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    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		):
						if isinstance(lowerCAmelCase__	,						lowerCAmelCase__		):
												SCREAMING_SNAKE_CASE							:					Tuple           =							np.full((len(lowerCAmelCase__		), sequence_length, 2)	,						lowerCAmelCase__		)
						else:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.full((len(lowerCAmelCase__		), sequence_length)	,						lowerCAmelCase__		)

						for i, tensor in enumerate(lowerCAmelCase__		):
												if padding_side == "right":
																		if isinstance(lowerCAmelCase__	,						lowerCAmelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[str]           =							tensor[:sequence_length]
																		else:
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							tensor[:sequence_length]
												else:
																		if isinstance(lowerCAmelCase__	,						lowerCAmelCase__		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tensor[:sequence_length]
																		else:
																								SCREAMING_SNAKE_CASE							:					str           =							tensor[:sequence_length]

						return out_tensor.tolist()
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							ord(lowerCAmelCase__		)
						if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
												return True
						SCREAMING_SNAKE_CASE							:					Any           =							unicodedata.category(lowerCAmelCase__		)
						if cat.startswith('''P'''		):
												return True
						return False





@dataclass
class       lowercase__		(     __SCREAMING_SNAKE_CASE):
						UpperCamelCase_  =	42
						UpperCamelCase_  =	True
						UpperCamelCase_  =	None
						UpperCamelCase_  =	None
						UpperCamelCase_  =	-100
						UpperCamelCase_  =	"pt"


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												import torch

												SCREAMING_SNAKE_CASE							:					List[str]           =							'''label''' if '''label''' in features[0].keys() else '''labels'''
												SCREAMING_SNAKE_CASE							:					List[Any]           =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.tokenizer.pad(
												    _a					,	padding=self.padding					,	max_length=self.max_length					,	pad_to_multiple_of=self.pad_to_multiple_of					,	return_tensors='''pt''' if labels is None else None					,	)

												if labels is None:
																		return batch

												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.tensor(batch['''entity_ids''']		).shape[1]
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.tokenizer.padding_side
												if padding_side == "right":
																		SCREAMING_SNAKE_CASE							:					int           =							[
																		    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
																		]
												else:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							[
																		    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
																		]

												SCREAMING_SNAKE_CASE							:					int           =							[feature['''ner_tags'''] for feature in features]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							padding_tensor(_a					,	-1					,	_a					,	_a		)
												SCREAMING_SNAKE_CASE							:					Any           =							[feature['''original_entity_spans'''] for feature in features]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							padding_tensor(_a					,	(-1, -1)					,	_a					,	_a		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							{k: torch.tensor(_a					,	dtype=torch.intaa		) for k, v in batch.items()}

												return batch






# 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 applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCamelCase	: Dict    		=  [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
						while ask_again:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input(_lowercase		)
												try:
																		if default is not None and len(_lowercase		) == 0:
																								return default
																		return convert_value(_lowercase		) if convert_value is not None else result
												except Exception:
																		if error_message is not None:
																								print(_lowercase		)
def    A			(					_lowercase	,						_lowercase=[]	,						_lowercase=None	,						_lowercase=0		):
						SCREAMING_SNAKE_CASE							:					Dict           =							BulletMenu(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							menu.run(default_choice=_lowercase		)
						return convert_value(_lowercase		) if convert_value is not None else result
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							int(_lowercase		)
						return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return DynamoBackend(DYNAMO_BACKENDS[value]		).value
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]		)
def    A			(					_lowercase		):
						return {"yes": True, "no": False}[value.lower()]





class       lowercase__		(     argparse.RawDescriptionHelpFormatter):


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							super()._format_usage(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							usage.replace('''<command> [<args>] '''					,	''''''		)
												return usage





