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"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxC... | 352 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Any:
A = 0
A = 0
A = {}
def UpperCamelCase__ ( self ,lowerCamelCase_ ) ... | 77 | 0 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
UpperCAmelCase =mod... | 353 |
"""simple docstring"""
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_a ) + 1
A = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
... | 77 | 0 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common impor... | 354 |
"""simple docstring"""
from math import factorial
def _A ( _a : int = 1_0_0 ):
"""simple docstring"""
return sum(map(_a , str(factorial(_a ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the N... | 77 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _A ... | 355 |
"""simple docstring"""
from typing import Dict, Iterable, 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,
... | 77 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
fro... | 356 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _A ( _a : Callable , _a : float , _a : float , _a : float , _a : float ):
"""simple docstring"""
A = int... | 77 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 357 |
"""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 .... | 77 | 0 |
"""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, Ten... | 358 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL im... | 77 | 0 |
def _A ( _a : Optional[int] , _a : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
A = str(bin(_a ) )[2:] # remove the lead... | 359 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self ,lowerCamelCase_="sayef/fsner-bert-base-uncased" ) -> Tuple:
super(lowerCamelCase_ ... | 77 | 0 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_... | 360 |
"""simple docstring"""
import random
def _A ( _a : list , _a : Any ):
"""simple docstring"""
A , A , A = [], [], []
for element in data:
if element < pivot:
less.append(... | 77 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ... | 361 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms... | 77 | 0 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCAmelCase =re.compile(R"\b(a|an|the)\b", re.UNICODE)
UpperCAmelCase =None
def _A ( ):
"""simple docstring"""
... | 362 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"distilbert-base-unca... | 77 | 0 |
"""simple docstring"""
def _A ( _a : List[Any] , _a : List[str] ):
"""simple docstring"""
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError(... | 363 |
"""simple docstring"""
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 lowerCamelCase__ ( SC... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
_lowerCamelCase = 42
_lowerCamelCase = None
_lowerCamelCase =... | 364 |
"""simple docstring"""
def _A ( _a : Optional[int] ):
"""simple docstring"""
A = []
A = set({"""(""", """[""", """{"""} )
A = set({""")""", """]""", """}"""} )
A ... | 77 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,... | 365 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase ="\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {... | 77 | 0 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_avail... | 366 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ... | 77 | 0 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase =logging.g... | 367 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def _A ( _a : Callable[[int | float], int | float] , _a : int | float , _a : int | float , _a : int = 1_0_0 , ):
"""simple docs... | 77 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm imp... | 368 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def _A ( _a : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(_a , 0 ... | 77 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, 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
fro... | 369 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
... | 77 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase ={
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 370 |
"""simple docstring"""
def _A ( _a : int ):
"""simple docstring"""
A = abs(_a )
A = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def _A ( _a... | 77 | 0 |
"""simple docstring"""
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,
... | 371 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase =argparse.ArgumentParser()
parser.add_argument("--dump_path", de... | 77 | 0 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _A ( ):
"""simple docstring"""
A = {
"""repo_name""": ["""t... | 350 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
UpperCAmelCase =generate_large_matrix()
UpperCAmelCase =(
[[4, 3, 2, -1], [3, ... | 77 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _A ( _a : Union[str, Any] = "https://www.worldometers.info/coronavirus" ):
"""simple docstring"""
A = BeautifulSoup(requests.get(__lowerCamelCase ... | 351 |
"""simple docstring"""
import pytest
UpperCAmelCase ="__dummy_dataset1__"
UpperCAmelCase ="\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \... | 77 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
# See all CANINE mode... | 352 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Any:
A = 0
A = 0
A = {}
def UpperCamelCase__ ( self ,lowerCamelCase_ ) ... | 77 | 0 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
tor... | 353 |
"""simple docstring"""
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_a ) + 1
A = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
... | 77 | 0 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 354 |
"""simple docstring"""
from math import factorial
def _A ( _a : int = 1_0_0 ):
"""simple docstring"""
return sum(map(_a , str(factorial(_a ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the N... | 77 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase ={
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_a... | 355 |
"""simple docstring"""
from typing import Dict, Iterable, 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,
... | 77 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 356 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _A ( _a : Callable , _a : float , _a : float , _a : float , _a : float ):
