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 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''tokeni... | 702 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 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
#
# Unl... | 703 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 652 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
fro... | 704 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 652 | 0 |
from __future__ import annotations
from math import pow, sqrt
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance == 0:
ret... | 705 |
lowercase_ : Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/tr... | 652 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
lowercase_ : Union[str, Any] = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and S... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''to... | 652 | 0 |
# 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, ... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 652 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ : Any = logging.get_logger(__na... | 708 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 652 | 0 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def A__( __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : List[str] = k_siz... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ : Optional[Any] = {'''configuration_refor... | 710 |
import itertools
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 652 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : List[str] = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
_UpperCamelCase : Optional[int] = "timm_backbone"
... | 711 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbar... | 652 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import Pr... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 0 |
import math
import random
def A__( __lowerCAmelCase , __lowerCAmelCase = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowercase_ : Union[str, Any] = 0.02
def A__( __lowerCAmelCase , __lowerCAmelCase ):
... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : List[str] = logging.get_logger(__name__)
lowercase_ : Tuple = {
'''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolv... | 714 |
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All prime... | 652 | 0 |
def A__( __lowerCAmelCase ):
_snake_case : list[list[float]] = []
for data in source_data:
for i, el in enumerate(__lowerCAmelCase ):
if len(__lowerCAmelCase ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(__lowerCAmelCase ) )
... | 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
... | 652 | 0 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
UpperCamelCase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
UpperCamelCase_ : List[An... | 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 652 | 0 |
'''simple docstring'''
import qiskit
def A__( __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Optional[Any] = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
_snake_case : List[str] = qiskit.Quant... | 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_ti... | 652 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase_ : List[Any] = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
lowercase_ : Union[str, Any] = _LazyModul... | 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
D... | 652 | 0 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = ... | 652 | 0 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transfo... | 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 652 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transforme... | 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 652 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attenti... | 700 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple docstri... | 652 | 0 |
import string
def A__( __lowerCAmelCase ):
for key in range(len(string.ascii_uppercase ) ):
_snake_case : List[Any] = ''
for symbol in message:
if symbol in string.ascii_uppercase:
_snake_case : Optional[int] = string.ascii... | 701 |
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.sta... | 652 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase_ : str = logging.get_logger(__name__)
lowercase_ : List[Any] = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob... | 702 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 0 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if not arr:
return None, None, 0
if low == high:
... | 703 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 652 | 0 |
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
lowercase_ : str = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def A__( __lowerCAmelC... | 704 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 652 | 0 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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... | 705 |
lowercase_ : Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/tr... | 652 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_v... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''to... | 652 | 0 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Union[str, Any] = len(__lowerCAmelCase )
_snake_case : Union[str, Any] = len(__lowerCAmelCase )
_snake_case : Optional[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_s... | 708 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 652 | 0 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is tr... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F... | 710 |
import itertools
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 652 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowercase_ : Tuple = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']... | 711 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbar... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase ):
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
_snake_case : Optional[int] = str(bin(__lowerCAmelCase ) )
binary_number += "0" * shift_amount
return binary_number
def ... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 0 |
import argparse
import os
import re
import packaging.version
lowercase_ : Optional[int] = '''examples/'''
lowercase_ : List[Any] = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.co... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
import copy
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 ..auto import CONFIG_MAPPING
lowercase_ : Dict = logging.get_logger(__name__... | 714 |
