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 |
|---|---|---|---|---|
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_keras_nlp, require_tf, s... | 314 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
a__: List[Any] = logging.getLogger()... | 193 | 0 |
"""simple docstring"""
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_lowerCAmelCase :Tuple = logging.get_logger(... | 68 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from .... | 68 | 1 |
'''simple docstring'''
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 Tokeniz... | 53 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
a__ : Any ... | 53 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
_lowerCamelCase : Union[str, Any] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def a_ ( ) -> List[Any]:
_snake_case = os.path.dirname(os.path.realpath(__lowercase ) )
_snake_case = ... | 130 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
Auto... | 130 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : int = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/r... | 54 |
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 90 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
if not (isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and isinstance(lowerCAmelCase__ , lowerCAmelCase__ )):
raise ValueError("longest_common_substring() ... | 241 |
"""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 = {
... | 241 | 1 |
"""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 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
... | 165 | 1 |
"""simple docstring"""
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 _UpperCAmelC... | 355 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : float , __lowerCamelCase : float ) -> float:
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(__lowerCamelCase ) * abs(__lowerCamelCase )
if __name__ ==... | 40 | 0 |
"""simple docstring"""
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,... | 44 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvai... | 11 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/c... | 366 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCamelCase_ :
def __init__( self : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : MutableSequence[float] ) -> None:
if len(low... | 253 | 0 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
SCREAMING_SNAKE_CASE : Union[str, Any] = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union... | 102 |
"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_... | 263 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase : Any = ge... | 357 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int = logging.get_logger(__name__)
_lowercase : Optional[Any] = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json... | 91 | 0 |
# 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
#
# Unless required by applic... | 262 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from... | 262 | 1 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
f... | 358 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
... | 36 | 0 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowerCAmelCase__ = TypeVar("""T""")
lowerCAmelCase__ = Union[List[T], Tuple[T, ...]]
lowerCAmelCase__ = Union[T, List[T], Dict[str, T]]
lowerCAmelCase__ = Union[str, bytes, os.PathLike]
| 68 |
import copy
import random
from transformers import CLIPTokenizer
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowercase , **lowe... | 68 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def snake_case_ (UpperCamelCase : Tuple ):
'''simple docstring'''
return choice(UpperCamelCase )
def snake_case_ (UpperCamelCase : list[int] , UpperCamelCase : in... | 179 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
_snake_case : Any = log... | 179 | 1 |
import functools
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ) or not all(isinstance(lowerCamelCase__ , lowerCamelCase__ ) for day in days ):
raise ValueErro... | 130 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerC... | 130 | 1 |
"""simple docstring"""
from itertools import product
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> list[int]:
__a = sides_number
__a = max_face_number * dice_number
__a = [0] * (max_total + 1)
_... | 11 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConf... | 11 | 1 |
"""simple docstring"""
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... | 241 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 241 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__ ( metaclass=__lowercase ):
a__ : Union[str, Any] = ["""sentencepiece"""]
def __init__( self : str , *SCREAMING_SNAKE_CASE__ : int , **SCREAMING_SNAKE_CASE__ : ... | 339 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers impo... | 339 | 1 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
SCREAMING_SNAKE_CASE : int = ""
SCREAMING_SNAKE_CASE : Any = ""
# append each character + "|" in new_string for range... | 76 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowercase = datasets.utils.logging.get_logger(__name__)
... | 40 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : Any = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokeniz... | 134 |
from ..utils import DummyObject, requires_backends
class a (metaclass=_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : int = ["speech"]
def __init__( self : List[Any] , *lowerCamelCase : List[Any] , **lowerCamelCase : Optional[Any] ... | 134 | 1 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
snake_case_... | 56 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(UpperCAmelCa... | 243 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 303 |
"""simple docstring"""
import os
import numpy
import onnx
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->List[str]:
"""simple docstring"""
a_ = a.name
a_ = b.name
a_ = ""
a_ = ""
a_ = a == b
a_ = name_a
a_ = n... | 303 | 1 |
def A ( a_ ) -> list:
if n_term == "":
return []
__UpperCamelCase : list =[]
for temp in range(int(a_ ) ):
series.append(F'1/{temp + 1}' if series else '1' )
return series
if __name__ == "__main__... | 71 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ ... | 91 | 0 |
from collections import defaultdict
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> bool:
'''simple docstring'''
lowerCAmelCase : Any = first_str.lower().strip()
lowerCAmelCase : List[str] = second_str.lower().strip()
