



'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowercase_      :				int   					=    logging.get_logger(__name__)

lowercase_      :				Optional[Any]   					=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase_      :				Dict   					=    {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

lowercase_      :				Any   					=    {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}




class      __UpperCamelCase       (SCREAMING_SNAKE_CASE_			):
	__A			  =					VOCAB_FILES_NAMES
	__A			  =					PRETRAINED_VOCAB_FILES_MAP
	__A			  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A			  =					['''input_ids''', '''attention_mask''']
	__A			  =					RobertaTokenizer







	def __init__(    self ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase="replace" ,				_lowerCAmelCase="<s>" ,				_lowerCAmelCase="</s>" ,				_lowerCAmelCase="</s>" ,				_lowerCAmelCase="<s>" ,				_lowerCAmelCase="<unk>" ,				_lowerCAmelCase="<pad>" ,				_lowerCAmelCase="<mask>" ,				_lowerCAmelCase=False ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    Optional[Any]:
							'''simple docstring'''






							super().__init__(
							    UpperCamelCase__ ,				UpperCamelCase__ ,				tokenizer_file=UpperCamelCase__ ,				errors=UpperCamelCase__ ,				bos_token=UpperCamelCase__ ,				eos_token=UpperCamelCase__ ,				sep_token=UpperCamelCase__ ,				cls_token=UpperCamelCase__ ,				unk_token=UpperCamelCase__ ,				pad_token=UpperCamelCase__ ,				mask_token=UpperCamelCase__ ,				add_prefix_space=UpperCamelCase__ ,				trim_offsets=UpperCamelCase__ ,				**UpperCamelCase__ ,				)

							lowercase        =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
							if pre_tok_state.get("""add_prefix_space""" ,				UpperCamelCase__						) != add_prefix_space:
													lowercase        =							getattr(UpperCamelCase__ ,				pre_tok_state.pop("""type"""						)						)
													lowercase        =							add_prefix_space
													lowercase        =							pre_tok_class(**UpperCamelCase__						)

							lowercase        =							add_prefix_space

							lowercase        =							'''post_processor'''
							lowercase        =							getattr(self.backend_tokenizer ,				UpperCamelCase__ ,				UpperCamelCase__						)
							if tokenizer_component_instance:
													lowercase        =							json.loads(tokenizer_component_instance.__getstate__()						)

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																			lowercase        =							tuple(state["""sep"""]						)
													if "cls" in state:
																			lowercase        =							tuple(state["""cls"""]						)

													lowercase        =							False

													if state.get("""add_prefix_space""" ,				UpperCamelCase__						) != add_prefix_space:
																			lowercase        =							add_prefix_space
																			lowercase        =							True

													if state.get("""trim_offsets""" ,				UpperCamelCase__						) != trim_offsets:
																			lowercase        =							trim_offsets
																			lowercase        =							True

													if changes_to_apply:
																			lowercase        =							getattr(UpperCamelCase__ ,				state.pop("""type"""						)						)
																			lowercase        =							component_class(**UpperCamelCase__						)
																			setattr(self.backend_tokenizer ,				UpperCamelCase__ ,				UpperCamelCase__						)







	@property
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							if self._mask_token is None:
													if self.verbose:
																			logger.error("""Using mask_token, but it is not set yet."""						)
													return None
							return str(self._mask_token						)







	@mask_token.setter
	def   _a   (    self ,				_lowerCAmelCase						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							AddedToken(UpperCamelCase__ ,				lstrip=UpperCamelCase__ ,				rstrip=UpperCamelCase__						) if isinstance(UpperCamelCase__ ,				UpperCamelCase__						) else value
							lowercase        =							value







	def   _a   (    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    int:
							'''simple docstring'''






							lowercase        =							kwargs.get("""is_split_into_words""" ,				UpperCamelCase__						)
							assert self.add_prefix_space or not is_split_into_words, (
							    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*UpperCamelCase__ ,				**UpperCamelCase__						)







	def   _a   (    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    int:
							'''simple docstring'''






							lowercase        =							kwargs.get("""is_split_into_words""" ,				UpperCamelCase__						)

							assert self.add_prefix_space or not is_split_into_words, (
							    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*UpperCamelCase__ ,				**UpperCamelCase__						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self._tokenizer.model.save(UpperCamelCase__ ,				name=UpperCamelCase__						)
							return tuple(UpperCamelCase__						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=None						)    ->    Any:
							'''simple docstring'''






							lowercase        =							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
													return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    Any:
							'''simple docstring'''






							lowercase        =							[self.sep_token_id]
							lowercase        =							[self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]