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All prime... | 652 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, ... | 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
... | 652 | 0 |
def A__( __lowerCAmelCase ):
_snake_case : List[Any] = [0] * len(__lowerCAmelCase )
for i in range(1 , len(__lowerCAmelCase ) ):
# use last results for better performance - dynamic programming
_snake_case : Tuple = prefix_result[i - 1]
wh... | 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 652 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Op... | 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_ti... | 652 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
D... | 652 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowercase_ : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvail... | 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = ... | 652 | 0 |
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 lowercase ( a_ ):
"""si... | 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 652 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 ConfigT... | 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 652 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCOND... | 700 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple docstri... | 652 | 0 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 701 |
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.sta... | 652 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
lowercase_ : str = {
'''t5-small''': '''https://huggingf... | 702 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 0 |
import os
from datetime import datetime as dt
from github import Github
lowercase_ : Optional[int] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip... | 703 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 652 | 0 |
class lowercase : # Public class to implement a graph
"""simple docstring"""
def __init__( self : Any , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : list[list[bool]] ):
'''simple docstring'''
... | 704 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 652 | 0 |
import unittest
import numpy as np
from datasets import load_dataset
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_imag... | 705 |
lowercase_ : Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/tr... | 652 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def A__( __lowerCAmelCase = 2_00_00_00 ):
_snake_case : list[int] = [0]
_snake_case : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
trian... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''to... | 652 | 0 |
lowercase_ : Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/tr... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 652 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( a_ ):
"""simple docstring"""
_UpperCamelCase : Union[str, Any] = (UnCLIPScheduler,)
def __UpperCAmelCase ... | 708 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 652 | 0 |
'''simple docstring'''
import itertools
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_... | 710 |
import itertools
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 652 | 0 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 711 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbar... | 652 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils im... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 0 |
from __future__ import annotations
import bisect
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0 , __lowerCAmelCase = -1 ):
if hi < 0:
_snake_case : int = len(__lowerCAmelCase )
while lo < hi:
_snake_case : Optional[int] = lo + (... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
import math
def A__( __lowerCAmelCase = 1_00 ):
_snake_case : str = sum(i * i for i in range(1 , n + 1 ) )
_snake_case : Optional[Any] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(F'''{solu... | 714 |
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All prime... | 652 | 0 |
import math
import unittest
def A__( __lowerCAmelCase ):
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
elif number < 2 or number % 2 == 0 o... | 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
... | 652 | 0 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple do... | 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 652 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Union[str, Any] = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_ti... | 652 | 0 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowercase_ : Tuple = get_logger(__name__)
lowercase_ : Union[str, Any] = r'''
Args:
input_ids (`jnp.ndarray` of shape `(... | 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
D... | 652 | 0 |
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,
)
lowercase_ : Optional[Any] = {
'''c... | 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = ... | 652 | 0 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filena... | 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 652 | 0 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokeni... | 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 652 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizati... | 700 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple docstri... | 652 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, sl... | 701 |
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.sta... | 652 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokeni... | 702 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
_UpperCamelCase : float
_UpperCamelCase : TreeNode | None = None
_UpperCamelCase : TreeNode | None = None
def ... | 703 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 652 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
lowercase_ : Any = logging.get_logger(__name_... | 704 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 652 | 0 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,... | 705 |
lowercase_ : Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/tr... | 652 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase_ : List[str] = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''to... | 652 | 0 |
from itertools import count
def A__( __lowerCAmelCase = 50 ):
_snake_case : str = [1] * min_block_length
for n in count(__lowerCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(__lowerCAmelCase , n + 1 ):
for block_star... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 652 | 0 |
import random
def A__( __lowerCAmelCase ):
_snake_case : List[str] = num - 1
_snake_case : Optional[int] = 0
while s % 2 == 0:
_snake_case : List[str] = s // 2
t += 1
for _ in range(5 ):
