code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
_lowerCAmelCase: List[str] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def _lowercase( ):
a__ =input('Enter message: ' )
a__ =input('Enter key [alphanumeric]: ' )
a__ =input('Encrypt/Decrypt [e/d]: ' )
if mode.lower().startswith('e' ):
... | 20 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test... | 84 | 0 |
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_IMAGE_GENERATION_BATCH_PARAMS, UNCONDIT... | 21 |
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_ ( __lowerCamelCase ):
'''simple docstring'''
_Upp... | 84 | 0 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
_a... | 22 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase ... | 84 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Dict = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
... | 23 |
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 ModelTesterMixin, ids_tensor
from .... | 84 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCAmelCase_ : Optional[Any] = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 24 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable... | 84 | 0 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
if length < 0 or breadth < 0 or height < 0:
raise Value... | 25 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
l... | 84 | 0 |
'''simple docstring'''
import numpy as np
import qiskit
def _a ( _lowerCamelCase = 8 , _lowerCamelCase = None ) -> str:
"""simple docstring"""
__snake_case : Any = np.random.default_rng(seed=_lowerCamelCase )
... | 26 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_struc... | 84 | 0 |
import os
import string
import sys
__A : Optional[Any] = 1 << 8
__A : List[str] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left"... | 27 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase ... | 84 | 0 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_... | 28 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 84 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCamelCase_ = 0
lowerCamelCase_ = len(lowerCAmelCase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
... | 29 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase ... | 84 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__a = True
except Imp... | 30 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import Tokeniz... | 84 | 0 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowerCamelCase__ : List[str] = logging.get_logger(__name__)
... | 31 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository c... | 84 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DU... | 32 |
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False ):
super().__init__()
lowercase ... | 84 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : Any = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if ... | 33 |
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which mus... | 84 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 34 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 84 | 0 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseT... | 35 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0)... | 84 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeni... | 36 |
from __future__ import annotations
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 ):
lowercase = 0
for i in range(1 , __SCRE... | 84 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY... | 37 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCA... | 84 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import ... | 38 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase =... | 84 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case_ ( __A ):
'... | 39 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = []
lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAKE_CASE ... | 84 | 0 |
from collections import Counter
from timeit import timeit
def UpperCamelCase ( snake_case__ : str = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def UpperCamelCase ( snake_case__ : str = "" ... | 40 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_a... | 84 | 0 |
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _A ( A__ , A__ , A__ , A__=5 ):
"""simple docstring"""
assert masked_input.count('''<mask>''' ) == 1
__lowercase = torch.tensor(tokenizer.encode(A__ ,... | 41 |
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... | 84 | 0 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassif... | 42 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test... | 84 | 0 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCAmelCase = logging.getLogger(__na... | 43 |
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_ ( __lowerCamelCase ):
'''simple docstring'''
_Upp... | 84 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = list(... | 44 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase ... | 84 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split,... | 45 |
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 ModelTesterMixin, ids_tensor
from .... | 84 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import random
class A_ :
def __init__( self: List[str] ,__lowerCAmelCase: int | None = None ):
'''simple docstring'''
_lowerCamelCase : Any = value
_lowerC... | 46 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable... | 84 | 0 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
SCREAMING_SNAKE_CASE__ ... | 47 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
l... | 84 | 0 |
'''simple docstring'''
from typing import Any
def A ( UpperCamelCase_ : list , UpperCamelCase_ : list , UpperCamelCase_ : dict , UpperCamelCase_ : dict , UpperCamelCase_ : dict , ) -> list:
'''simple docstring'... | 48 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_struc... | 84 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils impo... | 49 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase ... | 84 | 0 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = (UnCLIPScheduler,)
def UpperCamelCase_ ( self ,**_... | 50 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 84 | 0 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
a__ : int = {
'facebook/maskformer-swi... | 51 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase ... | 84 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
A = tuple[int, int]
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , ... | 52 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import Tokeniz... | 84 | 0 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
... | 53 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository c... | 84 | 0 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
impor... | 54 |
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False ):
super().__init__()
lowercase ... | 84 | 0 |
