



'''simple docstring'''




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 				lowerCAmelCase_   (	_lowerCamelCase:   List[Any]       ,      _lowerCamelCase:   Dict       ,      _lowerCamelCase:   int       ,      _lowerCamelCase:   List[str]					):
		if isinstance(_lowerCamelCase       ,      _lowerCamelCase					):
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		np.full((len(_lowerCamelCase					), sequence_length, 2)       ,      _lowerCamelCase					)
		else:
				__SCREAMING_SNAKE_CASE       :		str           =		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[Any]           =		tensor[:sequence_length]
						else:
								__SCREAMING_SNAKE_CASE       :		Dict           =		tensor[:sequence_length]
				else:
						if isinstance(_lowerCamelCase       ,      _lowerCamelCase					):
								__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tensor[:sequence_length]
						else:
								__SCREAMING_SNAKE_CASE       :		int           =		tensor[:sequence_length]

		return out_tensor.tolist()


def 				lowerCAmelCase_   (	_lowerCamelCase:   Optional[int]					):
		__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 >= 1_23 and cp <= 1_26):
				return True
		__SCREAMING_SNAKE_CASE       :		str           =		unicodedata.category(_lowerCamelCase					)
		if cat.startswith("""P"""					):
				return True
		return False






@dataclass
class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		PreTrainedTokenizerBase
			_A      :		Union[bool, str, PaddingStrategy]			    =		True
			_A      :		Optional[int]			    =		None
			_A      :		Optional[int]			    =		None
			_A      :		int			    =		-100
			_A      :		str			    =		"pt"

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					import torch

					__SCREAMING_SNAKE_CASE       :		Any           =		"""label""" if """label""" in features[0].keys() else """labels"""
					__SCREAMING_SNAKE_CASE       :		int           =		[feature[label_name] for feature in features] if label_name in features[0].keys() else None
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		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

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

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

					return batch




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list					):
		if any(not isinstance(_lowerCamelCase       ,      _lowerCamelCase					) or x < 0 for x in sequence					):
				raise TypeError("""Sequence must be list of non-negative integers"""					)
		for _ in range(len(_lowerCamelCase					)					):
				for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCamelCase       ,      sequence[1:]					)					):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
						assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
						assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]