code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransf... | 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {"config... | 158 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 371 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {'vocab_file': 'vocab.json'}
_a = {
'vocab_file': {
'mgp-str': 'https:/... | 23 | 0 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils i... | 249 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch... | 249 | 1 |
from __future__ import annotations
from random import random
class UpperCamelCase :
def __init__( self , UpperCAmelCase__ = None ):
A__ = value
A__ = random()
A__ = None
A__ = None
def __repr__( self )... | 198 |
import datasets
from .evaluate import evaluate
UpperCAmelCase_ : List[Any] = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},... | 198 | 1 |
from ..utils import DummyObject, requires_backends
class A__(metaclass=a_ ):
"""simple docstring"""
_A : Optional[Any] = ['''sentencepiece''']
def __init__( self , *_lowercase , **_lowercase ) -> Tuple:
requires_backends(self , ["""senten... | 248 |
__snake_case : Dict = 6_55_21
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : List[str] = 1
a_ : Any = 0
for plain_chr in plain_text:
a_ : Tuple = (a + ord(a__)) % MOD_ADLER
a_ : Union[str, Any] ... | 248 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a__ = logging.getLogger(__name__)
class UpperCAmelCase_ :
"""simple docstring"""
def __init__(... | 15 |
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
... | 15 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowercase__:
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ) -> Any:
lowercase_ = data
... | 30 |
'''simple docstring'''
from maths.prime_check import is_prime
def __lowerCAmelCase ( snake_case__ ):
if not isinstance(snake_case__ , snake_case__ ):
__UpperCamelCase : Optional[int] = F"Input value of [number={number}] must be an intege... | 298 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
UpperCAmelCase : Dict = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_AR... | 313 |
"""simple docstring"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
B... | 313 | 1 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A = logging.get_logger(__name__)
_A ... | 62 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice... | 60 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_rea... | 190 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase = None):
'''simple docstring'''
__A : str = value
__A ... | 190 | 1 |
'''simple docstring'''
from __future__ import annotations
a_ : str = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
a_ : str = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def a_ ( __snake_case : list[float... | 75 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
a__ : Optional[Any] =... | 349 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from... | 365 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing imp... | 183 | 0 |
from math import factorial
class __snake_case :
def __init__( self ,snake_case ,snake_case ):
'''simple docstring'''
lowercase : List[str] = real
if isinstance(snake_case ,snake_case ):
lowercase : Any ... | 20 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lic... | 23 | 0 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_at... | 370 |
'''simple docstring'''
import functools
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =len(_lowerCAmelCase )
__lowercase =len(_lowerCAmelCase )
@functools.cache
def min_distance(_lowerCAmelCase ,... | 48 | 0 |
"""simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__lowerCAmelCase : List[Any] ={
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.a... | 197 | """simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__lowerCAmelCase : Dict =version.parse(version.parse(torch.__version__).base_version) < version.parse(... | 197 | 1 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ... | 362 |
"""simple docstring"""
def _A ( lowercase = 2_00_00_00 ):
"""simple docstring"""
a =[0 for i in range(n + 1 )]
a =1
a =1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 215 | 0 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
SCREAMING_SNAKE_CASE :int = logging.getLogger(__name__)
class UpperCAmelCase :
'''simple docstring'''
de... | 15 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , ... | 15 | 1 |
import os
import pytest
from attr import dataclass
_A = "us-east-1" # defaults region
@dataclass
class lowerCamelCase :
UpperCAmelCase__ : str
UpperCAmelCase__ : Optional[int] = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
U... | 137 |
from __future__ import annotations
def lowercase_ ( A__ , A__ , A__ ) -> int | float:
"""simple docstring"""
if len(A__ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(A__ )
or left < -len(A__ ... | 137 | 1 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterM... | 141 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_... | 141 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestC... | 371 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : List[Any] = logging.get_log... | 338 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
requi... | 155 |
"""simple docstring"""
from __future__ import annotations
def lowercase (snake_case__ : list , snake_case__ : int , snake_case__ : int , snake_case__ : int ) -> list:
'''simple docstring'''
lowerCAmel... | 155 | 1 |
import datasets
from .evaluate import evaluate
__A = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
__A =... | 364 |
def lowerCAmelCase_ ( __a ) -> str:
"""simple docstring"""
if isinstance(__a , __a ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(__a , __a ):
raise TypeError("'str' object cannot be interpreted as an integer" )
if ... | 273 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import Auto... | 191 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device... | 191 | 1 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__lowercase = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual La... | 362 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWit... | 226 | 0 |
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__)
_UpperCAmelC... | 140 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Dict:
# Initialise PyT... | 48 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
a_ = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang... | 365 |
"""simple docstring"""
