

import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_UpperCAmelCase		:   List[Any]										=       logging.get_logger(__name__)


_UpperCAmelCase		:   Optional[int]										=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase		:   int										=       {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_UpperCAmelCase		:   Tuple										=       {
    """allenai/led-base-16384""": 1_6384,
}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE					: int       			=			LEDTokenizer
				__SCREAMING_SNAKE_CASE					: List[str]       			=			['''input_ids''', '''attention_mask''']




				def __init__(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case="replace"      ,   snake_case="<s>"      ,   snake_case="</s>"      ,   snake_case="</s>"      ,   snake_case="<s>"      ,   snake_case="<unk>"      ,   snake_case="<pad>"      ,   snake_case="<mask>"      ,   snake_case=False      ,   snake_case=True      ,   **snake_case      ,   ):
											super().__init__(
											    snake_case      ,   snake_case      ,   tokenizer_file=snake_case      ,   errors=snake_case      ,   bos_token=snake_case      ,   eos_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   unk_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   trim_offsets=snake_case      ,   **snake_case      ,   )

											snake_case_							=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
											if pre_tok_state.get('add_prefix_space'      ,   snake_case       ) != add_prefix_space:
																		snake_case_							=    getattr(snake_case      ,   pre_tok_state.pop('type'       )       )
																		snake_case_							=    add_prefix_space
																		snake_case_							=    pre_tok_class(**snake_case       )

											snake_case_							=    add_prefix_space

											# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
											snake_case_							=    'post_processor'
											snake_case_							=    getattr(self.backend_tokenizer      ,   snake_case      ,   snake_case       )
											if tokenizer_component_instance:
																		snake_case_							=    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:
																									snake_case_							=    tuple(state['sep']       )
																		if "cls" in state:
																									snake_case_							=    tuple(state['cls']       )

																		snake_case_							=    False

																		if state.get('add_prefix_space'      ,   snake_case       ) != add_prefix_space:
																									snake_case_							=    add_prefix_space
																									snake_case_							=    True

																		if state.get('trim_offsets'      ,   snake_case       ) != trim_offsets:
																									snake_case_							=    trim_offsets
																									snake_case_							=    True

																		if changes_to_apply:
																									snake_case_							=    getattr(snake_case      ,   state.pop('type'       )       )
																									snake_case_							=    component_class(**snake_case       )
																									setattr(self.backend_tokenizer      ,   snake_case      ,   snake_case       )




				@property
				# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
				def     a							(					self       ):
											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      ,   snake_case       ):
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else value
											snake_case_							=    value




				def     a							(					self      ,   *snake_case      ,   **snake_case       ):
											snake_case_							=    kwargs.get('is_split_into_words'      ,   snake_case       )

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
																		    'to use it with pretokenized inputs.'       )

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




				def     a							(					self      ,   *snake_case      ,   **snake_case       ):
											snake_case_							=    kwargs.get('is_split_into_words'      ,   snake_case       )

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
																		    'to use it with pretokenized inputs.'       )

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




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    self._tokenizer.model.save(snake_case      ,   name=snake_case       )
											return tuple(snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case=None       ):
											snake_case_							=    [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      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [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]




				def     a							(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = PaddingStrategy.DO_NOT_PAD      ,   snake_case = None      ,   snake_case = None      ,   ):
											snake_case_							=    super()._pad(
											    encoded_inputs=snake_case      ,   max_length=snake_case      ,   padding_strategy=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_attention_mask=snake_case      ,   )

											# Load from model defaults
											if return_attention_mask is None:
																		snake_case_							=    'attention_mask' in self.model_input_names

											if return_attention_mask and "global_attention_mask" in encoded_inputs:
																		snake_case_							=    encoded_inputs[self.model_input_names[0]]
																		# `global_attention_mask` need to have the same length as other (sequential) inputs.
																		snake_case_							=    len(encoded_inputs['global_attention_mask']       ) != len(snake_case       )

																		if needs_to_be_padded:
																									snake_case_							=    len(snake_case       ) - len(encoded_inputs['global_attention_mask']       )

																									if self.padding_side == "right":
																																# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																																snake_case_							=    (
																																    encoded_inputs['global_attention_mask'] + [-1] * difference
																																)
																									elif self.padding_side == "left":
																																snake_case_							=    [-1] * difference + encoded_inputs[
																																    'global_attention_mask'
																																]
																									else:
																																raise ValueError('Invalid padding strategy:' + str(self.padding_side       )       )

											return encoded_inputs







def 					__lowerCamelCase	( ):







							'''simple docstring'''






							return [list(range(1000 - i   ,							-1000 - i   ,							-1      )      ) for i in range(1000      )]


_UpperCAmelCase		:   Union[str, Any]										=       generate_large_matrix()
_UpperCAmelCase		:   Tuple										=       (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							assert all(row == sorted(UpperCamelCase__   ,							reverse=UpperCamelCase__      ) for row in grid      )
							assert all(list(UpperCamelCase__      ) == sorted(UpperCamelCase__   ,							reverse=UpperCamelCase__      ) for col in zip(*UpperCamelCase__      )      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    len(UpperCamelCase__      ) - 1

							# Edge cases such as no values or all numbers are negative.
							if not array or array[0] < 0:
														return 0

							while right + 1 > left:
														snake_case_							=    (left + right) // 2
														snake_case_							=    array[mid]

														# Num must be negative and the index must be greater than or equal to 0.
														if num < 0 and array[mid - 1] >= 0:
																					return mid

														if num >= 0:
																					snake_case_							=    mid + 1
														else:
																					snake_case_							=    mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
							return len(UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							snake_case_							=    len(grid[0]      )

							for i in range(len(UpperCamelCase__      )      ):
														snake_case_							=    find_negative_index(grid[i][:bound]      )
														total += bound
							return (len(UpperCamelCase__      ) * len(grid[0]      )) - total





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return len([number for row in grid for number in row if number < 0]      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    0
							for row in grid:
														for i, number in enumerate(UpperCamelCase__      ):
																					if number < 0:
																												total += len(UpperCamelCase__      ) - i
																												break
							return total





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							from timeit import timeit

							print('Running benchmarks'      )
							snake_case_							=    (
							    'from __main__ import count_negatives_binary_search, '
							    'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
							)
							for func in (
							    "count_negatives_binary_search",  # took 0.7727 seconds
							    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
							    "count_negatives_brute_force",  # took 12.8160 seconds
							):
														snake_case_							=    timeit(F'''{func}(grid=grid)'''   ,							setup=UpperCamelCase__   ,							number=500      )
														print(F'''{func}() took {time:0.4f} seconds'''      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()