# Rem... | 356 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) ... | 323 | 0 |
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 lowerCamelCase_ ( ... | 90 |
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_to_model_forward
from transformers.models.rob... | 36 | 0 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import lo... | 25 |
'''simple docstring'''
def lowercase (_A = 1_0_0_0_0_0_0 ):
"""simple docstring"""
_lowerCAmelCase : Any = set(range(3 , _A , 2 ) )
primes.add(2 )
for p in range(3 , _A , 2 ):
... | 25 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/nat-mini-in1k-224""": """https://huggingfac... | 179 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->int:
'''simple docstring'''
assert (
isinstance(snake_case_ ,snake_case_ ) and number_of_steps > 0
), F"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if nu... | 179 | 1 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
lowercase_ = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For b... | 11 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {
"configuration_vision_text_dual_encoder": ["VisionTextDual... | 11 | 1 |
from itertools import product
def _UpperCAmelCase (UpperCamelCase__ : int , UpperCamelCase__ : int ):
_A : Dict = sides_number
_A : Any = max_face_number * dice_number
_A : Optional[int] = [0] * (max_total + ... | 11 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowerCAmelCase__ = object()
# For specifying empty leaf dict `{}`
lowerCAmelCase__ = object()
def _UpperCAmelCase (Upp... | 11 | 1 |
"""simple docstring"""
import math
import qiskit
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1 ,_lowerCamelCase : int = 1 ,_lowerCamelCase : int = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowerCamelCase ,_lowerCamelCase )
... | 126 | """simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_a : List[Any] = logging.get_logger(__name__)
class __A :
_UpperCamelCase : str = None
@experimental
def ... | 126 | 1 |
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 8 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax... | 242 | 0 |
import gc
import threading
import time
import psutil
import torch
class _snake_case :
def __init__( self ):
a :Tuple = psutil.Process()
a :str = False
def SCREAMING_SNAKE_CASE__ ( self ):
a :Union[str, Any] = -1
whi... | 357 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 100 ):
"""simple docstring"""
a :List[Any] = 0
a :List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_int... | 281 | 0 |
'''simple docstring'''
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 .toke... | 134 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _... | 134 | 1 |
'''simple docstring'''
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def lowerCamelCase__ ( _A ):
a : List[str] = [
'decoder.version',
'decoder.output_projection.weight',
... | 96 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random... | 96 | 1 |
import math
import sys
def _lowerCamelCase( lowercase__ ) -> int:
'''simple docstring'''
if number != int(lowercase__ ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the value of input must not be a negative number' )
if ... | 295 |
from __future__ import annotations
def _lowerCamelCase( lowercase__ ) -> list[int]:
'''simple docstring'''
__lowercase= 2
__lowercase= []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(lowercase__ )
if n > 1:
factors.append(lower... | 295 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import... | 370 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCAmelCase__ ( ):
snake_case_ : str = ArgumentParser(
description=(
"PyTorch TPU distributed training l... | 36 | 0 |
"""simple docstring"""
import string
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
_snake_case : Union[str, Any] = """"""
for symbol in message:
if symbol in st... | 64 |
from itertools import permutations
def snake_case_ ( lowerCAmelCase_ : tuple ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
__lowercase : ... | 233 | 0 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
a_ : int = {
# 1536-bit
5: {
"""prime"""... | 6 |
'''simple docstring'''
a_ : List[Any] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def a_ ( __snake_case : int ) -> int:
"""simple docstring"""
lowerCamelCase_ =0
while number:
# I... | 6 | 1 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowercase_ ( _snake_case ):
... | 25 |
"""simple docstring"""
def lowercase_ ( _snake_case ,_snake_case ):
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 ... | 25 | 1 |
def A (__A : int = 1000000 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = [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 ... | 362 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
snake_case_ : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
snake_case_ : str = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __snake_case :
UpperCAmelCa... | 7 | 0 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase__ = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segme... | 11 |
from __future__ import annotations
def _UpperCAmelCase (UpperCamelCase__ : list[int] , UpperCamelCase__ : list[int] , UpperCamelCase__ : int ):
_A : Dict = list(range(len(UpperCamelCase__ ) ) )
_A : Any =... | 11 | 1 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
_a = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
_a = '\nArgs:\n predictions (`list` of ... | 144 |
"""simple docstring"""
import argparse
import os
import re
_a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+Order... | 144 | 1 |
"""simple docstring"""
# 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
lowerCAmelCase = TypeVar("""T""")
class A_ ( Generic[T] ):
... | 126 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : str , snake_case_ : str ,... | 126 | 1 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 50 | import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transfo... | 50 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase : Dict ={
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",... | 237 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase :
def __init__( self :Optional[int] , lowercase_ :int )-> None:
A__ = order
# a_{0} ... a_{k}
A__ = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A__ = [... | 237 | 1 |
"""simple docstring"""
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ... | 358 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 303 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {
"""configuration_funnel""": ["""FUNNEL_... | 96 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMA... | 96 | 1 |
"""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://huggingfa... | 291 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
... | 291 | 1 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowercase : Dict = "__DUMMY_TRANSFORMERS_USER__"
lowercase : Union[str, Any] = "Dummy User"
lo... | 42 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
... | 36 | 0 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __a ( lowerCAmelCase_ : Optional[int] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_= [
"""decoder.version"... | 277 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = '''https://openaipublic.azureedge.... | 277 | 1 |
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
A : Any = {
# 1536-bit
5: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA... | 6 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a ... | 6 | 1 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__A = logging.getLogger(__name... | 348 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils i... | 348 | 1 |
_lowerCAmelCase : List[str] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.6_0_9_3_4_4,
"knot": 1.8_5_2,
}
_lowerCAmelCase : Optional[Any] = {
"km/h": 1.0,
"m/s": 0.2_7_7_7_7_7_7_7_8,
"mph": 0.6_2_1_3_7_1_1_9_2,
"knot": 0.5_3_9_9_5_6_8_0_3,
}
def lowerCA... | 169 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir("fixtures/spiece.model")
... | 7 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Dict = logging.get_logger(__name__)
_a : str = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class __A ( S... | 126 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : str ,_lowerCamelCase : bool = False ) -> str:
if not isinstance(_lowerCamelCase ,_lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = f"Expected string as input, found {type(_l... | 126 | 1 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
A__ : Any = True
except (ImportError, ModuleNotFoundError):
A__ : str = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', q... | 144 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import D... | 144 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCamelCase ( _A ):
SCREAMING_SNAKE_CASE = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE = "CLIPImageProcesso... | 57 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Union[str, Any] = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de... | 57 | 1 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase=() , _UpperCAmelCase=None , _UpperCAmelCas... | 50 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100_0000 ) -> int:
lowerCamelCase__ : int = limit + 1
lowerCamelCase__ : Optional[Any] = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _Upper... | 50 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMulti... | 27 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acce... | 27 | 1 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
... | 9 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def a__ ( snake_case , snake_case=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = OmegaConf.load(snake_case )
if display:
print(yaml.dump(Om... | 303 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...imag... | 228 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = ... | 228 | 1 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import Flax... | 39 |
from __future__ import annotations
import math
def a_ ( lowerCAmelCase_ : int, lowerCAmelCase_ : int, lowerCAmelCase_ : bool, lowerCAmelCase_ : list[int], lowerCAmelCase_ : float ):
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if len(lowerC... | 284 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamel... | 359 |
import math
def _SCREAMING_SNAKE_CASE ( lowercase : float , lowercase : float ):
'''simple docstring'''
return math.pow(lowercase , 2 ) - a
def _SCREAMING_SNAKE_CASE ( lowercase : float ):
''... | 208 | 0 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowercase_ (A : List[str] ):
snake_case_... | 277 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 ConfigTe... | 277 | 1 |
from __future__ import annotations
def snake_case (A_ :Dict , A_ :Optional[int] ):
'''simple docstring'''
a, a : str = position
a : str = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
... | 361 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCamelCase : Optional[int] = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_ra... | 186 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__snake_case = logging.getLogger(__name__)
... | 348 | from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 348 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Dict = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtract... | 353 | '''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils im... | 345 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFI... | 126 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import... | 126 | 1 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelFo... | 243 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a__ : List[Any] = ... | 243 | 1 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
'''simple docstring'''
__lowerCAmelCase = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ... | 57 |
"""simple docstring"""
import numpy
# List of input, output pairs
A : Any = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
A : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A : Union[str, ... | 57 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import Au... | 362 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowerCamelCase : Dict = [
# tf -> hf
("/", "."),
("la... | 249 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_c... | 27 |
'''simple docstring'''
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from a... | 27 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require... | 30 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase__ = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_M... | 30 | 1 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerat... | 228 |
from __future__ import annotations
from collections.abc import Iterator
class __lowerCAmelCase :
def __init__( self :Optional[Any] , __magic_name__ :int ):
'''simple docstring'''
a = value
a ... | 228 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Any = R"\n Args:\n input_ids (`torch.LongTensor... | 159 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _UpperCAmelCase (UpperCamelCase_ : Sequence[float] , UpperCamelCase_ : int , UpperCamelCase_ : int ):