from math import factorial
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
re... | 291 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
lowerCAm... | 110 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 284 | 0 |
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
_A : Optional[Any] ='''1'''
_A : Any ='''0'''
_A : Union[str, Any] ='''1'''
_A : Tuple =ort.SessionOptions()
_A : List... | 353 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_A : str =WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''... | 129 | 0 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowerCAmelCase_ ( __a ) -> str:
"""simple docstring"""
lowerCamelCase__: List[Any] =[
"encoder.version",
"decoder.version",
"... | 10 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerC... | 130 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformer... | 370 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def a__ ( lowerCAmelCase__ ) -> None:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = analyze_text(lowerCA... | 299 | 0 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( _lowercase = 5000 ):
'''simple docstring'''
snake_case_ :Any = [(i * (3 * i - 1)) // 2 for i... | 66 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils impor... | 109 | 0 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Tuple ) -> int:
if not head:
return True
# split the list to two parts
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = head.next, head
while fast and fast.next:
__lowerCAmelCase : Any = fast.next.nex... | 232 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
... | 232 | 1 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'The converted tokenizer will b... | 107 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class snake_case__ :
"""simple docstring"""
def __init__( self : Any , __lowerCamelCase : list[tuple[float, float]] ) -> Tuple:
a = list_of_points
... | 107 | 1 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | 361 |
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 import TFXLMRobertaM... | 146 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
f... | 272 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstring"""
pass
class __snake_case :
"""simple docstring"""
... | 120 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 350 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ):
lowerCAmelCase = word_bank or []
# create a table
lowerCAmelCase = len(_UpperCAmelCase ) + 1
lowerCAmelCase ... | 309 | 0 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Tuple ... | 291 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
... | 291 | 1 |
'''simple docstring'''
import string
def __lowerCamelCase ( lowerCAmelCase_ ) -> str:
_a : List[Any] = ''
for i in sequence:
_a : Dict = ord(lowerCAmelCase_ )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= ... | 107 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 107 | 1 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class a__ ( nn.Module ):
_a : int
_a : int
_a : float = 0.0
_a ... | 92 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers... | 139 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
... | 362 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''facebook/xlm-roberta-xl''': '''https://h... | 211 | 0 |
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRoberta... | 28 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class _a ( _lowercase):
def __init__( self : Optional[int] , *_SCREAMING_SNAKE_CASE : ... | 131 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected", [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_... | 351 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
UpperCamelCase__ =logging.getLogger(__name__)
class lowerCAmelCase__( __lowercase ):
'''simple docstri... | 325 | 0 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ... | 92 |
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):
import torch_xla.core.x... | 92 | 1 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowerCAmelCase ( a , a ):
"""simple docstring"""
@r... | 254 |
"""simple docstring"""
from pathlib import Path
import fire
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ :List[str] = Path(_SCREAMING_SNAKE_CASE ... | 254 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float = 0 ) -> None:
__S... | 54 | from __future__ import annotations
def UpperCamelCase__ ( A__ , A__ , A__ ) -> tuple[float, list[float]]:
snake_case__ : Optional[Any] = list(range(len(A__ ) ) )
snake_case__ : str = [v / w for v, w in zip(A__ , A__ )]
index.sor... | 143 | 0 |
from __future__ import annotations
from math import pi
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if induct... | 357 |
def lowerCamelCase__ ( lowercase , lowercase = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = length or len(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 319 | 0 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( lowerCAmelCase__ = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
lowercase = BeautifulSoup(requests.get(lowerCAmelCase__ ).text , '''html.parser''' )
lowercase = soup.... | 101 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 195 | 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 a... | 364 | def lowercase_ ( _lowerCamelCase : int = 1 , _lowerCamelCase : int = 1000):
lowercase__ : Union[str, Any] = 1
lowercase__ : int = 0
for divide_by_number in range(_lowerCamelCase , digit + 1):
lowercase__ : list[int] = ... | 333 | 0 |
import os
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : Union[str, Any] = len(grid[0])
a_ : str = len(a__)
a_ : Tuple = 0
a_ : List[Any] = 0
a_ : Optional[Any] = 0
# Check vertically, horizon... | 248 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import... | 248 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
A_ ... | 366 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
D... | 132 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...uti... | 232 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Union[str, Any] ) -> None:
__UpperCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
__UpperCamelCase : List[str] = False
def _lowerCa... | 232 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import requests
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... | 190 |
'''simple docstring'''
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
lowercase__ : List[Any] = HfArgumentParser(InitializationArguments)
