code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
UpperCamelCase_ = logging.get_logger(__name__)
def lowerCamelCase_ ( _a : Any , _a : str ):
... | 362 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger('''transformers.models.speecht5''')
def lowerCamelCase_ ( _a : str , _a : int , _... | 59 | 0 |
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 import version
from .. import __ve... | 363 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowerCamelCase_ ( _a : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def ... | 59 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCa... | 364 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 59 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ ... | 365 |
import os
def lowerCamelCase_ ( _a : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_a ) , _a ) ) as input_file:
UpperCAmelCase_ : Dict = [
[int(_a ) for element in line.split(""",""" )]
... | 59 | 0 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
f... | 366 |
# Copyright 2023 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 appli... | 59 | 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_mobilebert import MobileBertTokenizer
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ ... | 367 |
def lowerCamelCase_ ( _a : int , _a : list[int] , _a : int ):
'''simple docstring'''
def count_of_possible_combinations(_a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_... | 59 | 0 |
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=__snake_case ):
'''simple docstring'''
A__ : List[Any] = ["keras_nlp"]
def __init__( self: int ,*lowerCamelCase_: Optional[int] ,**lowerCamelCase_: ... | 368 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''junnyu/roformer_chinese_small''': '''https://... | 59 | 0 |
import math
def lowerCamelCase_ ( _a : list , _a : int = 0 , _a : int = 0 ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = end or len(_a )
for i in range(_a , _a ):
UpperCAmelCase_ : str = i... | 369 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoC... | 59 | 0 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase_ = ['''small''', '''medium''', '''large''']
UpperCamelCase_ = '''lm_head.decoder.weight'''
UpperCamelCase_ = '''lm_head.weight'''
def lowerCamelCase_ ( _a : str , ... | 370 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils... | 59 | 0 |
import os
from datetime import datetime as dt
from github import Github
UpperCamelCase_ = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def lowerCamelCa... | 371 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 59 | 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_ = {
'''configurat... | 350 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 59 | 0 |
from scipy.stats import spearmanr
import datasets
UpperCamelCase_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply t... | 351 |
from scipy.stats import spearmanr
import datasets
UpperCamelCase_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 59 | 0 |
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 lowerCamelCase_ ( _a : Dataset , _a : Dict[str, str] ):
'''simple docstring'''
Upp... | 352 |
def lowerCamelCase_ ( _a : int = 50 ):
'''simple docstring'''
UpperCAmelCase_ : Dict = [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 ... | 59 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _snake_case :
'''simple docstring'''
A__ : float
A__ : TreeNode | None = None
A__ : TreeNode | None = None
def lowerCamelCase_ ( _a : Tree... | 353 |
def lowerCamelCase_ ( _a : int ):
'''simple docstring'''
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_a , _a ):
raise TypeError("""Input value must be a 'int' type""" )
return bin(_a ).count("""1""" )
if... | 59 | 0 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
requi... | 354 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 59 | 0 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCamelCase_ ( _a : str , _a : float | Decimal , _a : float = 10**-10 ):
'''simple docstring'''
UpperCAmelCase_ : Tuple ... | 355 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''shi-labs/dinat-mini-in1k-224''': '''http... | 59 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCamelCase_ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
Uppe... | 356 |
import random
from typing import Any
def lowerCamelCase_ ( _a : list ):
'''simple docstring'''
for _ in range(len(_a ) ):
UpperCAmelCase_ : List[str] = random.randint(0 , len(_a ) - 1 )
UpperCAmelCase_ : Any = r... | 59 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _snake_case :
'''simple docstring'''
pass
| 357 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that ... | 59 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequence... | 358 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase_ = ['''small''', '''medium''', '''large''']
UpperCamelCase_ = '''lm_head.decoder.weight'''
UpperCamelCase_ = '''lm_head.weight'''
def lowerCamelCase_ ( _a : str , ... | 59 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoC... | 359 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 59 | 0 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase_ = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and ... | 360 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar('''T''')