_snake_case : Dict ... | 708 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 652 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineCon... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Union[str, Any] = {
'''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP'''... | 710 |
import itertools
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 652 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase ( a_ ):
"""simpl... | 711 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbar... | 652 | 0 |
from __future__ import annotations
import numpy as np
def A__( __lowerCAmelCase ):
_snake_case : int = np.shape(__lowerCAmelCase )
if rows != columns:
_snake_case : Tuple = (
'\'table\' has to be of square shaped array but got a '
F'''{rows}x{... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowercase_ : str = logging.get_logger(__name__)
lowercase_ : Optional[int] = Ord... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase_ : int = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : Optional[Any] , *lowerCamelCase_ : str... | 714 |
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All prime... | 652 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutpu... | 715 |
import torch
from transformers import AutoModel
class lowercase ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Dict="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
... | 652 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
UpperCamelCase_ : List[str] = TypeVar('''T''')
class lowercase ( Generic[T] ):
"""simple docstring"""
def __init__( self : str ... | 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase ( ... | 652 | 0 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:REA... | 717 |
def A__( __lowerCAmelCase ):
assert column_title.isupper()
_snake_case : List[Any] = 0
_snake_case : List[str] = len(__lowerCAmelCase ) - 1
_snake_case : Dict = 0
while index >= 0:
_snake_case : List[str] = (ord(column_ti... | 652 | 0 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None ):
if version.parse(hfh.__version__ ).release < version.parse('0.11.0' ).release:
# old versions of... | 718 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase_ : List[str] = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
D... | 652 | 0 |
def A__( __lowerCAmelCase ):
if len(__lowerCAmelCase ) < 2:
return collection
def circle_sort_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
_snake_case : Any = False
if low == high:
return swapped
... | 719 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ : Optional[int] = object()
# For specifying empty leaf dict `{}`
lowercase_ : List[Any] = ... | 652 | 0 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
class lowercase ( a_ ):
... | 720 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowercase_ : Any = lo... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if exponent == 1:
return base
if exponent % 2 == 0:
_snake_case : Tuple = _modexpt(__lowerCAmelCase , exponent // 2 , __lowerCAmelCase ) % modulo_value
return (x * x) % modulo_... | 721 |
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
_snake_case : Any = str(abs(__lowerCAmelCase ) )
_snake_case : List[str] = [list(__lowerCAmelC... | 652 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : List[str] = {
'''fa... | 700 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowercase_ : Tuple = logging.getLogger(__name__)
class lowercase ( a_ ):
"""simple docstri... | 652 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...te... | 701 |
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.sta... | 652 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
t... | 702 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 0 |
import math
def A__( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_snake_case : int = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__lowerCAmelCase )
if number < 1:
_snake_case : L... | 703 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowercase_ : str = logging.get_logger(__name__)
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : int , *low... | 652 | 0 |
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Check if the input is valid
if not len(__lowerCAmelCase ) == len(__lowerCAmelCase ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
ra... | 704 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 652 | 0 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_te... | 705 |
lowercase_ : Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/tr... | 652 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : int = logging.get_logger(__name__)
lowercase_ : Optional[int] = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/m... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Optional[Any] = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''to... | 652 | 0 |
import math
def A__( __lowerCAmelCase ):
_snake_case : Any = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__lowerCAmelCase )
def A__( __lowerCAmelCase = 1 / 1_23_45 ):
_snake_case : str = 0
_snake_case : ... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Optional[int] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 652 | 0 |
import copy
import random
from transformers import CLIPTokenizer
class lowercase ( a_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , *lowerCamelCase_ : Dict , **lowerCamelCase_ : Any ):
'''simple docs... | 708 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase_ : Optional[Any] = pytest.mark.integration
@pytest.mark.parametr... | 652 | 0 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def A__( __lowerCAmelCase ):
return input_array.reshape((input_array.size, 1) )
def A__( __lowerC... | 710 |
import itertools
import math
def A__( __lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 652 | 0 |
from math import factorial
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-neg... | 711 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbar... | 652 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class lowercase :
"""simple docstring"""
def __init__( self : List[Any] ):
'''simple docstring'''
_snake_case : list[Any] = []
_snake_case : ... | 712 |
from __future__ import annotations
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Dict = list(range(len(__lowerCAmelCase ) ) )
_snake_case : Optional[int] = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
in... | 652 | 0 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_m... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_cani... | 652 | 0 |
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