code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__UpperCamelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__im... | 55 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environme... | 55 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 55 |
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu)... | 55 | 1 |
from collections import Counter
from timeit import timeit
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ... | 55 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : int = {
'configuration_bert... | 55 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Any = {
'configuration_xlm_ro... | 55 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 55 | 1 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from tra... | 55 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def __snake_case ( UpperCamelCase__ ):
raise NotImplementedError()
@abstractmethod
def __sna... | 55 | 1 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ... | 55 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_model... | 55 | 1 |
_SCREAMING_SNAKE_CASE : List[str] = range(2, 2_0 + 1)
_SCREAMING_SNAKE_CASE : Optional[Any] = [1_0**k for k in range(ks[-1] + 1)]
_SCREAMING_SNAKE_CASE : dict[int, dict[int, list[list[int]]]] = {}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : ... | 55 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
return 0
elif n == 2:
return 1
else:
A__ : Any = [0, 1]
... | 55 | 1 |
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
_SCREAMING_SNAKE_CASE : Optional[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class... | 55 |
_SCREAMING_SNAKE_CASE : List[str] = range(2, 2_0 + 1)
_SCREAMING_SNAKE_CASE : Optional[Any] = [1_0**k for k in range(ks[-1] + 1)]
_SCREAMING_SNAKE_CASE : dict[int, dict[int, list[list[int]]]] = {}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : ... | 55 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str = "laptop" ) -> DataFrame:
"""simple docstring"""
A__ : List[Any] = F"https://www.amazon.in/lapto... | 55 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from... | 55 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
'configuration_whisper': ['WHISPER_PRETRA... | 55 |
import numpy as np
_SCREAMING_SNAKE_CASE : Any = [
['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 UpperCamelCase__ :
'''simple docstring'''
def __init__( self... | 55 | 1 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 55 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .... | 55 | 1 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int = 10_00 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , __UpperCamelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""") | 55 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 55 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark... | 55 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 55 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca im... | 55 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_SCREAMING_SNAKE_CASE : ... | 55 | 1 |
from math import factorial
_SCREAMING_SNAKE_CASE : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
if not isinstance(__UpperCamelCase , ... | 55 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = {
'SenseTime/deformable-detr': 'https://huggi... | 55 | 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
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
# 1536-bit
5: {
'prime... | 55 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> List[Any]:
"""simple docstring"""
A__ : Optional[Any] = 0
A__ : Optional[Any] = len(__UpperCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , ... | 55 | 1 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ ( metaclass=SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
_lowerCAmelCase = ["speech"]
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ):
requires_backends(self... | 55 |
from PIL import Image
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Image , __UpperCamelCase : float ) -> Image:
"""simple docstring"""
def brightness(__UpperCamelCase : int ) -> float:
return 1_28 + level + (c - 1_28)
if not -2_5_5... | 55 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 55 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase__ :
'''simple docstring'''
_lowerCAmelCase = None
def __snake_case ( self ):
A__ : Dict = self.feature_extraction_cla... | 55 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Aut... | 55 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_SCREAMING_SNAKE_CASE : Union[str, Any] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\... | 55 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : Any = TypeVar('T')
_SCREAMING_SNAKE_CASE : Optional[int] = TypeVar('U')
class UpperCamelCase__ ( Generic[T, U] ):
'''simple d... | 700 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
# TODO Update this
_SCREAMING_SNAKE_CASE : Optional[int] = {
'... | 55 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 701 |
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 AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 0 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
... | 702 |
def SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
"""simple docstring"""
A__ : Optional[Any] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(__UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution()) | 55 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class UpperCamelCase__ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__(... | 703 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environme... | 55 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
_SCREAMING_SNAKE_CASE : List[Any] = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
"""simple docstring"""
A__ : Dict = os.path.dirn... | 704 |
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu)... | 55 | 0 |
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 ModelTesterMi... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : int = {
'configuration_bert... | 55 | 0 |
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 FlaxGenerationTeste... | 706 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 55 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str , __UpperCamelCase : str ) -> int:
"""simple docstring"""
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
raise ValueError('''String lengths must match!''' )
A__ : Dict ... | 707 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def __snake_case ( UpperCamelCase__ ):
raise NotImplementedError()
@abstractmethod
def __sna... | 55 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : int = {