SCREAMING_SNAKE_CASE :Union[str, Any] = [0, 2, 4, 6, 8]
SCREAMING_SNAKE_CASE :Tuple = [1, 3, 5, 7, 9]
def UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0... | 55 |
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which mus... | 84 | 0 |
'''simple docstring'''
def _a (lowercase__ : int ) -> list:
"""simple docstring"""
__snake_case = int(lowercase__ )
if n_element < 1:
__snake_case = ValueError('a should be a positive number' )
raise my_error
_... | 56 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 84 | 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,
)
A_ : List[Any] = {'config... | 57 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0)... | 84 | 0 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
... | 58 |
from __future__ import annotations
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 ):
lowercase = 0
for i in range(1 , __SCRE... | 84 | 0 |
def lowerCAmelCase_ ( __a ) -> list[list[float]]:
"""simple docstring"""
lowerCamelCase__: list[list[float]] =[]
for data in source_data:
for i, el in enumerate(__a ):
if len(__a ) < i + 1:
data_lists.append([] )
data_lists[i].appen... | 59 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCA... | 84 | 0 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHea... | 60 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase =... | 84 | 0 |
def _A ( lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = 0
for ch in input_str:
lowerCAmelCase__ = ord(lowerCAmelCase_ )
lowerCAmelCase__ = pow(2 , lowerCAmelCase_ )
... | 61 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = []
lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAKE_CASE ... | 84 | 0 |
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Dict ):
SCREAMING_SNAKE_CASE : Optional[int] = {}
def _A ( self : str ):
print(self.vertex )
for i in self.vertex:
... | 62 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_a... | 84 | 0 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a : Any = logging.get_logger(__name__)
class a ( lowercase__ ):
"""simple docstring"""
def __init__( self : Optional[int] ... | 63 |
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... | 84 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_tra... | 64 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test... | 84 | 0 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__... | 65 |
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_ ( __lowerCamelCase ):
'''simple docstring'''
_Upp... | 84 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
... | 66 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase ... | 84 | 0 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
s... | 67 |
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 ModelTesterMixin, ids_tensor
from .... | 84 | 0 |
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... | 68 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable... | 84 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common impo... | 69 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
l... | 84 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : Any = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
... | 70 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_struc... | 84 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class _snake_case :
def __init__( self ,_snake_case ):
UpperCAmelCase_ : list[dict] = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "... | 71 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase ... | 84 | 0 |
'''simple docstring'''
_UpperCAmelCase : dict[str, float] = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_... | 72 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 84 | 0 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _snake_case ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
_lowercase : Tuple = [('''size''', ctypes.c... | 73 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase ... | 84 | 0 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...ut... | 74 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import Tokeniz... | 84 | 0 |
'''simple docstring'''
from math import factorial
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> float:
if successes > trials:
raise ValueError('''successes must be lower or equal to trials''' )
if trials < 0 or successes < 0:
... | 75 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository c... | 84 | 0 |
"""simple docstring"""
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_w... | 76 |
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False ):
super().__init__()
lowercase ... | 84 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : list[list[str]] = [[] for _ in range(UpperCamelCase )]
__UpperCAmelCase : Union[str, Any] ... | 77 |
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which mus... | 84 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float , snake_case_ : float ) -> tuple:
'''simple docstring'''
UpperCAmelCase_ ... | 78 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 84 | 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 ( ... | 79 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0)... | 84 | 0 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return "".join([hex(lowerCamelCase )[2:].zfill(2 ).upper() for byte in list(lowerCamelCase )] )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if (len(lowerCamelCase... | 80 |
from __future__ import annotations
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 ):
lowercase = 0
for i in range(1 , __SCRE... | 84 | 0 |
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 lowerCAmelCase_ ( ... | 81 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCA... | 84 | 0 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def a__ ( ):
UpperCAmelCase_ , UpperCAmelCase_ = 9, 14 # noqa: F841
UpperCAmelCase_ = [
[0, 1, 4],
... | 82 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase =... | 84 | 0 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSam... | 83 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = []
lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAKE_CASE ... | 84 | 0 |
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _a ( lowercase__ : int = 50_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__... | 85 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_a... | 84 | 0 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneCon... | 86 |
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... | 84 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , ... | 87 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test... | 84 | 0 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting impo... | 88 |
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_ ( __lowerCamelCase ):
'''simple docstring'''
_Upp... | 84 | 0 |
SCREAMING_SNAKE_CASE : Dict = "Input must be a string of 8 numbers plus letter"
