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 itertools import count
def SCREAMING_SNAKE_CASE__ ( __A = 50 ) -> int:
_snake_case = [1] * min_block_length
for n in count(__A ):
fill_count_functions.append(1 )
for block_length in range(__A , n + 1 ):
for b... | 495 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class lowercase ( logging.LoggerAdapter ):
@staticmethod
def _snake_case ( lowercase ) -> Optional[Any]:
lowerCAmelCase = PartialState()
return n... | 532 | 0 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def A( snake_case_ ):
"""simple docstring"""
@wraps(snake_case_ )
def _inner_fn(*snake_case_ , **snake_case_ ):
warnings.warn(
(F"""... | 709 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def A( snake_case_ , snake_case_ , snake_case_ = 1 / sqrt(2 ) ):
"""simple docstring"""
lowercase__: Dict = tau * frequency / samplerat... | 120 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
_lowercase = logging.getLogger(__name__)
... | 91 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
cl... | 573 | 0 |
from math import pow
def UpperCAmelCase_( a__ , a__ , a__ , a__ , a__ , ):
"""simple docstring"""
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutio... | 333 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = list(a__ )
SCREAMING_SNAKE_CASE : int ... | 333 | 1 |
'''simple docstring'''
import warnings
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_ = logging.get_logger(__n... | 539 |
from scipy.stats import pearsonr
import datasets
__magic_name__ : Union[str, Any] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies ... | 615 | 0 |
"""simple docstring"""
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ):
"""simple docstring"""
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(__UpperCAmelCase ,__UpperCAmelCas... | 283 | """simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCamelCase (__lowerCamelCase ):
_snake_case = ""
_snake_case = (
None # protocol passed in p... | 283 | 1 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
lowerCAmelCase: Optional[int] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowerCAmelCase: O... | 526 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
lowerCAmelCase: Optional[int] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowerCAmelCase: O... | 526 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
... | 140 |
def lowercase ( a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE_ :set[int] = set()
return any(
node not in visited and depth_f... | 140 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils impo... | 87 |
'''simple docstring'''
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the c... | 407 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def lowerCAmelCase (__UpperCamelCase : int = 1_0_0_0_0_0_0 , __UpperCamelCase : int = 1_0 ):
"""simple docstring"""
__UpperCamelCase =defaultdict(__UpperCamelCase )
... | 296 | """simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 296 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __A :
a__ : int
a__ : TreeNode | None = None
a__ : TreeNode | None = None
SCREAMING_SNAKE_CASE_: Union[str, Any] =namedtu... | 78 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass... | 26 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
_UpperCamelCase: Tuple =logging.get_logger(__name__)
class __lowercase:
"""simple docstring"""
def... | 585 |
from __future__ import annotations
import math
class __lowercase:
"""simple docstring"""
def __init__( self : str , _lowerCAmelCase : int ) -> None:
_lowerCAmelCase = size
# approximate the overall size of segment tree with given value
... | 585 | 1 |
import numpy as np
UpperCamelCase = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class lowerCAmelCase_ :
def __init__( self ):
_lowercase : Any ... | 66 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class UpperCamelCase_ ( unittest.TestCase ):
def _snake_case ( self :Tuple ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [
"""safet... | 6 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
A : Dict = TypeVar('''T''')
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
return (position - 1) // 2
... | 721 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
A : Optional[Any] = logging.getLogger(__name__)
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring... | 247 | 0 |
'''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
#
#... | 588 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRoberta... | 588 | 1 |
import requests
_A = 'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : str ) -> None:
"""simple docstring"""
a_ = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in t... | 700 |
from typing import Any
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : list ) -> list[Any]:
"""simple docstring"""
if not input_list:
return []
a_ = [input_list.count(UpperCamelCase ) for value in input_list]
a_ = max(UpperCamelCase ) # Gets the maximum count in the inpu... | 403 | 0 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 63 | import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ):
_UpperCamelCase = []
def UpperCamelCase_ ( self : Any , _A : str ):