'''simple docstring'''
... | 159 | 1 |
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : int ):
_UpperCAmelCase : Dict = [1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = 0, 0, 0
_UpperCAmelCase : Union[str, Any] = ugly_nu... | 263 |
import warnings
warnings.warn(
'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
'`from accelerate import find_executable_batch_size` to avoid this warning.',
FutureWarning,
)
| 207 | 0 |
def __UpperCAmelCase ( __a : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
_a : List[Any] = [0] * len(__a )
_a : int = []
_a : int = [1] * len(__a )
for values in graph.value... | 15 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
a__ = '''\
'''
a__ = '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is de... | 15 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
fr... | 29 |
from typing import Dict
from .base import GenericTensor, Pipeline
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def _snake_case ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=... | 186 | 0 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Union[str, Any] = '''EncodecFeatureExtractor'''
_UpperCAmelCase : Dict ... | 113 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> bool:
if len(lowerCamelCase__ ) == 0:
return False
__lowerCamelCase : List[Any] = len(lowerCamelCase__ ) // 2
if a_list[midpoint] == item:
retu... | 113 | 1 |
def _A ( SCREAMING_SNAKE_CASE : List[Any] = 100 ):
"""simple docstring"""
a__ : str =n * (n + 1) * (2 * n + 1) / 6
a__ : int =(n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
... | 95 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
... | 296 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A__ ( __magic_name__ ):
lower... | 365 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PR... | 307 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagema... | 243 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase ( UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
def is_in_circle(UpperCAmelCase , UpperCAmelCase ) -> bool:
a_... | 243 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
UpperCAmelCase : str = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\n... | 331 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
UpperCAmelCase : List[str] = datasets.utils.logging.get_logger(__name__)
@... | 331 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import ... | 22 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
a_ = '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbo... | 249 | 0 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
'''simple docstring'''
... | 148 |
from __future__ import annotations
import math
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if num <= 0:
a__ : List[str] =f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(SCREAMING_SNAKE_CASE )
... | 148 | 1 |
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern t... | 30 |
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and... | 30 | 1 |
"""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
lowerCamelCase__ ... | 310 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
_UpperCamelCase : Optional[Any] = ta... | 310 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : str ) -> int:
"""simple docstring"""
snake_case_ = {}
... | 159 |
def _lowerCAmelCase ( lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :Tuple , lowerCAmelCase_ :Any )->List[Any]:
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(lowerCAmelCase_ , ... | 159 | 1 |
'''simple docstring'''
import re
def snake_case_ ( lowerCAmelCase_ )-> list:
'''simple docstring'''
return [char.split() for char in re.split(R"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
... | 354 |
'''simple docstring'''
from datetime import datetime
import requests
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
_Upp... | 349 | 0 |
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = [0] * len(a_ )
__A = []
__A = [1] * len(a_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(a_ ) ):
... | 15 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWith... | 15 | 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__)
... | 366 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( lowerCAmelCase_ ) -> str:
return "".join(sorted(lowerCAmelCase_ ) )
def __lowerCamelCase ( lowerCAmelCase_ ) -> list[str]:
... | 107 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> bool:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError('check_bouncy() accepts only integer arguments' )
SCREAMING_SNAKE_CASE = str(SC... | 113 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is... | 113 | 1 |
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_modeling_common import ... | 366 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
Upper... | 143 | 0 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_A = "▁"
_A = get_tests_dir("fix... | 231 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( __lowerCAmelCase : str = "" ):
"""simple docstring"""
lowerCAmelCase_ = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
lowerCAmelCase_ = BeautifulSo... | 231 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_a : Tuple= {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
"albert-... | 362 | """simple docstring"""
from __future__ import annotations
import math
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == ... | 95 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
snake_case_ : List[Any] = logging.... | 83 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''Ins... | 272 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_UpperCAmelCase ) == 0:
raise ValueError('Input list must be a non empt... | 45 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( lowerCamelCase_ ):
a__ : Optional[int] = (PNDMScheduler,)
a__ : str = (("""num_inference... | 148 |
"""simple docstring"""
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,... | 148 | 1 |
"""simple docstring"""
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _lowercase ( _lowerCamelCase ):
"""simple docstring"""
lowercase__ = '''facebook/bart-large-mnli'''
lowercase__ = ... | 353 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _lowercase :
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : Any ) -> Optional... | 85 | 0 |
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