
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


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


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

snake_case :		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""",
    },
}

snake_case :		Tuple							      =  {
    """allenai/led-base-16384""": 16_384,
}





class       _snake_case					(     lowercase_				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__          =    LEDTokenizer
			UpperCamelCase__          =    ['''input_ids''', '''attention_mask''']







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

				__magic_name__		:		Dict	   =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
				if pre_tok_state.get("add_prefix_space"						,   _a		) != add_prefix_space:
					__magic_name__		:		List[str]	   =     getattr(_a						,   pre_tok_state.pop("type"		)		)
					__magic_name__		:		int	   =     add_prefix_space
					__magic_name__		:		Optional[int]	   =     pre_tok_class(**_a		)

				__magic_name__		:		Any	   =     add_prefix_space

				# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
				__magic_name__		:		Dict	   =     "post_processor"
				__magic_name__		:		Optional[int]	   =     getattr(self.backend_tokenizer						,   _a						,   _a		)
				if tokenizer_component_instance:
					__magic_name__		:		Tuple	   =     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:
						__magic_name__		:		Tuple	   =     tuple(state["sep"]		)
					if "cls" in state:
						__magic_name__		:		Tuple	   =     tuple(state["cls"]		)

					__magic_name__		:		str	   =     False

					if state.get("add_prefix_space"						,   _a		) != add_prefix_space:
						__magic_name__		:		List[str]	   =     add_prefix_space
						__magic_name__		:		Optional[int]	   =     True

					if state.get("trim_offsets"						,   _a		) != trim_offsets:
						__magic_name__		:		int	   =     trim_offsets
						__magic_name__		:		Optional[Any]	   =     True

					if changes_to_apply:
						__magic_name__		:		Any	   =     getattr(_a						,   state.pop("type"		)		)
						__magic_name__		:		str	   =     component_class(**_a		)
						setattr(self.backend_tokenizer						,   _a						,   _a		)







			@property
			# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
			def 			SCREAMING_SNAKE_CASE    (				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 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Tuple	   =     AddedToken(_a						,   lstrip=_a						,   rstrip=_a		) if isinstance(_a						,   _a		) else value
				__magic_name__		:		Dict	   =     value







			def 			SCREAMING_SNAKE_CASE    (				self						,   *_a						,   **_a		):
				__magic_name__		:		Any	   =     kwargs.get("is_split_into_words"						,   _a		)

				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(*_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   *_a						,   **_a		):
				__magic_name__		:		Tuple	   =     kwargs.get("is_split_into_words"						,   _a		)

				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(*_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Tuple	   =     self._tokenizer.model.save(_a						,   name=_a		)
				return tuple(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=None		):
				__magic_name__		:		Union[str, Any]	   =     [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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		int	   =     [self.sep_token_id]
				__magic_name__		:		Optional[int]	   =     [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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None						,   _a = PaddingStrategy.DO_NOT_PAD						,   _a = None						,   _a = None						,   ):
				__magic_name__		:		Optional[int]	   =     super()._pad(
				    encoded_inputs=_a						,   max_length=_a						,   padding_strategy=_a						,   pad_to_multiple_of=_a						,   return_attention_mask=_a						,   )

				# Load from model defaults
				if return_attention_mask is None:
					__magic_name__		:		List[str]	   =     "attention_mask" in self.model_input_names

				if return_attention_mask and "global_attention_mask" in encoded_inputs:
					__magic_name__		:		Optional[Any]	   =     encoded_inputs[self.model_input_names[0]]
					# `global_attention_mask` need to have the same length as other (sequential) inputs.
					__magic_name__		:		Tuple	   =     len(encoded_inputs["global_attention_mask"]		) != len(_a		)

					if needs_to_be_padded:
						__magic_name__		:		Optional[Any]	   =     len(_a		) - 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`
							__magic_name__		:		Union[str, Any]	   =     (
							    encoded_inputs["global_attention_mask"] + [-1] * difference
							)
						elif self.padding_side == "left":
							__magic_name__		:		Union[str, Any]	   =     [-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()