return self.node_position[verte... | 10 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 420 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas... | 420 | 1 |
def __a ( ) -> Any:
"""simple docstring"""
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def __a ( __UpperCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ : Optional[A... | 488 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __a ( __UpperCAmelCase : Optiona... | 488 | 1 |
import colorsys
from PIL import Image # type: ignore
def lowerCAmelCase ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : int ):
"""simple docstring"""
UpperCAmelCase__ = x
UpperCAmelCase__ = y
f... | 364 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowerCAmelCase : Optional[int] = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_t... | 364 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"JukeboxPriorConfig",
"JukeboxVQVAEConfig",
... | 66 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowercase : Tuple = """src/transformers"""
# This is to make sure the t... | 210 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, 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_modelin... | 700 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2FormerConf... | 454 | 0 |
def lowercase ( __A : int = 200_0000 ) -> int:
'''simple docstring'''
snake_case : List[str] = [0 for i in range(n + 1 )]
snake_case : Optional[Any] = 1
snake_case : Tuple = 1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__magic_name__ = ... | 155 | 0 |
"""simple docstring"""
class _lowerCAmelCase :
def __init__( self : List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase = {}
def _lowerCAmelCase ( self : List[str] ) -> None:
"""si... | 396 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvis... | 396 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap im... | 104 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise ... | 486 | 0 |
"""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
#
# ... | 48 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs... | 48 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowerCamelCase : str = pd.read_csv("""sample_data.csv""", header=None)
_lowerCamelCa... | 87 | import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_c... | 558 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _snake_case( SCREAMING_SNAKE_CASE__ : Dict ) -> List[Any]:
'''simple docstring'''
... | 586 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
"tokenization_m2m_100": ["M2M1... | 586 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
UpperCamelCase = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 1_000,
"blo... | 66 | '''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class a__ :
@property
def SCREAMING_SNAKE_CASE__ ... | 546 | 0 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/micros... | 703 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import s... | 352 | 0 |
import math
from collections.abc import Iterator
from itertools import takewhile
def _a ( lowercase__ : 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:
... | 85 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokeni... | 392 | 0 |
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : List[Any] = """facebook/bart-large-mnli"""
a : str = (
"""This is a too... | 707 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
fro... | 39 | 0 |
from __future__ import annotations
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = 0.0_0
lowerCAmelCase_ = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase_ = f"Resistor at index {index} has a negative or zero value!"... | 431 |
_A = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
lowerCAmelCase_ = Stack()
lowerCAmelCase_ = Stack()
... | 431 | 1 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
UpperCamelCase =[
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell p... | 701 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_c... | 543 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class _lowerCAmelCase :
__UpperCAmelCase : str = field(
... | 178 |
"""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 requi... | 178 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
def _lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : int ):
lowercase__ : int = Path(lowerCamelCase__ )
lowercase__ : Optional[Any] = Path(... | 715 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datase... | 128 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(__name__)
class a ( a__ ):
SCREAMING_SNAKE_CASE__ : List[Any] = '''encoder-decoder'''
SCREAMING_SNAKE_CASE__ : ... | 202 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ : Any = logging.get_logger(__n... | 223 | 0 |
import requests
lowerCAmelCase__ = '''YOUR API KEY'''
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ = giphy_api_key ):
"""simple docstring"""
lowercase__ : Union[str, Any] = "+".join(query.split() )
lowercase__ : List[str] = ... | 717 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_t... | 81 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A__ ( UpperCamelCase ):
... | 494 | '''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when... | 494 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
