code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def a ( lowerCamelCase_ = 50 ):
'''simple docstring'''
lowercase__ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 671 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ =... | 671 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
A__ : Any = logging.get_logger(__name__)
A__ ... | 671 |
from functools import reduce
A__ : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648... | 671 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeni... | 671 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
f... | 671 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
... | 671 |
from collections import defaultdict
from math import gcd
def a ( lowerCamelCase_ = 150_0000 ):
'''simple docstring'''
lowercase__ = defaultdict(lowerCamelCase_ )
lowercase__ = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for eucli... | 671 | 1 |
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=A__ ):
"""simple docstring"""
lowercase__ = ["""flax"""]
def __init__( self : Any, *lowerCamelCase : Optional[Any], **lowerCamelCase : int ):
''... | 671 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : Dict =... | 671 | 1 |
def a ( lowerCamelCase_ ):
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
lowercase__ = sorted(string.lower() )
return len(lowerCamelCase_ )... | 671 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
... | 671 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : list[tuple[float, float]] ):
'''simple docstring'''
lowercase__ ... | 671 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A__ : Dict = 50_00_00
A__ , A__ : str = os.path.split(__file__)
A__ : Optional[Any] = os.path.join(RESULTS_BASEPATH, 'res... | 671 | 1 |
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,
AutoModelForMaskedL... | 671 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : str = "", lowerCamelCase : bool = False ):
'''simple docstring'''
# Mapping from the first character of the prefix of the node
lowercase... | 671 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswerin... | 671 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.uti... | 671 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask... | 671 |
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(lowerCamelCase_ , exponent // 2 , lowerCamelCase_ ) % modulo_value
... | 671 | 1 |
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 .utils import require_elasticsearch, r... | 671 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipelin... | 671 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
A__ : List[str] = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'... | 671 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = 42
class _UpperCAmelCase ... | 671 | 1 |
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 a ( lowerCamelCase_ ):
... | 671 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
# we need a list not a string, so do something to change the type
lowercase__ = arr.split('''... | 671 | 1 |
def a ( lowerCamelCase_ ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
lowercase__ = sum(lowerCamelCase_ ) / len(lowerCamelCase_ ) # Calculate the average
return su... | 671 |
from itertools import count
def a ( lowerCamelCase_ = 50 ):
'''simple docstring'''
lowercase__ = [1] * min_block_length
for n in count(lowerCamelCase_ ):
fill_count_functions.append(1 )
for block_length in range(lowerCamelCase_ , ... | 671 | 1 |
import re
def a ( lowerCamelCase_ ):
'''simple docstring'''
return [char.split() for char in re.split(r'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = split_input(str_ ... | 671 |
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 BatchFeature
from ...utils i... | 671 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_imag... | 671 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = None
lowe... | 671 | 1 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A__ : Dict = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
... | 671 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ ... | 671 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test... | 671 |
from __future__ import annotations
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = 0.00
lowercase__ = 0
for resistor in resistors:
if resistor <= 0:
lowercase__ = F"""Resistor at index {index} has a n... | 671 | 1 |
def a ( lowerCamelCase_=2_8123 ):
'''simple docstring'''
lowercase__ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += ... | 671 |
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 torchvision.transforms.functiona... | 671 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : Dict = logging.get_logger(__name__)
A__ : Any = {
'roberta-base': 'https://huggingface.co/ro... | 671 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 671 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumul... | 671 |
import argparse
import os
import re
A__ : Optional[int] = 'src/transformers'
# Pattern that looks at the indentation in a line.
A__ : Union[str, Any] = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A__ : List[str] = re.compil... | 671 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ : str = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
'toke... | 671 |
from math import sqrt
def a ( lowerCamelCase_ ):
'''simple docstring'''