'''simple docstring'''


def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      list[list[int]]						):

						def update_area_of_max_square(lowercase_    :      int ,  lowercase_    :      int						) -> int:
												# BASE CASE
												if row >= rows or col >= cols:
																		return 0

												lowercase        =							update_area_of_max_square(lowercase_ ,  col + 1						)
												lowercase        =							update_area_of_max_square(row + 1 ,  col + 1						)
												lowercase        =							update_area_of_max_square(row + 1 ,  lowercase_						)

												if mat[row][col]:
																		lowercase        =							1 + min([right, diagonal, down]						)
																		lowercase        =							max(largest_square_area[0] ,  lowercase_						)
																		return sub_problem_sol
												else:
																		return 0

						lowercase        =							[0]
						update_area_of_max_square(0 ,  0						)
						return largest_square_area[0]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      list[list[int]]						):

						def update_area_of_max_square_using_dp_array(
						    lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      list[list[int]]						) -> int:
												if row >= rows or col >= cols:
																		return 0
												if dp_array[row][col] != -1:
																		return dp_array[row][col]

												lowercase        =							update_area_of_max_square_using_dp_array(lowercase_ ,  col + 1 ,  lowercase_						)
												lowercase        =							update_area_of_max_square_using_dp_array(row + 1 ,  col + 1 ,  lowercase_						)
												lowercase        =							update_area_of_max_square_using_dp_array(row + 1 ,  lowercase_ ,  lowercase_						)

												if mat[row][col]:
																		lowercase        =							1 + min([right, diagonal, down]						)
																		lowercase        =							max(largest_square_area[0] ,  lowercase_						)
																		lowercase        =							sub_problem_sol
																		return sub_problem_sol
												else:
																		return 0

						lowercase        =							[0]
						lowercase        =							[[-1] * cols for _ in range(lowercase_						)]
						update_area_of_max_square_using_dp_array(0 ,  0 ,  lowercase_						)

						return largest_square_area[0]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      list[list[int]]						):
						lowercase        =							[[0] * (cols + 1) for _ in range(rows + 1						)]
						lowercase        =							0
						for row in range(rows - 1 ,  -1 ,  -1						):
												for col in range(cols - 1 ,  -1 ,  -1						):
																		lowercase        =							dp_array[row][col + 1]
																		lowercase        =							dp_array[row + 1][col + 1]
																		lowercase        =							dp_array[row + 1][col]

																		if mat[row][col] == 1:
																								lowercase        =							1 + min(lowercase_ ,  lowercase_ ,  lowercase_						)
																								lowercase        =							max(dp_array[row][col] ,  lowercase_						)
																		else:
																								lowercase        =							0

						return largest_square_area







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      list[list[int]]						):
						lowercase        =							[0] * (cols + 1)
						lowercase        =							[0] * (cols + 1)
						lowercase        =							0
						for row in range(rows - 1 ,  -1 ,  -1						):
												for col in range(cols - 1 ,  -1 ,  -1						):
																		lowercase        =							current_row[col + 1]
																		lowercase        =							next_row[col + 1]
																		lowercase        =							next_row[col]

																		if mat[row][col] == 1:
																								lowercase        =							1 + min(lowercase_ ,  lowercase_ ,  lowercase_						)
																								lowercase        =							max(current_row[col] ,  lowercase_						)
																		else:
																								lowercase        =							0
												lowercase        =							current_row

						return largest_square_area


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))