'configuration_rembert': ['REMBERT_PRETRAINE... | 708 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_model... | 55 | 0 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 709 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
return 0
elif n == 2:
return 1
else:
A__ : Any = [0, 1]
... | 55 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : Optional[Any] = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
i... | 710 |
_SCREAMING_SNAKE_CASE : List[str] = range(2, 2_0 + 1)
_SCREAMING_SNAKE_CASE : Optional[Any] = [1_0**k for k in range(ks[-1] + 1)]
_SCREAMING_SNAKE_CASE : dict[int, dict[int, list[list[int]]]] = {}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : ... | 55 | 0 |
import math
_SCREAMING_SNAKE_CASE : Any = 1_0
_SCREAMING_SNAKE_CASE : List[str] = 7
_SCREAMING_SNAKE_CASE : int = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int = 20 ) -> Optional[Any]:
"""si... | 711 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from... | 55 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[str, Any] ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
A__ : str = False
for j in range(__lowerCAmel... | 712 |
import numpy as np
_SCREAMING_SNAKE_CASE : Any = [
['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 UpperCamelCase__ :
'''simple docstring'''
def __init__( self... | 55 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Dict = {
'huggingface/time-series-transformer-tourism-monthly'... | 713 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .... | 55 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
if n_term == "":
return []
A__ : List[str] = []
for temp in range(int(lowerCAmelCase__ ) ):
series.append(F"1/{temp + 1}" if se... | 714 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 55 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : str = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerCo... | 715 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 55 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Any = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
... | 716 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_SCREAMING_SNAKE_CASE : ... | 55 | 0 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[Any] , __UpperCamelCase : List[str]=False ):
"""simple docstring"""
A__ : Union[str, Any] ... | 717 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = {
'SenseTime/deformable-detr': 'https://huggi... | 55 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContext... | 718 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> List[Any]:
"""simple docstring"""
A__ : Optional[Any] = 0
A__ : Optional[Any] = len(__UpperCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , ... | 55 | 0 |
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_formatter import NumpyFormatter... | 719 |
from PIL import Image
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Image , __UpperCamelCase : float ) -> Image:
"""simple docstring"""
def brightness(__UpperCamelCase : int ) -> float:
return 1_28 + level + (c - 1_28)
if not -2_5_5... | 55 | 0 |
from collections import Counter
from timeit import timeit
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ... | 720 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase__ :
'''simple docstring'''
_lowerCAmelCase = None
def __snake_case ( self ):
A__ : Dict = self.feature_extraction_cla... | 55 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_SCREAMING_SNAKE_CASE : List[str] = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Languag... | 721 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_SCREAMING_SNAKE_CASE : Union[str, Any] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\... | 55 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int = 10**9 ) -> Union[str, Any]:
"""simple docstring"""
A__ : Union[str, Any] = 1
A__ : Optional[int] = 2
A__ : Union[str, Any] = 0
A__ : Any = 0
A__... | 700 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
# TODO Update this
_SCREAMING_SNAKE_CASE : Optional[int] = {
'... | 55 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict = 10 , __UpperCamelCase : Optional[int] = 22 ) -> int:
"""simple docstring"""
A__ : Dict = range(1 , __SCREAMING_SNAKE_CASE )
A__ : Union[str, Any] = range... | 701 |
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 AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 0 |
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 stable_softmax
if is_torch_a... | 702 |
def SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
"""simple docstring"""
A__ : Optional[Any] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(__UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution()) | 55 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : Optional[int] = {
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig"""... | 703 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environme... | 55 | 0 |
import operator as op
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] ) -> int:
"""simple docstring"""
A__ : Union[str, Any] = []
A__ : Optional[Any] = lambda __UpperCamelCase , __UpperCamelCase : int(x / y ... | 704 |
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu)... | 55 | 0 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : int = {
'configuration_bert... | 55 | 0 |
import inspect
import unittest
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __snake_case ( self ):
try:
import diffusers # noqa: F401
except ImportError:
assert False
def __snake... | 706 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 55 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : str = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBa... | 707 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def __snake_case ( UpperCamelCase__ ):
raise NotImplementedError()
@abstractmethod
def __sna... | 55 | 0 |
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
_SCREAMING_SNAKE_CASE : Dict = "\\n@misc{chen2021evaluating,\n title={Evaluating... | 708 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_model... | 55 | 0 |
import fire
from utils import calculate_rouge, save_json
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple , __UpperCamelCase : List[str] , __UpperCamelCase : Tuple=None , **__UpperCamelCase : Optional[Any] ) -> List[Any]:
"""simple... | 709 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
return 0
elif n == 2:
return 1
else:
A__ : Any = [0, 1]
... | 55 | 0 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # ... | 710 |
_SCREAMING_SNAKE_CASE : List[str] = range(2, 2_0 + 1)
_SCREAMING_SNAKE_CASE : Optional[Any] = [1_0**k for k in range(ks[-1] + 1)]
