code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
U... | 387 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow... | 256 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a= logging.get_logger(__name__)
class __lowercase ( _lowerCamelCase , _lowerCamelCase ... | 287 | '''simple docstring'''
from timeit import timeit
a= {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# Ensure our test data is valid... | 287 | 1 |
'''simple docstring'''
from collections.abc import Callable
def lowerCamelCase__ ( a , a , a ):
__snake_case = a
__snake_case = b
if function(a ) == 0: # one of the a or b is a root for the function
return a
elif function(a )... | 356 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
raise... | 356 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class A_ ... | 535 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
snake_case = logging.get_logger(__name__)
class A_ ( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Dict ... | 535 | 1 |
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))
| 47 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerC... | 47 | 1 |
from manim import *
class __A ( snake_case__ ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Dict = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase : Optional[Any] = Rectangle(height=0.46 , ... | 721 | 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 (
AutoProcessor,
BertToken... | 587 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ):
# test for the above condition
self.test()
def UpperCamelCase_ ( ... | 50 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowerCAmelCase_ = None
try:
import msvcrt
except ImportError:
lowerCAmelCase_ = None
try:
import fcntl
except ImportError:
lowerCAmelCase_ = None
# ... | 173 | 0 |
'''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,
)
UpperCamelCase_ ... | 718 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke-... | 510 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class A__ ( datasets.BeamBasedBuilder ):
def A ( self : str ... | 405 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_d... | 405 | 1 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ) -> list[float]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , ... | 710 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__snake_case = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__snake_case = _LazyMod... | 400 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : int = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQ... | 265 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin... | 265 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , a__ : int ):
__magic_name__ = value
__magic_name__ = None
__magic_name__ ... | 721 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class _SCREAMING_SNAKE_CASE ( nn.Module ):
__SCREAMING_SNAKE_CASE :int
__SCREAMING_SNAKE_CASE :jnp.dtype = jnp.floataa
def snake_case__ ( self : List[str] ):
__magic_name_... | 245 | 0 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_t... | 98 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _lowerCamelCase( lowercase__ ) -> list[list[float]]:
'''simple docstring'''
__lowercase= Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementat... | 230 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 4_00_00_00 ):
'''simple docstring'''
lowercase = [0, 1]
lowercase = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
... | 716 |
"""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 : List[str] = logging.get_logger(__name__)
_UpperCamelC... | 134 | 0 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCAmelCase__ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCAmelCase__ = [ord(letter) for let... | 224 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_... | 257 | 0 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__a : Optional[int] = """\
@misc{chen2021evaluating,
title={Evaluating Large La... | 713 | import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
__lowercase = [
'''encoder.version''',
'''decoder.version''',
'... | 522 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
__snake_case = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
__snake_case = '''
Args:
predictions (`list` of... | 189 |
'''simple docstring'''
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,
... | 189 | 1 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a : Optional[int] = logging.getLogger(__name__)
class __Upper... | 711 |
"""simple docstring"""
from ....utils import logging
a : List[str] = logging.get_logger(__name__)
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self , snake_case__ , ... | 85 | 0 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : int ) -> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
UpperCAmelCase = gray_cod... | 51 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 51 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase__ :Optional[Any] = logging.get_logger(__name__)
lowercase_... | 633 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ :Tuple = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
... | 633 | 1 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
a = Mapping[str, np.ndarray]
a = Mapping[str, Any] # Is a nested dict.