... | 700 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> None:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase : ... | 94 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowercase )
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
_snake_case : ... | 45 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Dict = [0 for i in range(r + 1 )]
# nc0 = 1
A_ : Tuple = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
A_ : str = mi... | 590 | 0 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 525 | from ... import PretrainedConfig
SCREAMING_SNAKE_CASE : Any = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class A_ ( a_ ):
_SCREAMING_SNAKE_CASE = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
_SCREAMING_SNAKE_CASE ... | 525 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowerCAmelCase__ ) , """Tatoeb... | 393 |
'''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
lowercase_ = {
# 1536-bit
5: {
"prime": int... | 11 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/con... | 703 | UpperCAmelCase_ = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cook... | 476 | 0 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from... | 71 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import ... | 685 | 0 |
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
snake_case_ = namedtuple(
"""_TestCommandArgs""",
... | 537 |
'''simple docstring'''
def _lowerCamelCase( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str ) -> Optional[int]:
A : Optional[int] = 0
A : str = len(UpperCamelCase__ ) - 1
while left <= right:
# avoid divi... | 537 | 1 |
from __future__ import annotations
import time
import numpy as np
__A : Any = [8, 5, 9, 7]
__A : Tuple = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__A : Optional[Any] = [
[3, 2, 1,... | 27 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCAmelCase( ... | 27 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 279 |
import functools
def lowerCamelCase__ ( __lowerCAmelCase : str , __lowerCAmelCase : str ):
"""simple docstring"""
lowerCAmelCase_ = len(__lowerCAmelCase )
lowerCAmelCase_ = len(__lowerCAmelCase )
@functools.ca... | 279 | 1 |
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
lowerCAmelCase_ =... | 326 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE_ ):
snake_case__ = 42
snake_case__ = 42
def _UpperCAmelCase ( __A : str ):
... | 466 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : str = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCT... | 708 |
"""simple docstring"""
from statistics import mean, stdev
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = 3 ) -> list:
"""simple docstring"""
A = min(UpperCamelCase__ )
A = max(UpperCamelCase__ )
# normalize data
return [round((x - x_min)... | 91 | 0 |
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_available():
im... | 520 | 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 onnxruntime as ort
Upp... | 520 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_c... | 701 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'scipy']
def __init__( self: Tuple , *_SCREAMING_SNAKE_C... | 615 | 0 |
import math
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> list[int]:
_lowercase : int = []
_lowercase : Optional[Any] = 2
_lowercase : Union[str, Any] = int(math.sqrt(SCREAMING_SNAKE_CASE ) ) # Size of every seg... | 66 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_lo... | 569 | 0 |
'''simple docstring'''
def snake_case (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
def get_matched_characters(UpperCamelCase : str , UpperCamelCase : str ) -> str:
lowerCamelCase__ = []
lowerCamelCase__ = m... | 705 |
import math
def snake_case (UpperCamelCase : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# ... | 235 | 0 |
import operator
def UpperCamelCase ( _A : str , _A : int = False , _A : Union[str, Any] = None )-> Union[str, Any]:
"""simple docstring"""
A__ = operator.lt if reverse else operator.gt
A__ = solution or []
if not arr:
ret... | 491 |
"""simple docstring"""
from collections import UserDict
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_avai... | 34 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase : int = {"processing_layoutx... | 718 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ = None , snake_case__ = None ) -> None:
if start is None:
lowerCamelCase = 0
if end is None:
lowerCamelCase = len(snake_case__ ) - 1
... | 533 | 0 |
'''simple docstring'''
from math import sqrt
def A (__lowerCamelCase :str ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 5 | """simple docstring"""
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLO... | 434 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
snake_case_ : int = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author=... | 707 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __snake_case :
def __init__( self : Dict , _snake_case : Optional[int] , _snake_case : int , _snake_case : int):
"""simple docstring"""
if ds... | 169 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCamelCase__ ( __lowerCamelCase ):
def __lowercase( self : int, __lowerCamelCase : str ) -> Any:
with open(__lowerCamelCase, encoding='''utf-8... | 344 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 344 | 1 |