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowercase__ = True
# 0 and 1 are none primes.
... | 671 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCAmelCase ( A_... | 671 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ =... | 671 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_s... | 671 |
from functools import reduce
A__ : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648... | 671 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A__ : Union[str, Any] = logging.get_logger(__name__)
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
def __init__( self : Tuple, *lowerCamelCase : Lis... | 671 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
f... | 671 | 1 |
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if len(lowerCamelCase_ ) != len(lowerCamelCase_ ):
raise ValueError('''String lengths must match!''' )
lowercase__ = 0
for chara, chara in zip(lowerCamelCase_ , ... | 671 |
from collections import defaultdict
from math import gcd
def a ( lowerCamelCase_ = 150_0000 ):
'''simple docstring'''
lowercase__ = defaultdict(lowerCamelCase_ )
lowercase__ = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for eucli... | 671 | 1 |
import os
def a ( ):
'''simple docstring'''
lowercase__ = os.path.join(os.path.dirname(lowerCamelCase_ ) , '''num.txt''' )
with open(lowerCamelCase_ ) as file_hand:
return str(sum(int(lowerCamelCase_ ) for line in file_hand ) ... | 671 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : Dict =... | 671 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceF... | 671 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
... | 671 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.... | 671 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A__ : Dict = 50_00_00
A__ , A__ : str = os.path.split(__file__)
A__ : Optional[Any] = os.path.join(RESULTS_BASEPATH, 'res... | 671 | 1 |
import warnings
warnings.warn(
'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
'`from accelerate import find_executable_batch_size` to avoid this warning.',
FutureWarning,
)
| 671 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : str = "", lowerCamelCase : bool = False ):
'''simple docstring'''
# Mapping from the first character of the prefix of the node
lowercase... | 671 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, a... | 671 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.uti... | 671 | 1 |
from functools import reduce
A__ : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648... | 671 |
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(lowerCamelCase_ , exponent // 2 , lowerCamelCase_ ) % modulo_value
... | 671 | 1 |
import math
def a ( lowerCamelCase_ ):
'''simple docstring'''
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
... | 671 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipelin... | 671 | 1 |
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 torchvision.transforms.functiona... | 671 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = 42
class _UpperCAmelCase ... | 671 | 1 |
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowercase__ = set()
return any(
node not in visited and depth_first_search(lowerCamelCase_... | 671 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
# we need a list not a string, so do something to change the type
lowercase__ = arr.split('''... | 671 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 671 |
from itertools import count
def a ( lowerCamelCase_ = 50 ):
'''simple docstring'''
lowercase__ = [1] * min_block_length
for n in count(lowerCamelCase_ ):
fill_count_functions.append(1 )
for block_length in range(lowerCamelCase_ , ... | 671 | 1 |
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if digit_amount > 0:
return round(number - int(lowerCamelCase_ ) , lowerCamelCase_ )
return number - int(lowerCamelCase_ )
if __name__ == "__main__":
print(decimal_isol... | 671 |
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 BatchFeature
from ...utils i... | 671 | 1 |
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', level=logging.INFO
)
A__ : ... | 671 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = None
lowe... | 671 | 1 |
import math
def a ( lowerCamelCase_ ):
'''simple docstring'''
return math.sqrt(lowerCamelCase_ ) * math.sqrt(lowerCamelCase_ ) == num
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = 0
lowercase__ ... | 671 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ ... | 671 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils... | 671 |
from __future__ import annotations
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = 0.00
lowercase__ = 0
for resistor in resistors:
if resistor <= 0:
lowercase__ = F"""Resistor at index {index} has a n... | 671 | 1 |
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(lowerCamelCase_ , exponent // 2 , lowerCamelCase_ ) % modulo_value
... | 671 |
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 torchvision.transforms.functiona... | 671 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
A__ : Any = False
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
... | 671 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 671 | 1 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pre... | 671 |
import argparse
import os
import re
A__ : Optional[int] = 'src/transformers'
# Pattern that looks at the indentation in a line.
A__ : Union[str, Any] = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A__ : List[str] = re.compil... | 671 | 1 |
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 BatchFeature
from ...utils i... | 671 |
from math import sqrt
def a ( lowerCamelCase_ ):
'''simple docstring'''