_SCREAMING_SNAKE_CASE : dict[int, dict[int, list[list[int]]]] = {}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : ... | 55 | 0 |
import numpy as np
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple , __UpperCamelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
return np.where(vector > 0 , _snake_case , (alpha * (np.exp(_snake_case ) - 1)) )... | 711 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from... | 55 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
... | 712 |
import numpy as np
_SCREAMING_SNAKE_CASE : Any = [
['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 UpperCamelCase__ :
'''simple docstring'''
def __init__( self... | 55 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> float:
"""simple docstring"""
return np.dot(__UpperCamelCase , __UpperCamelCase )
clas... | 713 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .... | 55 | 0 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] = "AAPL" ) -> Optional[Any]:
"""simple docstring"""
A__ : List[str] = F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
A__ : Union[str, Any] ... | 714 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 55 | 0 |
from ....utils import logging
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase__ ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=2048... | 715 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 55 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelin... | 716 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_SCREAMING_SNAKE_CASE : ... | 55 | 0 |
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 : Tuple = datasets.logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : List[str] = '''\
... | 717 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = {
'SenseTime/deformable-detr': 'https://huggi... | 55 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : str = {
'configuration_albert... | 718 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> List[Any]:
"""simple docstring"""
A__ : Optional[Any] = 0
A__ : Optional[Any] = len(__UpperCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , ... | 55 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipeli... | 719 |
from PIL import Image
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Image , __UpperCamelCase : float ) -> Image:
"""simple docstring"""
def brightness(__UpperCamelCase : int ) -> float:
return 1_28 + level + (c - 1_28)
if not -2_5_5... | 55 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : str = {... | 720 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase__ :
'''simple docstring'''
_lowerCAmelCase = None
def __snake_case ( self ):
A__ : Dict = self.feature_extraction_cla... | 55 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple ) -> str:
"""simple docstring"""
A__ : Optional[int] ... | 721 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_SCREAMING_SNAKE_CASE : Union[str, Any] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\... | 55 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : str = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConf... | 700 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
# TODO Update this
_SCREAMING_SNAKE_CASE : Optional[int] = {
'... | 55 | 0 |
from string import ascii_uppercase
_SCREAMING_SNAKE_CASE : List[Any] = {str(ord(c) - 5_5): c for c in ascii_uppercase}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple , __UpperCamelCase : Tuple ) -> str:
"""simple docstring"""
if is... | 701 |
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 AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int = 50 ) -> Tuple:
"""simple docstring"""
A__ : Dict = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):... | 702 |
def SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
"""simple docstring"""
A__ : Optional[Any] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(__UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution()) | 55 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get... | 703 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environme... | 55 | 0 |
from manim import *
class UpperCamelCase__ ( _UpperCAmelCase ):
'''simple docstring'''
def __snake_case ( self ):
A__ : Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
A__ : str = Rectangle(height=0.4_6 , width=0.4_6 )... | 704 |
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu)... | 55 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int | float | str , __UpperCamelCase : int | float | str ) -> Dict:
"""simple docstring"""
if nth_term == "":
return [""]
A__ = int(a_ )
A_... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : int = {
'configuration_bert... | 55 | 0 |
from __future__ import annotations
_SCREAMING_SNAKE_CASE : Any = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
""... | 706 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 55 | 0 |
from math import loga
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[Any] ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(lowercase_ , lowercase_ ):
... | 707 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def __snake_case ( UpperCamelCase__ ):
raise NotImplementedError()
@abstractmethod
def __sna... | 55 | 0 |
from __future__ import annotations
_SCREAMING_SNAKE_CASE : Tuple = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class UpperCamelCase__ :
'''simple docstring'''
def _... | 708 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_model... | 55 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
... | 709 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
return 0
elif n == 2:
return 1
else:
A__ : Any = [0, 1]
... | 55 | 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 applicab... | 710 |
_SCREAMING_SNAKE_CASE : List[str] = range(2, 2_0 + 1)
_SCREAMING_SNAKE_CASE : Optional[Any] = [1_0**k for k in range(ks[-1] + 1)]
_SCREAMING_SNAKE_CASE : dict[int, dict[int, list[list[int]]]] = {}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : ... | 55 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_SCREAMING_SNAKE_CASE : List[Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_SCREAMING_SNAKE_CASE : Tuple = typing.Union[np.floataa, int, flo... | 711 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from... | 55 | 0 |
from collections import defaultdict
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
A__ : List[Any] = 1
A__ : int = True
for v in tree[start]:
if v not in visited:
... | 712 |
import numpy as np
_SCREAMING_SNAKE_CASE : Any = [
['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 UpperCamelCase__ :
'''simple docstring'''
def __init__( self... | 55 | 0 |
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class UpperCamelCase__ ( __lowercase ):