UpperCamelCase_ = TypeVar('''U''')
class _snake_case ( Generic[T, U] ):
'''simple docstring'''
def __init__( ... | 59 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( __snake_case ):
'''simple docstring'''
A__ : int = ["image_processor", "tokenizer"]
A__ : int = "AutoImageProcessor"
A__ :... | 361 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase_ = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and fa... | 59 | 0 |
import operator as op
UpperCamelCase_ = '''scaler.pt'''
UpperCamelCase_ = '''pytorch_model'''
UpperCamelCase_ = '''random_states'''
UpperCamelCase_ = '''optimizer'''
UpperCamelCase_ = '''scheduler'''
UpperCamelCase_ = '''pytorch_model.bin'''
UpperCamelCase_ ... | 362 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger('''transformers.models.speecht5''')
def lowerCamelCase_ ( _a : str , _a : int , _... | 59 | 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.... | 363 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowerCamelCase_ ( _a : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def ... | 59 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowerCamelCase_ ( _a : str , _a : str , **_a : Any ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = AutoConfig.from_pretrained(_a , **... | 364 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 59 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _snake_case :
'''simple docstring'''
A__ : int
A__ : TreeNode | None = None
A__ : TreeNode | None =... | 365 |
import os
def lowerCamelCase_ ( _a : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_a ) , _a ) ) as input_file:
UpperCAmelCase_ : Dict = [
[int(_a ) for element in line.split(""",""" )]
... | 59 | 0 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
... | 366 |
# Copyright 2023 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 appli... | 59 | 0 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def lowe... | 367 |
def lowerCamelCase_ ( _a : int , _a : list[int] , _a : int ):
'''simple docstring'''
def count_of_possible_combinations(_a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_... | 59 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...token... | 368 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''junnyu/roformer_chinese_small''': '''https://... | 59 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvai... | 369 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoC... | 59 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
'''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100OnnxConfig'''],
'''tokenizati... | 370 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils... | 59 | 0 |
from manim import *
class _snake_case ( __snake_case ):
'''simple docstring'''
def A__ ( self: List[str] ) -> Optional[Any]:
UpperCAmelCase_ : int = Rectangle(height=0.5 ,width=0.5 )
UpperCAmelCase_ :... | 371 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 59 | 0 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _snake_case :
'''simple docstring'''
def __init__( self: Dict ) -> Any:
UpperCAmelCase_ : Optional[Any] = """"""... | 350 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 59 | 0 |
import math
import unittest
def lowerCamelCase_ ( _a : int ):
'''simple docstring'''
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number... | 351 |
from scipy.stats import spearmanr
import datasets
UpperCamelCase_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 59 | 0 |
# Copyright 2023 The HuggingFace Inc. 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 ... | 352 |
def lowerCamelCase_ ( _a : int = 50 ):
'''simple docstring'''
UpperCAmelCase_ : Dict = [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 ... | 59 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase_ ( _a : Optional[int] ):
'''simple docstring'''
monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings""" , set() )
@pytest.fix... | 353 |
def lowerCamelCase_ ( _a : int ):
'''simple docstring'''
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_a , _a ):
raise TypeError("""Input value must be a 'int' type""" )
return bin(_a ).count("""1""" )
if... | 59 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = R'''
Args:
input_ids (`torch.LongTensor` of shape ... | 354 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 59 | 0 |
import qiskit
def lowerCamelCase_ ( _a : int , _a : int ):
'''simple docstring'''
UpperCAmelCase_ : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
UpperCAmelCase_ : Tuple ... | 355 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''shi-labs/dinat-mini-in1k-224''': '''http... | 59 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( _a : list , _a : int , _a : int , _a : int ):
UpperCAmelCase_ : Tuple = []
UpperCAmelCase_ : Optional[int] = input_list[low:mid], input_list[mid : high + 1]
w... | 356 |
import random
from typing import Any
def lowerCamelCase_ ( _a : list ):
'''simple docstring'''
for _ in range(len(_a ) ):
UpperCAmelCase_ : List[str] = random.randint(0 , len(_a ) - 1 )
UpperCAmelCase_ : Any = r... | 59 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase_ = {
'''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Perceiver... | 357 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that ... | 59 | 0 |