lowercase__ : List[Any] ... | 190 | 1 |
from __future__ import annotations
import math
def a__ ( A_ ):
'''simple docstring'''
if num <= 0:
__magic_name__ = f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(A_ )
__magic_name__ = [True] ... | 88 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils... | 334 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_... | 354 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lo... | 212 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__... | 177 | """simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase=2_8_1_2_3 ) -> Any:
lowercase__: Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k... | 177 | 1 |
'''simple docstring'''
from __future__ import annotations
def a_ ( lowerCamelCase : int ):
lowerCAmelCase = 2
lowerCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(lowerCam... | 55 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
... | 55 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( _a):
def decorator(_a):
SCREAMING_SNAKE_CASE : Dict = getattr(_a , "handle_key" , [])
handle += [key]
setattr(_a , "handle_key" , _a)
return func
return decorat... | 76 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 0 |
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = generate_pascal_triangle(__lowerCamelCase )
for row_idx in range(__lowerCamelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''' )
# Print row values
for col_idx in rang... | 257 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def A__ ( ):
SCREAMING_SNAKE_CASE_ = 9
SCREAMING_SNAKE_CASE_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
... | 257 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMS... | 294 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_snake_case = logging.getLogger(__name__)
class UpperCamelCase ( snak... | 294 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
__A : Union[str, Any] = "2020.9.26"
__A : Union[str, Any] = "xcodz-dot, cclaus, dhruvmanila"
def UpperCamelCase_ ( A__ : Optional[int] , A__ : List[An... | 371 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( A__ : int | float | str , A__ : int | float | str ):
'''simple docstring'''
if nth_term == "":
return [""]
lowerCAmelCase_ : str = i... | 89 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
"""configuration_efficientformer""": [
"""EFF... | 319 | from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
... | 210 | 0 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : List[Any] = year % 19
lowerCAmelCase__ : Dict = year % 4
lowerCAmelCase__ : Dict ... | 360 |
'''simple docstring'''
import unittest
import numpy as np
import requests
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_inpu... | 184 | 0 |
import unittest
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , ) -> np.ndarray:
"""simple docstring"""
A : Tuple = np.shape(lowercase__ )
A : Any ... | 116 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase = '''▁'''
lowerCAmelCase = {'''vocab_file''': '''spiece.model'''}
lowerCA... | 295 | 0 |
from __future__ import annotations
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
lowercase :list[list[int]] = []
lowercase :list[int] = []
lowercase :Optional[Any] = 0
lowercase :Any = sum(lowerCamelCase )
create_state_space_tree(lowerCam... | 158 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, **lowerCamelCase ):
lowercase :List[Any] = AutoConfig.from_pretrained(lowerCamelCase, **lowerCamelCase )
lowercase :Union[st... | 158 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
snake_case : int = (3, 9, -11, 0, 7, 5, 1, -1)
snake_case : Optional[Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _snake_case :
SCREAMING_SNAK... | 94 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 100_0000 ):
"""simple docstring"""
a :Any = set(range(3 , UpperCAmelCase_ , 2 ) )
primes.add(2 )
for p in range(3 , UpperCAmelCase_ , 2 ):
if p not in primes:
... | 94 | 1 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class lowercase_ :
_lowerCamelCase = field(
metadata={'help': 'The output directory w... | 365 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimension_... | 284 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, i... | 335 | '''simple docstring'''
_lowercase : str = tuple[float, float, float]
_lowercase : List[Any] = tuple[float, float, float]
def lowerCamelCase ( UpperCAmelCase__ : Pointad , UpperCAmelCase__ : Pointad ) -> Vectorad:
lowercase_ ... | 239 | 0 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_uti... | 361 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCamelCase__ =np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (... | 325 | 0 |
'''simple docstring'''
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
cl... | 206 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_mo... | 206 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
lowerCamelCase_ : Optional[int] = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def A__ ( ) -> Dict:
UpperCamelCase_: Optional[int] = os.path.dirname(os.path.realpath(__UpperCAmelCase ) )
UpperCamelC... | 356 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCamelCase_ : Optional[Any] = datasets.utils.logging... | 223 | 0 |
"""simple docstring"""
from math import sqrt
def __lowerCAmelCase (_UpperCamelCase = 100_0000 ):
__lowerCAmelCase : int = 0
__lowerCAmelCase : int = 0
__lowerCAmelCase : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in... | 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 FlaxTimestepEmbed... | 59 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
A: Union[str, Any] = datasets.utils.logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( folder_based_builder.Fol... | 76 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 1 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
snake_case__ : Optional[Any] ... | 38 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase_ : Dict = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
UpperCAmelCase_ : Any = '''
Args:
... | 38 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_av... | 164 |
'''simple docstring'''
import math
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
return math.sqrt(a_ ) * math.sqrt(a_ ) == num
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
... | 164 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