from functools import reduce
a_ : Optional[int] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'668966489... | 711 |
def _SCREAMING_SNAKE_CASE ( ):
__magic_name__ = []
__magic_name__ = 1
while len(snake_case_ ) < 1E6:
constant.append(str(snake_case_ ) )
i += 1
__magic_name__ = ''''''.join(snake_case_ )
return (
int(constant[0] )
* int... | 678 | 0 |
from __future__ import annotations
from collections.abc import Generator
def snake_case__ ( ):
lowerCAmelCase__ :Union[str, Any] = {}
lowerCAmelCase__ :Optional[Any] = 2
while True:
lowerCAmelCase__ :Union[str, Any] = factor_map.pop(lowercase__ ... | 145 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : int ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
__lowercase =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__... | 119 | 0 |
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))
| 715 |
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
... | 32 | 0 |
"""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_ARCH... | 77 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCamelCase : Optional[int] = {
"configuration_vision_text_dual_encoder": ["Visi... | 284 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[int]:
lowerCAmelCase__ : Union[str, Any] = {
"""en""": """Machine learning is great, isn't ... | 709 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import... | 507 | 0 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 21 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,... | 649 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch... | 524 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# Th... | 524 | 1 |
"""simple docstring"""
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... | 299 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if ... | 296 | 0 |
'''simple docstring'''
snake_case : Tuple = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def lowercase__ ( __UpperCamelCase : int ):
'''simple docstring'''
__lowercase = 0
while number:
# Increased Speed Slightly by ... | 339 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase : int = 50000000 ):
'''simple docstring'''
__lowercase = set()
__lowercase = int((limit - 24) ** (1 / 2) )
__lowercase = set(range(3 , prime_square_limit + 1 , 2... | 339 | 1 |
from __future__ import annotations
import math
def a_ ( __magic_name__ , __magic_name__ ) -> Dict:
"""simple docstring"""
if len(_snake_case ) != 2 or len(a[0] ) != 2 or len(_snake_case ) != 2 or len(b[0] ) != 2:
raise Exception('''Mat... | 598 |
'''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
SCREAMING_SNAKE_CASE__ = {1: (1, 1), 2: (2,... | 267 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__: Tuple = {
'''configuration_distilbert''': [
'''DISTILB... | 720 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
fro... | 221 | 0 |
'''simple docstring'''
def A (__lowerCamelCase :int = 100 ):
_lowerCAmelCase = 0
_lowerCAmelCase = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__mai... | 5 |
"""simple docstring"""
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class lowercase ( lowercase__ ,unittest.TestCase ):
lowercase = DownBlockaD # noqa F40... | 535 | 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 (
SegformerConfig,
SegformerForImageClassification,
Segformer... | 521 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify... | 521 | 1 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCAmelCase_ :
def __init__( self ,snake_case__=2 ,snake_case__=3 ,snake_case__=64 ,snake_case__=None ):
SCREAMING_SNAKE... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAMING_SNAKE_CASE ):
if dist[i][j] != float('inf' ):
... | 707 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class a :
UpperCamelCase : int
UpperCamelCase : int
class a :
def __init__( ... | 254 | 0 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
_lowerCAmelCase : Tuple = 1.0_5457_1817e-34 # unit of ℏ : J * s
_lowerCAmelCase : int = 3e8 # unit of c : m * s^-1
... | 246 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowerCAmelCase : List[Any] = '\\n@misc{chen2021evaluating,\n title... | 246 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Optional[int] = logging.get_logger(__name__)
A_ : Optional[Any] = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https... | 716 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transforme... | 419 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __a ( __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
lowerCamelCase_ ... | 488 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case_ ( __A ):
'''simple docstring'''
... | 488 | 1 |
"""simple docstring"""
from __future__ import annotations
A = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
class ... | 701 |
"""simple docstring"""
from math import loga
def UpperCamelCase_ ( lowerCamelCase : int ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(lowerCamelCase , lo... | 147 | 0 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallb... | 71 |
'''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_... | 71 | 1 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowerCAmelCase = [
# tf -> hf
("""/""", """."""),