# Copyright 2023 The HuggingFace Inc. 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 ... | 358 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase_ = ['''small''', '''medium''', '''large''']
UpperCamelCase_ = '''lm_head.decoder.weight'''
UpperCamelCase_ = '''lm_head.weight'''
def lowerCamelCase_ ( _a : str , ... | 59 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin... | 359 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 59 | 0 |
import random
from typing import Any
def lowerCamelCase_ ( _a : list ):
'''simple docstring'''
for _ in range(len(_a ) ):
UpperCAmelCase_ : List[str] = random.randint(0 , len(_a ) - 1 )
UpperCAmelCase_ : Any = ... | 360 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar('''T''')
UpperCamelCase_ = TypeVar('''U''')
class _snake_case ( Generic[T, U] ):
'''simple docstring'''
def __init__( ... | 59 | 0 |
class _snake_case :
'''simple docstring'''
def __init__( self: Union[str, Any] ,lowerCamelCase_: list[int] ) -> None:
UpperCAmelCase_ : Optional[int] = len(lowerCamelCase_ )
UpperCAmelCase_ : Any = [0... | 361 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase_ = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and fa... | 59 | 0 |
import torch
from diffusers import StableDiffusionPipeline
UpperCamelCase_ = '''path-to-your-trained-model'''
UpperCamelCase_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
UpperCamelCase_ = '''A photo of sks dog in a bucket'''
UpperCam... | 362 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger('''transformers.models.speecht5''')
def lowerCamelCase_ ( _a : str , _a : int , _... | 59 | 0 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
UpperCamelCas... | 363 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowerCamelCase_ ( _a : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def ... | 59 | 0 |
from ...configuration_utils import PretrainedConfig
class _snake_case ( __snake_case ):
'''simple docstring'''
A__ : Tuple = "bert-generation"
def __init__( self: str ,lowerCamelCase_: Any=50358 ,lowerCamelCase_: List[str]=1024 ... | 364 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 59 | 0 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _snake_case ( __snake_case ):
'''simple docstring'''
def A__ ( self: List[str] ,lowerCamelCase_: str ) -... | 365 |
import os
def lowerCamelCase_ ( _a : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_a ) , _a ) ) as input_file:
UpperCAmelCase_ : Dict = [
[int(_a ) for element in line.split(""",""" )]
... | 59 | 0 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that ... | 366 |
# Copyright 2023 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 appli... | 59 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar('''T''')
def lowerCamelCase_ ( _a : int ):
'''simple docstring'''
return (position - 1) // 2
def lowerCamelCase_ ( _a : int ... | 367 |
def lowerCamelCase_ ( _a : int , _a : list[int] , _a : int ):
'''simple docstring'''
def count_of_possible_combinations(_a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_... | 59 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INP... | 368 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''junnyu/roformer_chinese_small''': '''https://... | 59 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_co... | 369 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoC... | 59 | 0 |
from __future__ import annotations
import queue
class _snake_case :
'''simple docstring'''
def __init__( self: Optional[Any] ,lowerCamelCase_: Union[str, Any] ) -> str:
UpperCAmelCase_ : Optional[Any] = data
UpperCAm... | 370 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils... | 59 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCamelCase_ = HfApi()
UpperCamelCase_ = {}
# fmt: off
UpperCamelCase_ = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
... | 371 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 59 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ ( _a : list[int] , _a : int ):
'''simple docstring'''
UpperCAmelCase_ : list[list[int]] = []
UpperCAmelCase_ : list[int] = []
UpperCAmelCa... | 350 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 59 | 0 |
import argparse
from collections import defaultdict
import yaml
UpperCamelCase_ = '''docs/source/en/_toctree.yml'''
def lowerCamelCase_ ( _a : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase_ : Dict = defaultdict(_a )
UpperCAmelCase_ ... | 351 |
from scipy.stats import spearmanr
import datasets
UpperCamelCase_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 59 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTe... | 352 |
def lowerCamelCase_ ( _a : int = 50 ):
'''simple docstring'''
UpperCAmelCase_ : Dict = [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 ... | 59 | 0 |
def lowerCamelCase_ ( _a : int = 1000 ):
'''simple docstring'''
UpperCAmelCase_ : int = 2**power
UpperCAmelCase_ : str = 0
while n:
UpperCAmelCase_ : Tuple = r + n % 10, n // 10
return r
if __name__ == "__main__":
p... | 353 |
def lowerCamelCase_ ( _a : int ):
'''simple docstring'''
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_a , _a ):
raise TypeError("""Input value must be a 'int' type""" )
return bin(_a ).count("""1""" )