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowercase__ = True
# 0 and 1 are none primes.
... | 671 | 1 |
import os
import numpy
import onnx
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = a.name
lowercase__ = b.name
lowercase__ = ''''''
lowercase__ = ''''''
lowercase__ = ... | 671 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ =... | 671 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ : List[str] = '▁'
A__ : Tuple = {'vocab_file': 'spiece.model'}
A__ :... | 671 |
from functools import reduce
A__ : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648... | 671 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ ... | 671 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
f... | 671 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ ... | 671 |
from collections import defaultdict
from math import gcd
def a ( lowerCamelCase_ = 150_0000 ):
'''simple docstring'''
lowercase__ = defaultdict(lowerCamelCase_ )
lowercase__ = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for eucli... | 671 | 1 |
from math import sqrt
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = 0
for i in range(1 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE_CASE_ ):
total += i +... | 700 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : Dict =... | 671 | 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_retribert import RetriBertTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : Op... | 701 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
... | 671 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ : Optional[Any] = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
try:
if no... | 702 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A__ : Dict = 50_00_00
A__ , A__ : str = os.path.split(__file__)
A__ : Optional[Any] = os.path.join(RESULTS_BASEPATH, 'res... | 671 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils i... | 703 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : str = "", lowerCamelCase : bool = False ):
'''simple docstring'''
# Mapping from the first character of the prefix of the node
lowercase... | 671 | 0 |
from collections.abc import Callable
import numpy as np
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = int(np.ceil((x_end - xa) / step_size ) )
... | 704 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.uti... | 671 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
A__ : Tuple = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():... | 705 |
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(lowerCamelCase_ , exponent // 2 , lowerCamelCase_ ) % modulo_value
... | 671 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
A__ : int = logging.get_logger(__name__)
def a ( )... | 706 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipelin... | 671 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeni... | 707 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = 42
class _UpperCAmelCase ... | 671 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils i... | 708 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
# we need a list not a string, so do something to change the type
lowercase__ = arr.split('''... | 671 | 0 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 709 |
from itertools import count
def a ( lowerCamelCase_ = 50 ):
'''simple docstring'''
lowercase__ = [1] * min_block_length
for n in count(lowerCamelCase_ ):
fill_count_functions.append(1 )
for block_length in range(lowerCamelCase_ , ... | 671 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCol... | 710 |
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 BatchFeature
from ...utils i... | 671 | 0 |
from __future__ import annotations
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any], lowerCamelCase : str, lowerCamelCase : str ):
'''simple docstring'''
lowercase__ = text, pattern
lowercas... | 711 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = None
lowe... | 671 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVe... | 712 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ ... | 671 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
Dis... | 713 |
from __future__ import annotations
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = 0.00
lowercase__ = 0
for resistor in resistors:
if resistor <= 0:
lowercase__ = F"""Resistor at index {index} has a n... | 671 | 0 |
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
A__ : List[str] = '.'
# Internal TensorFlow ops that c... | 714 |
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 torchvision.transforms.functiona... | 671 | 0 |
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase__ = mf_knapsack(i - 1 , __A , __A , ... | 715 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 671 | 0 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import... | 716 |
import argparse
import os
import re
A__ : Optional[int] = 'src/transformers'
# Pattern that looks at the indentation in a line.
A__ : Union[str, Any] = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A__ : List[str] = re.compil... | 671 | 0 |
import numpy as np
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Dict ):
'''simple docstring'''
lowercase__ = (0, 0)
lowercase__ = None
lowercase__ = 0
lowercase__ = 0
... | 717 |
from math import sqrt
def a ( lowerCamelCase_ ):
'''simple docstring'''
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowercase__ = True
# 0 and 1 are none primes.
... | 671 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ : List[str] = logging.get_logger(__name__)
A__ : Optional[int] = {"vocab_file": "vocab.json"}
A__ : ... | 718 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ =... | 671 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoT... | 719 |
from functools import reduce
A__ : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648... | 671 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__ : Union[str, Any] = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config"""... | 720 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
f... | 671 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
... | 721 |
from collections import defaultdict
from math import gcd
def a ( lowerCamelCase_ = 150_0000 ):
'''simple docstring'''
lowercase__ = defaultdict(lowerCamelCase_ )
lowercase__ = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for eucli... | 671 | 0 |
import argparse
import copy
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = {}
with open(snake_case__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
lowercase__ = []
... | 700 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : Dict =... | 671 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
A__ : int = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
A__ : Any = typing.Union[np.floataa, int, float] # noqa: UP007
def a ( l... | 701 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
... | 671 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWi... | 702 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A__ : Dict = 50_00_00