SCREAMING_SNAKE_CASE : List[str] = "TRWAGMYFPDXBNJZSQVHLCKE"
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
_low... | 89 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase ... | 84 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffus... | 90 |
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 ModelTesterMixin, ids_tensor
from .... | 84 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers i... | 91 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable... | 84 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCamelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDepende... | 92 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
l... | 84 | 0 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .sch... | 93 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_struc... | 84 | 0 |
'''simple docstring'''
def lowercase_ ( __A : int = 3 , __A : int = 7 , __A : int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
lowercase : Union[str, Any] =0
lowercase : List[Any] =1
for current_denominator ... | 94 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase ... | 84 | 0 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" ,[
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.j... | 95 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 84 | 0 |
"""simple docstring"""
import enum
import shutil
import sys
__lowerCamelCase , __lowerCamelCase = shutil.get_terminal_size()
__lowerCamelCase = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class __A ( enum.Enum ):
UpperCAmelCase__ ... | 96 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase ... | 84 | 0 |
from __future__ import annotations
__a = 8.988E9 # units = N * m^s * C^-2
def a ( snake_case__: float , snake_case__: float , snake_case__: float , snake_case__: float ):
'''simple docstring'''
lowercase_ = abs(chargea * chargea )
if (forc... | 97 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import Tokeniz... | 84 | 0 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowercase__ : Optional[int] = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def a__ ( ) -... | 98 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository c... | 84 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
S... | 99 |
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False ):
super().__init__()
lowercase ... | 84 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.... | 100 |
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which mus... | 84 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : Any = [
'encoder.version',
'decoder.version',
'model.encoder.version',
... | 101 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerat... | 84 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_input... | 102 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0)... | 84 | 0 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 103 |
from __future__ import annotations
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 ):
lowercase = 0
for i in range(1 , __SCRE... | 84 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ ) -> None:
A__ = value
A__ ... | 104 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCA... | 84 | 0 |
import math
def __UpperCAmelCase ( lowerCamelCase_ : list , lowerCamelCase_ : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = len(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tuple = int(math.flo... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase =... | 84 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
Aut... | 106 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = []
lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAKE_CASE ... | 84 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import flo... | 107 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_a... | 84 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__a: Optional[int] = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
if ... | 108 |
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... | 84 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers... | 109 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test... | 84 | 0 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
UpperCamelCase__ = logging.get_logger(__name__)
def lowerCamelCase ( _snake_case ,_snake_case... | 110 |
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_ ( __lowerCamelCase ):
'''simple docstring'''
_Upp... | 84 | 0 |
def _A ( SCREAMING_SNAKE_CASE__ : Optional[Any] ):
for i in range(len(__SCREAMING_SNAKE_CASE ) - 1 , 0 , -1 ):
UpperCamelCase :Dict = False
for j in range(__SCREAMING_SNAKE_CASE , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 658 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase ... | 84 | 0 |
from collections.abc import Callable
import numpy as np
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , snake_case , snake_case ) -> int:
__lowercase = int(np.ceil((x_end - xa) / step_size ) )
__lowercase ... | 375 |
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 ModelTesterMixin, ids_tensor
from .... | 84 | 0 |
from sklearn.metrics import mean_squared_error
import datasets
A__ : Any = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and ... | 183 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable... | 84 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
... | 466 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
l... | 84 | 0 |
'''simple docstring'''
import math
def __UpperCAmelCase ( A : Tuple ) -> str:
UpperCAmelCase_ : int = [True] * n
UpperCAmelCase_ : Optional[Any] = False
UpperCAmelCase_ : Optional[int] = False
UpperCA... | 541 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_struc... | 84 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Optional[int] = logging.get_logger(__name__)
snake_case__ : Union[str, Any] = {
"""asapp/sew-d-tiny-100k""": """https... | 23 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase ... | 84 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_M... | 617 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ..... | 84 | 0 |
'''simple docstring'''
def _A ( lowercase__ ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persisten... | 325 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase ... | 84 | 0 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A_ : Optional[int] = 'src/transformers'
# This is to make sure the... | 57 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import Tokeniz... | 84 | 0 |
"""simple docstring"""
def _lowerCamelCase ( __a ):
SCREAMING_SNAKE_CASE_ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE_ = set()
return any(
node not in visited and depth_first_search(__SCREAMING_SNAKE_CASE... | 626 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository c... | 84 | 0 |
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