if... | 59 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
UpperCamelCase_ = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECHT5_PRETRAI... | 354 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 59 | 0 |
from __future__ import annotations
UpperCamelCase_ = list[list[int]]
# assigning initial values to the grid
UpperCamelCase_ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
... | 355 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''shi-labs/dinat-mini-in1k-224''': '''http... | 59 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
UpperCamelCase_ = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', '''we... | 356 |
import random
from typing import Any
def lowerCamelCase_ ( _a : list ):
'''simple docstring'''
for _ in range(len(_a ) ):
UpperCAmelCase_ : List[str] = random.randint(0 , len(_a ) - 1 )
UpperCAmelCase_ : Any = r... | 59 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10... | 357 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that ... | 59 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( _a : list[int] ):
'''simple docstring'''
return len(set(_a ) ) == len(_a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 358 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase_ = ['''small''', '''medium''', '''large''']
UpperCamelCase_ = '''lm_head.decoder.weight'''
UpperCamelCase_ = '''lm_head.weight'''
def lowerCamelCase_ ( _a : str , ... | 59 | 0 |
import math
def lowerCamelCase_ ( _a : float , _a : float ):
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle... | 359 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 59 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def lowerCamelCase_ ( _a : float ):
'''simple docstring'''
if num <= 0:
raise ValueError("""math domain error""" )
return quad(_a , 0 , _a , args=(_a) )[0]
def lowerC... | 360 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar('''T''')
UpperCamelCase_ = TypeVar('''U''')
class _snake_case ( Generic[T, U] ):
'''simple docstring'''
def __init__( ... | 59 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 361 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase_ = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and fa... | 59 | 0 |
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,
blenderbot,
blend... | 362 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger('''transformers.models.speecht5''')
def lowerCamelCase_ ( _a : str , _a : int , _... | 59 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvai... | 363 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowerCamelCase_ ( _a : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def ... | 59 | 0 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
UpperCamelCase_ = get_tests_dir() +... | 364 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 59 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
... | 365 |
import os
def lowerCamelCase_ ( _a : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_a ) , _a ) ) as input_file:
UpperCAmelCase_ : Dict = [
[int(_a ) for element in line.split(""",""" )]
... | 59 | 0 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def lowerCamelCase_ ... | 366 |
# Copyright 2023 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 appli... | 59 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = 9, 14 # noqa: F841
UpperCAmelCase_ : Optional[int] ... | 367 |
def lowerCamelCase_ ( _a : int , _a : list[int] , _a : int ):
'''simple docstring'''
def count_of_possible_combinations(_a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_... | 59 | 0 |
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_tokenizers, require_vision
from transformers.util... | 368 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''junnyu/roformer_chinese_small''': '''https://... | 59 | 0 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _snake_case ( __snake_case ):
A__ : ... | 369 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoC... | 59 | 0 |
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
import jax.numpy as jnp
... | 370 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils... | 59 | 0 |
def lowerCamelCase_ ( _a : str ):
'''simple docstring'''
return "".join(chr(ord(_a ) - 32 ) if """a""" <= char <= """z""" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 371 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 59 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _snake_case ( __snake_case ):
'''simple docstring'''
def __init__( self: Optional[int] ,lowerCamelCase_: List[str] ,lower... | 350 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 59 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCamelCase_ = 50000
UpperCamelCase_ = 5000
UpperCamelCase_ ,UpperCamelCase_ = os.path.split(__file__)
UpperCamelCase_ = os.path.join(RESULTS_BASEPATH, '''resul... | 351 |
from scipy.stats import spearmanr
import datasets
UpperCamelCase_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 59 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import... | 352 |
def lowerCamelCase_ ( _a : int = 50 ):
'''simple docstring'''
UpperCAmelCase_ : Dict = [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 ... | 59 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 353 |