a = 0.01
@data... | 109 | """simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbo... | 646 | 0 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compressi... | 702 | import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class lowercase_ ( datasets.BuilderConfig ):
_lowerCamelCase = None
class lowercase_ ( datasets.Ar... | 580 | 0 |
"""simple docstring"""
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,
get_resize_output_image_size,
normalize,
rescale,
re... | 174 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .ut... | 174 | 1 |
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 _UpperCamelCa... | 717 |
UpperCamelCase__ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def _UpperCamelCase ():
"""simple docstring"""
UpperCamelCase__ = input("""Enter message: """ )
UpperCamelCase__ = input("""Enter key [alphanumeric]: """ )
UpperCamelCase__... | 548 | 0 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvi... | 46 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : List[str] = logging.get_logger(__name__)
a__ : str = {
'''xlm-... | 682 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowercase_ : List[str] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7:... | 107 | import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cach... | 107 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require... | 293 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionCon... | 293 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.... | 705 |
"""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.a... | 397 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ... | 65 |
"""simple docstring"""
__UpperCAmelCase = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = ... | 65 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __magic_name__ ( SCREAMING_SNAKE_CA... | 710 |
from collections.abc import Sequence
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
return sum(c * (x**i) for i, c in enumerate(SCREAMING_SNAKE_CASE ) )
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREA... | 677 | 0 |
from __future__ import annotations
import math
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if len(UpperCamelCase__ ) != 2 or len(a[0] ) != 2 or len(UpperCamelCase__ ) != 2 or len(b[0] ) != 2:
raise Exception('Matri... | 362 |
_UpperCAmelCase : Any = 6_5521
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = 1
snake_case_ = 0
for plain_chr in plain_text:
snake_case_ = (a + or... | 362 | 1 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
A = numpy.array([0, 0])
A = numpy.array([0.5, 0.8660254])
A = numpy.array([1, 0])
A = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def __UpperC... | 277 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is... | 277 | 1 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
UpperCamelCase__ :Optional[Any] = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""":... | 355 |
"""simple docstring"""
def A_ ( snake_case__ , snake_case__ = " " ) -> list:
_UpperCamelCase :List[str] = []
_UpperCamelCase :int = 0
for index, char in enumerate(snake_case__ ):
if char == separator:
split_words.append(string[last_i... | 355 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"... | 541 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils im... | 541 | 1 |
import sys
from collections import defaultdict
class A_ :
'''simple docstring'''
def __init__(self ) -> Union[str, Any]:
__UpperCAmelCase = []
def lowerCAmelCase_ (self , lowercase__ ) -> Union[str, Any]:
return self.node_po... | 303 |
def __a ( SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
__UpperCAmelCase ... | 303 | 1 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
Ber... | 17 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_A )
class lowerCamelCase_ ( _A ):
'''simple docstring'''
# `task` is not a ClassVar since... | 17 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_... | 71 |
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self ... | 24 | 0 |
import argparse
SCREAMING_SNAKE_CASE__ = '''docs/source/_static/js/custom.js'''
def A ( __UpperCamelCase ) -> Dict:
with open(__UpperCamelCase , encoding='utf-8' , newline='\n' ) as f:
A__ = f.readlines()
A__ = 0
# First let's put the rig... | 52 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.p... | 52 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def __UpperCAmelCase ( ):
from torch.utils.cpp_extension import load
__lowercase : List[Any] = Path(__UpperCamelCase ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
... | 76 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging impo... | 541 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get... | 705 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCAmelCase ( _UpperCamelCase ):
@require_torch
def _snake_case ( self ) -> str:
"""simple ... | 629 | 0 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCAmelCase ( unittest.TestCase ):
@prope... | 678 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
Ma... | 678 | 1 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self ) -> None:
'''simple docstring'... | 712 |
import os
# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE__ = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(__lowerCamelCase ) )
lo... | 601 | 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
SCREAMING_SNAKE_CASE_ = (
"""This metric will be removed from the... | 582 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ : Optional[int] = {
"""configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}
try:
if n... | 548 | 0 |
'''simple docstring'''
import qiskit
def UpperCamelCase ( a , a ) -> qiskit.result.counts.Counts:
'''simple docstring'''
__magic_name__ = qiskit.Aer.get_backend('''aer_simulator''' )
__magic_name__ = qiskit.QuantumCircuit(4 , 2 )
# encode ... | 704 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at... | 245 | 0 |
'''simple docstring'''
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
... | 3 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : Optional[Any] = ... | 57 | 0 |
'''simple docstring'''
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = """▁"""
lowerCam... | 454 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( A , unit... | 454 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
__a =None
__a =False
__a =False
__a =False
__a =None
... | 692 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ , unittest.TestCase ):
"""s... | 692 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNA... | 719 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate mode... | 157 | 0 |
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 = logging... | 97 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 273 | 0 |
'''simple docstring'''
import numpy as np
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
return np.where(vector > 0, UpperCamelCase__, (alpha * (np.exp(UpperCamelCase__ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 720 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
def UpperCAmelCase__ ( lowerCamelCase ):
lowercase :int = ... | 453 | 0 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils ... | 33 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowerCamelCase_ ( lowerCamelCase ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
__magic_name__ :Optional[... | 0 | 0 |
'''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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transfo... | 714 |
'''simple docstring'''
_A : Dict ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_A : ... | 4 | 0 |
'''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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
I... | 379 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_fi... | 379 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__SCREAMING_SNAKE_CASE =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
... | 89 |
from __future__ import annotations
__SCREAMING_SNAKE_CASE ={
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""... | 89 | 1 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def ... | 411 |
from __future__ import annotations
from collections.abc import MutableSequence
class _lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCamelCase : int , UpperCamelCase : MutableSequence[float] ):
'''simple ... | 411 | 1 |
import random
def __UpperCamelCase ( A ):
UpperCamelCase__ = num - 1
UpperCamelCase__ = 0
while s % 2 == 0:
UpperCamelCase__ = s // 2
t += 1
for _ in range(5 ):
UpperCamelCase__ = rand... | 716 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ =logging.get_logger(__name__)
__magic_name__ ={
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all ViT MAE models at https://h... | 469 | 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 __a ( __snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CAS... | 650 |
import torch
from diffusers import DiffusionPipeline
class lowercase__ (__snake_case ):
"""simple docstring"""
def __init__( self : List[Any] , __a : Optional[Any] , __a : List[str] ):
super().__init__()
self.register_modules(unet=__a ... | 648 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
... | 254 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase__ = '.'