("""layer... | 701 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"""vocab_file""": """vocab.j... | 16 | 0 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbo... | 575 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 685 | 0 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__lowerCAmelCase : Any =logging.getLogger(__name__)
class _A :
def _... | 197 | """simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( lowerCAmelCase ):
snake_case__ : Union[str, Any] = (IPNDMScheduler,)
snake_case__ : List[... | 197 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase (_lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and ... | 331 |
'''simple docstring'''
import json
import sys
def _lowerCAmelCase (_lowercase , _lowercase ):
"""simple docstring"""
with open(_lowercase , encoding="utf-8" ) as f:
a__ = json.load(_lowercase )
a__ = ["<deta... | 331 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _snake_case ( __snake_case , __snake_case = True , __snake_case = math.inf , __snake_case = -math.inf , __snake_case = math.inf , __snake_case = -math.in... | 700 | from sklearn.metrics import mean_squared_error
import datasets
_lowerCAmelCase = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P... | 71 | 0 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__snake_case =argparse.ArgumentParser()
parser.add_argument(... | 133 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__snake_case =Lock()
def a_ ( lowerCamelCase : int , lowerCamelCase : List[str] , lowerCamel... | 133 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NA... | 716 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert i... | 106 | 0 |
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 import Conversation
_lo... | 113 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class a ( UpperCAmelCase__ ):
def __init__( ... | 409 | 0 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def A ( A_ : Callable ):
@wraps(A_ )
def _inner_fn(*A_ : List[Any] , **A_ : Union[str, Any] ):
warnings.warn(
... | 555 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version... | 555 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}
class _UpperCamelCase ( ... | 25 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers... | 329 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__lowerCamelCase : str = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDepen... | 707 |
'''simple docstring'''
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
lowerCamelCase_ : Union[str, Any] = sorted(string.lower() ... | 418 | 0 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatu... | 412 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiua... | 18 | 0 |
def lowercase_ (A : int , A : int ):
return 1 if input_a == input_a else 0
def lowercase_ ():
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if _... | 243 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines... | 243 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Optional[Any] = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
... | 152 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_snake_case : int = logging.get_logger(__name__)
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[int] , ... | 81 | 0 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttenti... | 653 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
lowercase = HfArgumentParser(lowercase_ )
lowercase = parser.parse_args_into_dataclasses()[0]
lowerca... | 653 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __SCREAMING_SNAKE_CASE (SCREAMING_SNA... | 39 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase__ : List[str] = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase... | 407 | 0 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoa... | 708 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from tran... | 17 | 0 |
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(... | 105 |
"""simple docstring"""
def lowercase__ ( lowerCamelCase : list , lowerCamelCase : int = 0 ) -> list:
lowerCAmelCase__ : List[Any] = length or len(lowerCamelCase )
lowerCAmelCase__ : Optional[Any] = False
for i in... | 308 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 274 |
"""simple docstring"""
def UpperCAmelCase__ ( ) -> int:
"""simple docstring"""
return 1
def UpperCAmelCase__ ( A__ ) -> int:
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCAmelCase__ ( A__ ) -> ... | 274 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __lowercase ( snake_case = 3 ):
"""simple docstring"""
if isinstance(snake_case, snake_case ):
raise TypeError('''number of qubits must... | 0 |
import string
def lowerCamelCase_ ( lowerCAmelCase: str )-> str:
_snake_case : str = ''
for i in sequence:
_snake_case : Tuple = ord(lowerCAmelCase )
if 65 <= extract <= 90:
output += chr(1_55 - extract )
elif 97 <= extract <= 1... | 411 | 0 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCAmelCase__ ( lowerCamelCase__ ) -> List[str]:
return getitem, k
def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCas... | 703 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-large': 'https://huggingface.co/google/fnet-... | 109 | 0 |
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