def lowerCamelCase_ ( _a : int ):
'''simple docstring'''
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_a , _a ):
raise TypeError("""Input value must be a 'int' type""" )
return bin(_a ).count("""1""" )
if... | 59 | 0 |
def lowerCamelCase_ ( _a : int ):
'''simple docstring'''
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
UpperCAmelCase_ : List[Any] = 4
UpperCAmelCase_ : Optional[int] = (... | 354 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 59 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase_ = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'''tokenization_biogpt''': ['''BioGptTo... | 355 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''shi-labs/dinat-mini-in1k-224''': '''http... | 59 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCamelCase_ ( _a : str = "" ):
UpperCAmelCase_ : Optional[Any] = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
UpperCAmelCase_ : int ... | 356 |
import random
from typing import Any
def lowerCamelCase_ ( _a : list ):
'''simple docstring'''
for _ in range(len(_a ) ):
UpperCAmelCase_ : List[str] = random.randint(0 , len(_a ) - 1 )
UpperCAmelCase_ : Any = r... | 59 | 0 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.... | 357 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that ... | 59 | 0 |
from typing import Any
import numpy as np
def lowerCamelCase_ ( _a : np.ndarray ):
'''simple docstring'''
return np.array_equal(_a , matrix.conjugate().T )
def lowerCamelCase_ ( _a : np.ndarray , _a : np.ndarray ):
'''simple docstrin... | 358 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
UpperCamelCase_ = ['''small''', '''medium''', '''large''']
UpperCamelCase_ = '''lm_head.decoder.weight'''
UpperCamelCase_ = '''lm_head.weight'''
def lowerCamelCase_ ( _a : str , ... | 59 | 0 |
import numpy as np
from PIL import Image
def lowerCamelCase_ ( _a : np.ndarray , _a : int , _a : int ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = np.array(_a )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""... | 359 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 59 | 0 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 360 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar('''T''')
UpperCamelCase_ = TypeVar('''U''')
class _snake_case ( Generic[T, U] ):
'''simple docstring'''
def __init__( ... | 59 | 0 |
UpperCamelCase_ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
... | 361 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase_ = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and fa... | 59 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...... | 362 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger('''transformers.models.speecht5''')
def lowerCamelCase_ ( _a : str , _a : int , _... | 59 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_commo... | 363 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowerCamelCase_ ( _a : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def ... | 59 | 0 |
def lowerCamelCase_ ( _a : int , _a : list[int] , _a : int ):
'''simple docstring'''
def count_of_possible_combinations(_a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_... | 364 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 59 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _snake_case ( unittest.TestCase ):
'... | 365 |
import os
def lowerCamelCase_ ( _a : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_a ) , _a ) ) as input_file:
UpperCAmelCase_ : Dict = [
[int(_a ) for element in line.split(""",""" )]
... | 59 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ : int = logging.get_logger(__name__)
UpperCamelCase_ : str = {
'''vocab_file''': '''vocab.json'... | 366 |
# Copyright 2023 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 appli... | 59 | 0 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCamelCase_ = logging.getLogger(__name__)
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = argp... | 367 |
def lowerCamelCase_ ( _a : int , _a : list[int] , _a : int ):
'''simple docstring'''
def count_of_possible_combinations(_a : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_... | 59 | 0 |
def lowerCamelCase_ ( _a : List[str] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = len(_a )
while cur > 1:
# Find the maximum number in arr
UpperCAmelCase_ : Tuple = arr.index(max(arr[0:cur] ) )
# R... | 368 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''junnyu/roformer_chinese_small''': '''https://... | 59 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 369 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoC... | 59 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
'''configuration_blenderbot_small''': [
'''BLENDERBOT_SMALL_PRET... | 370 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils... | 59 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See all CANINE models at https://huggingfac... | 371 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 59 | 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, NewT... | 60 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, lo... | 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLI... | 60 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
i... | 60 | 1 |
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