"""simple docstring"""
A = int... | 77 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( __lowerCamelCase ):
'''simple docstring'''
_lowerCamelCase = ['image_processor', 'tokenizer']
... | 357 |
"""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 .... | 77 | 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 imp... | 358 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL im... | 77 | 0 |
def _A ( _a : str , _a : list[str] ):
"""simple docstring"""
A = """"""
for word_or_phrase in separated:
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise Exception("""joi... | 359 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self ,lowerCamelCase_="sayef/fsner-bert-base-uncased" ) -> Tuple:
super(lowerCamelCase_ ... | 77 | 0 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copie... | 360 |
"""simple docstring"""
import random
def _A ( _a : list , _a : Any ):
"""simple docstring"""
A , A , A = [], [], []
for element in data:
if element < pivot:
less.append(... | 77 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
... | 361 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms... | 77 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ... | 362 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"distilbert-base-unca... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...tes... | 363 |
"""simple docstring"""
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 lowerCamelCase__ ( SC... | 77 | 0 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
UpperCAmelCase ={
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>='''... | 364 |
"""simple docstring"""
def _A ( _a : Optional[int] ):
"""simple docstring"""
A = []
A = set({"""(""", """[""", """{"""} )
A = set({""")""", """]""", """}"""} )
A ... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _A ( _a : List[str] , _a : Any , _a : Union[str, Any] ):
"""simp... | 365 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase ="\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {... | 77 | 0 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sk... | 366 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ... | 77 | 0 |
"""simple docstring"""
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
UpperCAmelCase ... | 367 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def _A ( _a : Callable[[int | float], int | float] , _a : int | float , _a : int | float , _a : int = 1_0_0 , ):
"""simple docs... | 77 | 0 |
"""simple docstring"""
from string import ascii_uppercase
UpperCAmelCase ={char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase =dict(enumerate(ascii_uppercase))
def _A ( _a : str , _a : str ):
"""simple docstring"""
... | 368 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def _A ( _a : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(_a , 0 ... | 77 | 0 |
"""simple docstring"""
import random
def _A ( _a : int , _a : float , _a : bool = False ):
"""simple docstring"""
A = {i: [] for i in range(_snake_case )}
# if probability is greater or equal tha... | 369 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
... | 77 | 0 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 370 |
"""simple docstring"""
def _A ( _a : int ):
"""simple docstring"""
A = abs(_a )
A = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def _A ( _a... | 77 | 0 |
"""simple docstring"""
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class lowerCamelCase__ ( _a ):
'''simple docstring'''
_lowerCamelCase = '''facebook/bart-large-mnli'''
_lowerCamelC... | 371 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase =argparse.ArgumentParser()
parser.add_argument("--dump_path", de... | 77 | 0 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB 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.apa... | 350 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
UpperCAmelCase =generate_large_matrix()
UpperCAmelCase =(
[[4, 3, 2, -1], [3, ... | 77 | 0 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _A ( _a : Optional[Any] , _a : Dict , _a : Optional[int] ):
"""simple docstring"""
A... | 351 |
"""simple docstring"""
import pytest
UpperCAmelCase ="__dummy_dataset1__"
UpperCAmelCase ="\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \... | 77 | 0 |
"""simple docstring"""
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
UpperCAmelCase =logging.get_logger(__name... | 352 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Any:
A = 0
A = 0
A = {}
def UpperCamelCase__ ( self ,lowerCamelCase_ ) ... | 77 | 0 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase =O... | 353 |
"""simple docstring"""
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_a ) + 1
A = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
... | 77 | 0 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _A ( _a : List[Any] , _a : List[str]=1 ):
"""simple docstring"""
if n_shave_prefix_segments ... | 354 |
"""simple docstring"""
from math import factorial
def _A ( _a : int = 1_0_0 ):
"""simple docstring"""
return sum(map(_a , str(factorial(_a ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the N... | 77 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase__ ( lowerCAmelCase__ ):
'''simple docstring'''
_lowerCamelCase = "M-CLIP"
def __init__( self ,lowerCamelCase_=1_0_2_4 ,lowerCamelCase_=7_6_... | 355 |
"""simple docstring"""
from typing import Dict, Iterable, 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,
... | 77 | 0 |
"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCamelCase__ ( nn.Module ):
'''simple docstring'''
_lowerCamelCase = 42
_lowerCamelCase = jnp.floataa
def UpperCamelCase__ ( self ) -> str:
A = ... | 356 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _A ( _a : Callable , _a : float , _a : float , _a : float , _a : float ):
"""simple docstring"""
A = int... | 77 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from ... | 357 |
"""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 .... | 77 | 0 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( lowerCamelCase__ ):
'''simple docstring'''
_lowerCamelCase = (DDIMParallelScheduler,)
_lowerCa... | 358 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL im... | 77 | 0 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transfo... | 359 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self ,lowerCamelCase_="sayef/fsner-bert-base-uncased" ) -> Tuple:
super(lowerCamelCase_ ... | 77 | 0 |
"""simple docstring"""
def _A ( _a : Union[str, Any] ):
"""simple docstring"""
A = [0] * len(a__ )
A = []
A = []
A = 0
for values in graph.values()... | 360 |
"""simple docstring"""
import random
def _A ( _a : list , _a : Any ):
"""simple docstring"""
A , A , A = [], [], []
for element in data:
if element < pivot:
less.append(... | 77 | 0 |
"""simple docstring"""
def _A ( _a : int = 1_0_0_0 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 361 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms... | 77 | 0 |
"""simple docstring"""
import re
def _A ( _a : Optional[Any] ):
"""simple docstring"""
A = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.s... | 362 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"distilbert-base-unca... | 77 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin... | 363 |
"""simple docstring"""
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 lowerCamelCase__ ( SC... | 77 | 0 |
"""simple docstring"""
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
UpperCA... | 364 |
"""simple docstring"""
def _A ( _a : Optional[int] ):
"""simple docstring"""
A = []
A = set({"""(""", """[""", """{"""} )
A = set({""")""", """]""", """}"""} )
A ... | 77 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowerCamelCase__ ( __snake_case ):
'''simple docstring'''
def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> str:
... | 365 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase ="\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {... | 77 | 0 |
"""simple docstring"""
import sys
UpperCAmelCase =(
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
... | 366 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ... | 77 | 0 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,... | 367 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def _A ( _a : Callable[[int | float], int | float] , _a : int | float , _a : int | float , _a : int = 1_0_0 , ):
"""simple docs... | 77 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeTokenize... | 368 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def _A ( _a : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(_a , 0 ... | 77 | 0 |
"""simple docstring"""
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def _A ( _a : List[Any] , _a : int , _a : Optional[Any] , _a : List[str] , _a : List[Any] ):
"""simple docs... | 369 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
... | 77 | 0 |
import functools
from typing import Any
def _A ( _a : str , _a : list[str] ):
"""simple docstring"""
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or len(_lowerCamelCase ) == 0:
raise ValueError("""the st... | 370 |
"""simple docstring"""
def _A ( _a : int ):
"""simple docstring"""
A = abs(_a )
A = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def _A ( _a... | 77 | 0 |
"""simple docstring"""
import functools
from typing import Any
def _A ( _a : int , _a : Optional[Any] ):
"""simple docstring"""
if not isinstance(_a , _a ) or len(_a ) == 0:
raise ValueError("""the string sh... | 371 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase =argparse.ArgumentParser()
parser.add_argument("--dump_path", de... | 77 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def ... | 350 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
UpperCAmelCase =generate_large_matrix()
UpperCAmelCase =(
[[4, 3, 2, -1], [3, ... | 77 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
... | 351 |
"""simple docstring"""
import pytest
UpperCAmelCase ="__dummy_dataset1__"
UpperCAmelCase ="\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \... | 77 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNe... | 352 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Any:
A = 0
A = 0
A = {}
def UpperCamelCase__ ( self ,lowerCamelCase_ ) ... | 77 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configurati... | 353 |
"""simple docstring"""
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_a ) + 1
A = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
... | 77 | 0 |
"""simple docstring"""
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_lowerCAmelCase ) + 1
A = len(_lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes ... | 354 |
"""simple docstring"""
from math import factorial
def _A ( _a : int = 1_0_0 ):
"""simple docstring"""
return sum(map(_a , str(factorial(_a ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the N... | 77 | 0 |
def _A ( _a : int = 1_0_0 ):
"""simple docstring"""
A = (n * (n + 1) // 2) ** 2
A = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"""{solution() = }""")... | 355 |
"""simple docstring"""
from typing import Dict, Iterable, 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,
... | 77 | 0 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _A ( _a : List[str] , _a : List[Any] , _a : Union[str, Any] , _a : Unio... | 356 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _A ( _a : Callable , _a : float , _a : float , _a : float , _a : float ):
"""simple docstring"""
A = int... | 77 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase ={}
try:
if not is_sentencepiece_available():
r... | 357 |
"""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 .... | 77 | 0 |
"""simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_co... | 358 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL im... | 77 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
UpperCAmelCa... | 359 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self ,lowerCamelCase_="sayef/fsner-bert-base-uncased" ) -> Tuple:
super(lowerCamelCase_ ... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _A ( _a : int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number %... | 360 |
"""simple docstring"""
import random
def _A ( _a : list , _a : Any ):
"""simple docstring"""
A , A , A = [], [], []
for element in data:
if element < pivot:
less.append(... | 77 | 0 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
return [
a * b * (1_0_0_0 - a - b)
for a in range(1 , 9_9_9 )
for b in range(__lowerCAmelCase , 9_9_9 )