# Internal TensorF... | 254 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTI... | 120 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 120 | 1 |
from ...processing_utils import ProcessorMixin
class __magic_name__ ( _a):
_UpperCAmelCase : Union[str, Any] = ['image_processor', 'feature_extractor']
_UpperCAmelCase : int = 'TvltImageProcessor'
_UpperCAmelCase : Any = 'TvltFeatureExtractor'
def __init__( self : ... | 405 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if index == r:
for j in range(_lowerCAmelCase ):
print(data[j] , end=" " )
print(" " )
... | 405 | 1 |
'''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_m... | 672 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from... | 509 | 0 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .tes... | 717 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import T... | 261 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase__: Dict = logging.get_logger(__name__)
class SCRE... | 127 | 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
__lowercase = logging.get_logger(__name__)
__lowercase = {'''vocab_file''': '''sen... | 167 | 0 |
SCREAMING_SNAKE_CASE_ = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
SCREAMING_SNAKE_CASE_ = ... | 717 |
'''simple docstring'''
def __lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
__a = """"""
for word_or_phrase in separated:
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
... | 201 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 76 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeat... | 323 | 0 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
a : Tuple = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n ... | 609 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
a : str = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def lowercase ( ):
... | 609 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_s... | 585 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE = "laptop" ):
_snake_case = f"""https://www.amazon.in/laptop/s?k={product}"""
_snake_case = {
... | 585 | 1 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__A ={1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1)... | 113 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A ={
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_... | 113 | 1 |
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
__A = []
__A = set({"(", "[", "{"} )
__A = set({")", "]", "}"} )
__A = {"{": "}", "[": "]", "(": ")"}
for i in range(len(a_ ) ):
if s[i] in open_brackets... | 55 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Con... | 517 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> float:
'''simple docstring'''
__UpperCAmelCase = sorted(numsa + numsa )
__UpperCAmelCase , __UpperCAmelCase = divmod(len(_lowerCA... | 714 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCAmelCase ( _lowerCAmelCase = 3 )-> qiskit.result.counts.Counts:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ... | 617 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.trans... | 46 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : ... | 46 | 1 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow #... | 421 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case : Tuple = logging.get_logger(__name__)
_snake_case : Any = {
'nielsr/canine-s': 2048,
}
# Unicode defines 1,114,112 t... | 421 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_availa... | 476 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from... | 30 | 0 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
lowerCAmelCase_ : Optional[Any] = 'us-east-1' # defaults region
@dataclass
class lowerCamelCase_ :
_lowerCAmelCase : str
_lowerCAmelCase : Union[str, Any] = ... | 721 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils ... | 464 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
),
... | 77 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Ba... | 77 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A ( unittest.TestCase ):... | 715 |
'''simple docstring'''
from manim import *
class A ( _a ):
def __lowerCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
_a = Rectangle(height=0.5 , width=0.5 )
... | 377 | 0 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_lowerCAmelCase : str = logging.getLogger()
@unittest.skip('''Temporarily disable th... | 454 |
import argparse
import struct
import unittest
class __magic_name__ :
"""simple docstring"""
def __init__( self :str , snake_case :bytes ):
'''simple docstring'''
A_ : Dict = data
# Initialize hash values
... | 454 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__magic_name__ = logging.get_logger(__name__)
__mag... | 700 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _lowerCAmelCase ( A__: Dict , A__: int , A__: Optional[int]=1024 , A__: str=1024 , A__: Tuple=False , **A__:... | 391 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase_ ( __A, __A, __... | 486 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class A ( UpperCA... | 486 | 1 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _lowerCamelCase ( pl.LightningModule ):
def __init__(self , __a ) -> List[str]:
... | 544 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def a__ ( _SCREAMING_SNAKE_CASE ): # picklable for... | 544 | 1 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 107 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _lowerCamelCase :