'''simple d... | 713 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .... | 55 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
_SCREAMING_SNAKE_CASE : int = TypeVar('T')
class UpperCamelCase__ ( Generic[T] ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ ... | 714 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 55 | 0 |
_SCREAMING_SNAKE_CASE : str = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[Any] , __UpperCamelCase :... | 715 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 55 | 0 |
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()
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE ... | 716 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_SCREAMING_SNAKE_CASE : ... | 55 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
A__ : List[str] = 0
for ch in input_str:
A__ : Any = ord(_lowercase )
A__ : List[str] = pow(2 , _lowercase ... | 717 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = {
'SenseTime/deformable-detr': 'https://huggi... | 55 | 0 |
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ ):
A__ : Optional[Any] = val
A__ : Any = None
A__ : Optional[Any] = None
def __snake_case ( self , UpperCamelCase__ ):
... | 718 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> List[Any]:
"""simple docstring"""
A__ : Optional[Any] = 0
A__ : Optional[Any] = len(__UpperCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , ... | 55 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ) -> Any:
"""simple docstring"""
A__ : Any = HfArgumentParser(lowercase_ )
A__ : List[Any] = parser.parse_args_into_datacla... | 719 |
from PIL import Image
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Image , __UpperCamelCase : float ) -> Image:
"""simple docstring"""
def brightness(__UpperCamelCase : int ) -> float:
return 1_28 + level + (c - 1_28)
if not -2_5_5... | 55 | 0 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM im... | 720 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase__ :
'''simple docstring'''
_lowerCAmelCase = None
def __snake_case ( self ):
A__ : Dict = self.feature_extraction_cla... | 55 | 0 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
_SCREAMING_SNAKE_CASE : List[str] = None
try:
import msvcrt
except ImportError:
_SCREAMING_SNAKE_CASE : Optional[Any] = None
try:
... | 721 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_SCREAMING_SNAKE_CASE : Union[str, Any] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\... | 55 | 0 |
import re
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> Union[str, Any]:
"""simple docstring"""
if len(re.findall('''[ATCG]''' , __UpperCamelCase ) ) != len(__UpperCamelCase ):
raise ValueError('''Invalid Strand''' )
... | 700 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
# TODO Update this
_SCREAMING_SNAKE_CASE : Optional[int] = {
'... | 55 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : Union[str, Any] = TypeVar('T')
class UpperCamelCase__ ( Generic[T] ):
_lowerCAmelCase = 42 # Cache store of keys
_lowerCAmelCase ... | 701 |
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 AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[int] ) -> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
A__ : Any = gray_code_sequenc... | 702 |
def SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
"""simple docstring"""
A__ : Optional[Any] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(__UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution()) | 55 | 0 |
from collections import defaultdict
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int = 1_00_00_00 , __UpperCamelCase : int = 10 ):
"""simple docstring"""
A__ : defaultdict = defaultdict(__lowercase )
for out... | 703 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environme... | 55 | 0 |
import re
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] ) -> Tuple:
"""simple docstring"""
if len(re.findall('''[ATCG]''' , __snake_case ) ) != len(__snake_case ):
raise ValueError('''Invalid Strand''' )
return dna... | 704 |
from numpy import exp, pi, sqrt
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu)... | 55 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Any = {
'''vocab_file''': '''vo... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : int = {
'configuration_bert... | 55 | 0 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class UpperCamelCase__ ( ctypes.Structure ):
'''simple docstring'''
_lowerCAmelCase = [('''size''... | 706 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 55 | 0 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def SCREA... | 707 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def __snake_case ( UpperCamelCase__ ):
raise NotImplementedError()
@abstractmethod
def __sna... | 55 | 0 |
from collections import defaultdict
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str , __UpperCamelCase : str ) -> bool:
"""simple docstring"""
A__ : Tuple = first_str.lower().strip()
A__ : str = second_str.lower().strip(... | 708 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_model... | 55 | 0 |
# flake8: noqa
# Lint as: python3
_SCREAMING_SNAKE_CASE : Tuple = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, ... | 709 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
return 0
elif n == 2:
return 1
else:
A__ : Any = [0, 1]
... | 55 | 0 |
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
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__... | 710 |
_SCREAMING_SNAKE_CASE : List[str] = range(2, 2_0 + 1)
_SCREAMING_SNAKE_CASE : Optional[Any] = [1_0**k for k in range(ks[-1] + 1)]
_SCREAMING_SNAKE_CASE : dict[int, dict[int, list[list[int]]]] = {}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : ... | 55 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> float:
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
A__ : Union[str, Any] = sum(__snake_case ) / len(__snak... | 711 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from... | 55 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[Any] = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config... | 712 |
import numpy as np
_SCREAMING_SNAKE_CASE : Any = [
['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 UpperCamelCase__ :
'''simple docstring'''
def __init__( self... | 55 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCamelCase__ ( __l... | 713 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .... | 55 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple , __UpperCamelCase : Optional[Any] ) -> Tuple:
"""simple docstring"""
A__ : Any = int(lowerCamelCase__ )
... | 714 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 55 | 0 |
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