if (a * a + b * b == (1_0_0_0 - a - b)... | 361 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms... | 77 | 0 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transforme... | 362 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"distilbert-base-unca... | 77 | 0 |
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
C... | 363 |
"""simple docstring"""
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 lowerCamelCase__ ( SC... | 77 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def _A ( _a : Sequence[float] , _a : bool = False ):
"""simple docstring"""
if not arr:
return 0
A = 0 if allow_empty_subarrays el... | 364 |
"""simple docstring"""
def _A ( _a : Optional[int] ):
"""simple docstring"""
A = []
A = set({"""(""", """[""", """{"""} )
A = set({""")""", """]""", """}"""} )
A ... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : int | str ):
"""simple docstring"""
A = str(lowerCAmelCase__ )
return n == n[::-1]
def _A ( _a : int = 1_0_0_0_0_0_0 ... | 365 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase ="\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {... | 77 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAm... | 366 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ... | 77 | 0 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _A ( ):
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename ... | 367 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def _A ( _a : Callable[[int | float], int | float] , _a : int | float , _a : int | float , _a : int = 1_0_0 , ):
"""simple docs... | 77 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils impor... | 368 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def _A ( _a : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(_a , 0 ... | 77 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ... | 369 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
... | 77 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase ={
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
... | 370 |
"""simple docstring"""
def _A ( _a : int ):
"""simple docstring"""
A = abs(_a )
A = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def _A ( _a... | 77 | 0 |
"""simple docstring"""
def _A ( _a : str , _a : Optional[int] ):
"""simple docstring"""
return 1 if input_a == input_a else 0
def _A ( ):
"""simple docstring"""
assert xnor_gate(0 , 0 ... | 371 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase =argparse.ArgumentParser()
parser.add_argument("--dump_path", de... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase =TypeVar("T")
class lowerCamelCase__ ( Generic[T] ):
'''simple docstring'''
def __init__( self ,lowerCa... | 350 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
UpperCAmelCase =generate_large_matrix()
UpperCAmelCase =(
[[4, 3, 2, -1], [3, ... | 77 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase__ ( __lowercase , unittest.TestCase ):
... | 351 |
"""simple docstring"""
import pytest
UpperCAmelCase ="__dummy_dataset1__"
UpperCAmelCase ="\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \... | 77 | 0 |
"""simple docstring"""
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.mo... | 352 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Any:
A = 0
A = 0
A = {}
def UpperCamelCase__ ( self ,lowerCamelCase_ ) ... | 77 | 0 |
"""simple docstring"""
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 import TensorFormatter... | 353 |
"""simple docstring"""
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_a ) + 1
A = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
... | 77 | 0 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
UpperCAmelCase ="\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: imp... | 354 |
"""simple docstring"""
from math import factorial
def _A ( _a : int = 1_0_0 ):
"""simple docstring"""
return sum(map(_a , str(factorial(_a ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the N... | 77 | 0 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _A ( ):
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
fr... | 355 |
"""simple docstring"""
from typing import Dict, Iterable, 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,
... | 77 | 0 |
"""simple docstring"""
def _A ( _a : int , _a : int , _a : int ):
"""simple docstring"""
A = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def ... | 356 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _A ( _a : Callable , _a : float , _a : float , _a : float , _a : float ):
"""simple docstring"""
A = int... | 77 | 0 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
UpperCAmelCase =(
'This metric will be removed from the... | 357 |
"""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 .... | 77 | 0 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCAmelCase =logging.get_logger("transformers.models.speecht5")
def _A ( _a : str ... | 358 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL im... | 77 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _A ( _a : Union[str, Any] = "isbn/0140328726" ):
"""simple docstring"""
A = olid.strip().strip("""/""" ) # Remove lead... | 359 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self ,lowerCamelCase_="sayef/fsner-bert-base-uncased" ) -> Tuple:
super(lowerCamelCase_ ... | 77 | 0 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _A ( _a : int , _a : str , _a : Union[str, Any]=1_0_2_4 , _a : T... | 360 |
"""simple docstring"""
import random
def _A ( _a : list , _a : Any ):
"""simple docstring"""
A , A , A = [], [], []
for element in data:
if element < pivot:
less.append(... | 77 | 0 |
"""simple docstring"""
import math
def _A ( _a : int , _a : str ):
"""simple docstring"""
A = len(__snake_case )
A = int(math.floor(math.sqrt(__snake_case ) ) )
A ... | 361 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : Optional[int] , _a : Union[str, Any] ):
"""simple docstring"""
A = sorted(numsa + numsa )
A , A = divmod(len(Up... | 362 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"distilbert-base-unca... | 77 | 0 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
UpperCAmelCase ="\\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,... | 363 |
"""simple docstring"""
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 lowerCamelCase__ ( SC... | 77 | 0 |
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