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase ):
"""simple docstring"""
if dst_width < 0 or dst_height < 0... | 365 | 0 |
"""simple docstring"""
def lowerCamelCase (a_ :int = 6008_5147_5143) -> int:
try:
lowercase :Dict = int(a_)
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''')
if n <= 0:
... | 475 |
"""simple docstring"""
UpperCAmelCase = {str(digit): digit**5 for digit in range(10)}
def lowerCamelCase (a_ :int) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_))
def lowerCamelCase () -> int:
return sum(
... | 475 | 1 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
rais... | 82 |
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'])
def UpperCamelCase (lowercase_: Union[str, Any] ) ->... | 456 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowerCAmelCase_ = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowerCAmelCase_ = typing.Union[np.floataa, int, float] # noqa: UP007
... | 494 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resol... | 494 | 1 |
# 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.apache.org/licenses/LICENSE-2.0
#
#... | 559 | import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowerCamelCase_ : List[Any] = pd.read_csv("""sample_data.csv""", header=None)
lowerC... | 559 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : List[str] ... | 293 |
"""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,
)
UpperCamelCase : List[Any] = {
... | 293 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-cc-n... | 68 |
from __future__ import annotations
from collections.abc import Iterator
class _A :
"""simple docstring"""
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : int ) -> None:
__UpperCAmelCase =value
__UpperCAmelCase ... | 68 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...t... | 721 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = 0
for ch in input_str:
lowercase__ = ord(SCREAMING_SNAKE_CASE_ )
lowercase__ = pow(2 , SCREAMING_SNAKE_CASE_ )
# If we already turned on bit for current character's unicode
if bitmap ... | 37 | 0 |
"""simple docstring"""
_lowerCAmelCase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
... | 259 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
SCREAMING_SNAKE_CASE_ : str = {
'''... | 375 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.uti... | 434 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Optional[int] =logging.get_logger(__name__)
a__ : int ={
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''... | 434 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_snake_case : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Option... | 22 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase_ : int = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tokenization_tran... | 461 | 0 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.rober... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'''tokenization_transfo_xl''': [''... | 161 | 0 |
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ ... | 493 |
def lowerCAmelCase_ (lowercase__ : list ) -> list:
'''simple docstring'''
lowerCAmelCase__ = len(lowercase__ )
for _ in range(lowercase__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i... | 668 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_comm... | 715 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCamelCase = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 211 | 0 |
class UpperCamelCase__ :
def __init__( self : Dict , UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
lowercase_ = set_counts
lowercase_ = max(_snake_case )
lowercase_ = len(_snake_... | 412 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', ... | 4 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCamelCase = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
tr... | 536 | """simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class UpperCame... | 536 | 1 |
'''simple docstring'''
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
A__ : List[str] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclasses ... | 13 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import... | 37 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : str , __snake_case : int ) -> str:
__A : list[list[str]] = [[] for _ in range(__snake_case )]
__A : Optional[int] = key - 1
if key <= 0:
raise Value... | 338 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase=None , _UpperCAmelCase=None):
'''simple docstring'''
__A : Optional[Any] = list... | 338 | 1 |
import math
def _A ( _lowercase , _lowercase ) -> str:
"""simple docstring"""
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_lowercase )
else:
if x == 0: # 0 raised to an... | 1 |
from collections.abc import Callable
class __UpperCamelCase :
def __init__( self : Dict , lowerCAmelCase : Callable | None = None ):
'''simple docstring'''
UpperCAmelCase_ = []
# Stores indexes of each item for supporting updates and deletion.
U... | 162 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
snake_case = logging.get_logger(__name__)
class UpperCamelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self , ... | 718 |
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case = 'sr... | 406 | 0 |
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