A__ , A__ : str = os.path.split(__file__)
A__ : Optional[Any] = os.path.join(RESULTS_BASEPATH, 'res... | 671 | 0 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_confi... | 703 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : str = "", lowerCamelCase : bool = False ):
'''simple docstring'''
# Mapping from the first character of the prefix of the node
lowercase... | 671 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
A__ : Any = logging.get_logger(__name__)
A__ : List[Any] = [
["attention", "attn"],
["encoder... | 704 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.uti... | 671 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from tran... | 705 |
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(lowerCamelCase_ , exponent // 2 , lowerCamelCase_ ) % modulo_value
... | 671 | 0 |
from abc import ABC, abstractmethod
from typing import List, Optional
class _UpperCAmelCase ( __a ):
"""simple docstring"""
def __init__( self : Dict ):
'''simple docstring'''
self.test()
def lowercase__ ( self : Optional[Any] ):
... | 706 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipelin... | 671 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase_... | 707 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = 42
class _UpperCAmelCase ... | 671 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A__ : List[str] = logging.get_logger(__name__)
A__ : int = {
"shi-labs/nat-mini-in1k-224": "h... | 708 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
# we need a list not a string, so do something to change the type
lowercase__ = arr.split('''... | 671 | 0 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class _UpperCAmelCase ( a... | 709 |
from itertools import count
def a ( lowerCamelCase_ = 50 ):
'''simple docstring'''
lowercase__ = [1] * min_block_length
for n in count(lowerCamelCase_ ):
fill_count_functions.append(1 )
for block_length in range(lowerCamelCase_ , ... | 671 | 0 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_t... | 710 |
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 BatchFeature
from ...utils i... | 671 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A__ : str = logging.get_logger(__name__)
A__ : int ... | 711 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = None
lowe... | 671 | 0 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.... | 712 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ ... | 671 | 0 |
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def a ( lowerCamelCase_... | 713 |
from __future__ import annotations
def a ( lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = 0.00
lowercase__ = 0
for resistor in resistors:
if resistor <= 0:
lowercase__ = F"""Resistor at index {index} has a n... | 671 | 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
A__ : Tuple = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: ... | 714 |
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 torchvision.transforms.functiona... | 671 | 0 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( lowercase__ ,unittest.TestCase ):
"""simple docstring"""
lowercase__ ... | 715 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 671 | 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.... | 716 |
import argparse
import os
import re
A__ : Optional[int] = 'src/transformers'
# Pattern that looks at the indentation in a line.
A__ : Union[str, Any] = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A__ : List[str] = re.compil... | 671 | 0 |
from __future__ import annotations
import math
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if len(lowerCamelCase_ ) != 2 or len(a[0] ) != 2 or len(lowerCamelCase_ ) != 2 or len(b[0] ) != 2:
raise Exception('''Mat... | 717 |
from math import sqrt
def a ( lowerCamelCase_ ):
'''simple docstring'''
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
lowercase__ = True
# 0 and 1 are none primes.
... | 671 | 0 |
'''simple docstring'''
A__ : Dict = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'datac... | 718 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ =... | 671 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a ( lowerCamelCase_ ):
'''simple docstring'''
for param in module.parameters():
lowercase__ = False
def a ( ):
'''simple docstring'''
l... | 719 |
from functools import reduce
A__ : Union[str, Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648... | 671 | 0 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@req... | 720 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
f... | 671 | 0 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
A__ : Dict = 20_48
A__ : Dict = 40_96
A__ : List[Any] = 42
A__ : List[str] = os.environ.pop('PROCESS_TRAIN', 'false')
A__ : Optional[int] = {"nu... | 721 |
from collections import defaultdict
from math import gcd
def a ( lowerCamelCase_ = 150_0000 ):
'''simple docstring'''
lowercase__ = defaultdict(lowerCamelCase_ )
lowercase__ = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for eucli... | 671 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__ : int = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'C... | 700 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : Dict =... | 671 | 0 |
from PIL import Image
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
def brightness(lowerCamelCase_ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('''level must be between -255.0... | 701 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
... | 671 | 0 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_to... | 702 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A__ : Dict = 50_00_00
A__ , A__ : str = os.path.split(__file__)
A__ : Optional[Any] = os.path.join(RESULTS_BASEPATH, 'res... | 671 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before t... | 703 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : str = "", lowerCamelCase : bool = False ):
'''simple docstring'''
# Mapping from the first character of the prefix of the node
lowercase... | 671 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A__ : Any = 1_00
A__ : List[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A__ : Dict = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
... | 704 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.uti... | 671 | 0 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
fr... | 705 |
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(lowerCamelCase_ , exponent // 2 , lowerCamelCase_ ) % modulo_value
... | 671 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : int = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
r... | 706 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipelin... | 671 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
A__ : str = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex a... | 707 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = 42
class _UpperCAmelCase ... | 671 | 0 |
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