loggi... | 85 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .to... | 168 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from .... | 354 | """simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ... | 154 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
... | 159 |
from __future__ import annotations
from collections import namedtuple
def _lowerCAmelCase ( lowerCAmelCase_ :float , lowerCAmelCase_ :float , lowerCAmelCase_ :float )->tuple:
'''simple docstring'''
snake_case_ = namedtuple("result"... | 159 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import Patchin... | 362 |
"""simple docstring"""
_UpperCamelCase : List[Any] = 8.31_44_62 # Unit - J mol-1 K-1
def snake_case (A_ :float , A_ :float , A_ :float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. E... | 186 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Optional[int]= {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():
r... | 172 | import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCAmelCase = version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
from nltk import word_tokenize
_UpperCAmelCa... | 140 | 0 |
class _lowerCamelCase:
"""simple docstring"""
def __init__( self, lowerCamelCase) -> str:
"""simple docstring"""
_lowercase : str = arr.split(',')
def UpperCamelCase ( self) -> Optional[int]:
"""simple d... | 353 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> int:
_lowercase : Union[str, Any] = len(lowerCamelCase_ ) // 2
# choose the middle 3 elements
_lowercase : Any = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] >... | 84 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class lowerCamelCase__ ( __lowercase):
'''simple do... | 232 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, ... | 232 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase : str = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""":... | 354 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 45 | 0 |
from __future__ import annotations
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : str) ->List[str]:
'''simple docstring'''
A__ , A__ = te... | 14 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCAm... | 346 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer L... | 369 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
... | 238 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)... | 38 |
# 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 ap... | 38 | 1 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
__lowerCAmelCase = "us-east-1" # defaults region
@dataclass
class _lowerCAmelCase :
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAmelCase_ = "arn:aws:iam::558105141... | 361 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(_SCREAMI... | 270 | 0 |
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
raise ValueError("Capacitance cannot be 0 or negative"... | 19 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = [0 for i in range(r + 1 )]
# nc0 = 1
lowerCamelCase_ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
lowerCamelCase_ = min(lowerCamelCase__ , lo... | 19 | 1 |
from __future__ import annotations
class a_ :
'''simple docstring'''
def __init__( self : List[str] , lowercase__ : list[list[int]]):
'''simple docstring'''
lowerCAmelCase__ = TypeError(
'Matrices must be formed from a list of zero or more... | 357 | lowerCAmelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def __lowerCamelCase ( lowerCAmelCase__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCa... | 119 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available(... | 25 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = ('''dense.weight''', '''attention.self.qu... | 101 | 0 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = {
'''en''': '''Machine learn... | 356 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}... | 300 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 73 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 0 |
"""simple docstring"""
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase : List[str] ... | 320 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : str , _UpperCamelCase : Optional[int] ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = 0
while b > 0:
if b & 1:
res += a... | 320 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Union[str, Any] = args.pruning_method
UpperCAmelCase_ : int ... | 61 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> str | Literal[False]:
_a : Optional[int] = list(lowerCAmelCase_ )
_a ... | 89 | 0 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=... | 217 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'''Intel/dpt-large''': '''https://huggingface.... | 217 | 1 |
'''simple docstring'''
def a_ ( __snake_case : int = 400_0000 ) -> int:
"""simple docstring"""
lowerCamelCase_ =[]
lowerCamelCase_, lowerCamelCase_ =0, 1
while b <= n:
if b % 2 == 0:
even_fib... | 75 | def a__ ( __UpperCamelCase ):
if n == 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
return 0
elif n == 2:
return 1
else:
SCREAMING_SNAKE_CASE_ = [0, 1]
for i in range(2 , n + 1 ):
sequence... | 118 | 0 |
'''simple docstring'''
from math import factorial
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = 100 ):
return sum(map(_a , str(factorial(_a ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 361 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 294 | 0 |
# Imports
import numpy as np
class _a :
"""simple docstring"""
def __init__( self: str , __lowerCamelCase: Optional[int]=None , __lowerCamelCase: Optional[int]=None , __lowerCamelCase: Union[str, Any]=None , __lowerCamelCase... | 149 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_avail... | 149 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 339 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
SCREAMING_SNAKE_CASE__ : List[Any] = namedtuple("covid_data", "cases deaths recovered")
def __magic_name__ ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/" ... | 339 | 1 |
def _A ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
a__ : int =len(SCREAMING_SNAKE_CASE )
a__ : int =len(SCREAMING_SNAKE_CASE )
a__ : int =(
fir... | 95 |
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 FlaxTimestepEmbed... | 59 | 0 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self):
__SCREAMING_SNAKE_CASE = """"""
__SCREAMING_SNAKE_CASE = """"""
__SCREAMING_... | 255 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()[0]
__SCREAMIN... | 255 | 1 |
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