





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       _UpperCamelCase       (a__				:Optional[Any]				,     a__				:Any				,     a__				:Any				,     a__				:Optional[int]    ):






							"""simple docstring"""





							if isinstance(a_				,     a_    ):
														UpperCamelCase__			       =     np.full((len(a_    ), sequence_length, 2)				,     a_    )
							else:
														UpperCamelCase__			       =     np.full((len(a_    ), sequence_length)				,     a_    )

							for i, tensor in enumerate(a_    ):
														if padding_side == "right":
																					if isinstance(a_				,     a_    ):
																												UpperCamelCase__			       =     tensor[:sequence_length]
																					else:
																												UpperCamelCase__			       =     tensor[:sequence_length]
														else:
																					if isinstance(a_				,     a_    ):
																												UpperCamelCase__			       =     tensor[:sequence_length]
																					else:
																												UpperCamelCase__			       =     tensor[:sequence_length]

							return out_tensor.tolist()





def       _UpperCamelCase       (a__				:Dict    ):






							"""simple docstring"""





							UpperCamelCase__			       =     ord(a_    )
							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
							UpperCamelCase__			       =     unicodedata.category(a_    )
							if cat.startswith("""P"""    ):
														return True
							return False

@dataclass
class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : PreTrainedTokenizerBase
							snake_case       : Union[bool, str, PaddingStrategy]         =    True
							snake_case       : Optional[int]         =    None
							snake_case       : Optional[int]         =    None
							snake_case       : int         =    -1_00
							snake_case       : str         =    "pt"






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														import torch

														UpperCamelCase__			       =     "label" if "label" in features[0].keys() else "labels"
														UpperCamelCase__			       =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
														UpperCamelCase__			       =     self.tokenizer.pad(
														    lowerCAmelCase__					,	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

														UpperCamelCase__			       =     torch.tensor(batch["""entity_ids"""]					).shape[1]
														UpperCamelCase__			       =     self.tokenizer.padding_side
														if padding_side == "right":
																					UpperCamelCase__			       =     [
																					    list(lowerCAmelCase__					) + [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__					)) for label in labels
																					]
														else:
																					UpperCamelCase__			       =     [
																					    [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase__					)) + list(lowerCAmelCase__					) for label in labels
																					]

														UpperCamelCase__			       =     [feature["ner_tags"] for feature in features]
														UpperCamelCase__			       =     padding_tensor(lowerCAmelCase__					,	-1					,	lowerCAmelCase__					,	lowerCAmelCase__					)
														UpperCamelCase__			       =     [feature["original_entity_spans"] for feature in features]
														UpperCamelCase__			       =     padding_tensor(lowerCAmelCase__					,	(-1, -1)					,	lowerCAmelCase__					,	lowerCAmelCase__					)
														UpperCamelCase__			       =     {k: torch.tensor(lowerCAmelCase__					,	dtype=torch.intaa					) for k, v in batch.items()}

														return batch









def       _UpperCamelCase       (a__				:str    ):






							"""simple docstring"""





							UpperCamelCase__			       =     0

							# if input_string is "aba" than new_input_string become "a|b|a"
							UpperCamelCase__			       =     """"""
							UpperCamelCase__			       =     """"""

							# append each character + "|" in new_string for range(0, length-1)
							for i in input_string[: len(a__    ) - 1]:
														new_input_string += i + "|"
							# append last character
							new_input_string += input_string[-1]

							# we will store the starting and ending of previous furthest ending palindromic
							# substring
							UpperCamelCase__       ,       UpperCamelCase__			       =     0, 0

							# length[i] shows the length of palindromic substring with center i
							UpperCamelCase__			       =     [1 for i in range(len(a__    )    )]

							# for each character in new_string find corresponding palindromic string
							UpperCamelCase__			       =     0
							for j in range(len(a__    )    ):
														UpperCamelCase__			       =     1 if j > r else min(length[l + r - j] // 2				,     r - j + 1    )
														while (
														    j - k >= 0
														    and j + k < len(a__    )
														    and new_input_string[k + j] == new_input_string[j - k]
														):
																					k += 1

														UpperCamelCase__			       =     2 * k - 1

														# does this string is ending after the previously explored end (that is r) ?
														# if yes the update the new r to the last index of this
														if j + k - 1 > r:
																					UpperCamelCase__			       =     j - k + 1  # noqa: E741
																					UpperCamelCase__			       =     j + k - 1

														# update max_length and start position
														if max_length < length[j]:
																					UpperCamelCase__			       =     length[j]
																					UpperCamelCase__			       =     j

    # create that string
							UpperCamelCase__			       =     new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
							for i in s:
														if i != "|":
																					output_string += i

							return output_string


if __name__ == "__main__":
					import doctest

					doctest.testmod()




