """triangle.txt""" )
with open(lowercase__ ) as f:
lowercase__ ... | 164 |
'''simple docstring'''
def _A ( lowercase__ = 1000000 ):
lowercase__ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ... | 164 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggingface.co/CarlCochet/trajectory-transforme... | 333 | import argparse
import datetime
def lowercase_ ( _lowerCamelCase : str):
lowercase__ : Optional[Any] = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturda... | 333 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __snake_case( _lowerCAmelCase ) -> str:
return "".join(sorted(_lowerCAmelCase ) )
def __snake_case( _lowerCAmelCase ) -> list[str]:
return word_... | 35 |
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
_snake_case : List[Any] = logging.get_logger(__name__)
_snake_case : List[Any] ... | 284 | 0 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, 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 ModelT... | 370 | '''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules i... | 18 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertToke... | 66 |
"""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, torch_d... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case , _snake_case = None , _snake_case = None , _snake_case = False , ):
"""simple docstring"""
UpperCAmelCase = cipher_alphabet or [chr(_lowercase ) for i in range(97 , 12... | 363 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokeniz... | 234 | 0 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, 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_modeli... | 31 |
'''simple docstring'''
def a ( __a ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
UpperCamelCase__ :Optional[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) =... | 97 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
class A__ ( snake_case__ ):
"... | 213 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
_SC... | 213 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
__a: Optional[int] = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
__a: Optional[Any] = """
Args:
... | 198 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/confi... | 119 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPExcep... | 311 |
'''simple docstring'''
from __future__ import annotations
from random import random
class A :
def __init__( self , SCREAMING_SNAKE_CASE = None ) -> Tuple:
"""simple docstring"""
A : Optional[Any] = ... | 311 | 1 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def a__ ( lowerCAmelCase__ ) -> Tuple:
UpperCAmelCase__ : Any = FileLock(str(tmpdir / '''foo.lock''' ) )
UpperCAmelCase__... | 181 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --u... | 181 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfi... | 360 |
"""simple docstring"""
def __A ( a_ :int = 1_00_00_00) -> int:
__a : Tuple = [i - 1 for i in range(limit + 1)]
for i in range(2 , limit + 1):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , a_):
... | 188 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOut... | 84 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowercase__ = logging.get_logger(__name__)
class A_ ( _snake_case ):
'''simple docstring'''
def __init__( se... | 151 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaP... | 95 | """simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
S... | 95 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowercase__ ( __lowercase : int , __lowercase : Optional[Any] , __lowercase : Dict , __lowercase : Optional[int]=1024 ) -... | 53 |
import inspect
import unittest
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def lowercase_ ( self ) -> int:
try:
import diffusers # noqa: F401
except ImportError:
assert False
d... | 262 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a :Tuple = {
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Lo... | 359 |
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_co... | 329 | 0 |
lowercase : Union[str, Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def _snake_case( ) -> None:
lowercase : Tuple = input("""Enter message: """ )
lowercase : Dict = input("""Enter key [alphanumeric]: """ )
lowercase ... | 20 | import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : Tuple ... | 18 | 0 |
'''simple docstring'''
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
f... | 111 |
'''simple docstring'''
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ ( __A : Sequence[int] | None = None ) -> int:
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
_SCREAMING_SNAKE_CASE = nums[0]
for i in range(1 , ... | 111 | 1 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase__ ( __UpperCamelCase )-> np.ndarray:
UpperCamelCase ,UpperCamelCase ,UpperCamelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_989 * ... | 321 |
'''simple docstring'''
import math
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> float:
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initia... | 321 | 1 |
'''simple docstring'''
lowercase__ : Any = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''f... | 190 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowercase__ : Optional[int] = HfApi()
lowercase__ : Dict = {}
# fmt: off
lowercase__ : List[str] = torch.tensor([
-0.7515, ... | 190 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileNetV2Config',
'MobileNetV2OnnxConfig',... | 30 |
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 OptionalDependencyNo... | 244 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase ( __SCREAMING_SNAK... | 363 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...ut... | 124 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fasta... | 311 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_... | 311 | 1 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers imp... | 132 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodule... | 132 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_av... | 72 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCamelCase = logging.get_logger(__name__)
... | 188 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
snake_case : Optional[int] = logging.get_logger(__name__)
def lowerCAmelCase_ ( _snake_case : Optional[int]=None , ... | 41 |
import numpy
class _snake_case :
def __init__( self , _a , _a ):
__magic_name__ : Optional[Any] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
#... | 41 | 1 |
from __future__ import annotations
import math
def _A ( SCREAMING_SNAKE_CASE : 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 ev... | 95 |
import numpy
# List of input, output pairs
UpperCAmelCase : str = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCAmelCase : Optional[int] = (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase ... | 95 | 1 |
def lowerCamelCase__ ( UpperCamelCase__ : str ) -> list:
'''simple docstring'''
_snake_case = [0] * len(UpperCamelCase__ )
for i in range(1 , len(UpperCamelCase__ ) ):
# use last results for better performance - dynamic programmin... | 295 |
import random
def lowerCamelCase__ ( UpperCamelCase__ : int , UpperCamelCase__ : float , UpperCamelCase__ : bool = False ) -> dict:
'''simple docstring'''
_snake_case = {i: [] for i in range(UpperCamelCase__ )}
# if... | 295 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
... | 187 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :List[Any] = logging.get_logger(__name__)
lowerCAmelCase__ :Tuple = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __a ( UpperCAmelCase ):
_a : ... | 329 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase: int = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig... | 351 |
'''simple docstring'''
from math import factorial, pi
def lowerCamelCase__ ( _A , _A = 30 ):
if not isinstance(_A , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta' )
if not isinstance(_A , _A ) or accuracy <= 0:
... | 96 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class __snake_case :
'''simple docstring'''
def __init__( self : Optional[Any] ):
__snake_case: List[str] = {}
def UpperCAmelC... | 111 |
__UpperCAmelCase : int = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, A... | 111 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/mai... | 237 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> float:
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' ... | 237 | 1 |
'''simple docstring'''
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase):
'''simple docstring'''
__A : Optional[Any] = data
__A : Node | None = ... | 190 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import Ten... | 190 | 1 |
import sys
import turtle
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) ->int:
"""simple docstri... | 353 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class snake_case ( unittest.TestCase ... | 303 | 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
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ ... | 32 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowerC... | 124 | 0 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_... | 365 |
def A_ ( _UpperCAmelCase = 10**9 ):
SCREAMING_SNAKE_CASE_: List[str] = 1
SCREAMING_SNAKE_CASE_: Optional[int] = 2
SCREAMING_SNAKE_CASE_: int = 0
SCREAMING_SNAKE_CASE_: Dict = 0
SCREAMING_SNAKE_CASE_: List[str] = 0
while perimeter <= max_perime... | 127 | 0 |
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