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


_A   					=    logging.get_logger(__name__)


_A   					=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A   					=    {
    '''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''',
    },
}

_A   					=    {
    '''allenai/led-base-16384''': 1_6_3_8_4,
}

class lowerCamelCase		(snake_case__      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		PRETRAINED_VOCAB_FILES_MAP
					a   	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		LEDTokenizer
					a   	=		['''input_ids''', '''attention_mask''']



					def __init__(						self			:				str   , _snake_case			:				Union[str, Any]=None   , _snake_case			:				Optional[int]=None   , _snake_case			:				Union[str, Any]=None   , _snake_case			:				List[str]="replace"   , _snake_case			:				Union[str, Any]="<s>"   , _snake_case			:				Optional[Any]="</s>"   , _snake_case			:				Tuple="</s>"   , _snake_case			:				List[str]="<s>"   , _snake_case			:				str="<unk>"   , _snake_case			:				Optional[Any]="<pad>"   , _snake_case			:				Any="<mask>"   , _snake_case			:				str=False   , _snake_case			:				Dict=True   , **_snake_case			:				Optional[Any]   , )							->       Dict:
									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   , )

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

									SCREAMING_SNAKE_CASE__		  =				add_prefix_space

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

													SCREAMING_SNAKE_CASE__		  =				False

													if state.get("add_prefix_space"   , _A		) != add_prefix_space:
																	SCREAMING_SNAKE_CASE__		  =				add_prefix_space
																	SCREAMING_SNAKE_CASE__		  =				True

													if state.get("trim_offsets"   , _A		) != trim_offsets:
																	SCREAMING_SNAKE_CASE__		  =				trim_offsets
																	SCREAMING_SNAKE_CASE__		  =				True

													if changes_to_apply:
																	SCREAMING_SNAKE_CASE__		  =				getattr(_A   , state.pop("type"		)		)
																	SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				str		)							->       List[str]:
									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 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				Tuple		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_A   , lstrip=_A   , rstrip=_A		) if isinstance(_A   , _A		) else value
									SCREAMING_SNAKE_CASE__		  =				value



					def 			lowerCAmelCase_					(						self			:				str   , *_snake_case			:				Any   , **_snake_case			:				Optional[int]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				Any   , *_snake_case			:				Tuple   , **_snake_case			:				Any		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				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 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Tuple   , _snake_case			:				Any = None		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self._tokenizer.model.save(_A   , name=_A		)
									return tuple(_A		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				List[str]=None		)							->       Optional[int]:
									SCREAMING_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 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Any   , _snake_case			:				Optional[int] = None		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				[self.sep_token_id]
									SCREAMING_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 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				List[Any] = None   , _snake_case			:				List[str] = PaddingStrategy.DO_NOT_PAD   , _snake_case			:				Any = None   , _snake_case			:				List[str] = None   , )							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				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:
													SCREAMING_SNAKE_CASE__		  =				'attention_mask' in self.model_input_names

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

													if needs_to_be_padded:
																	SCREAMING_SNAKE_CASE__		  =				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`
																					SCREAMING_SNAKE_CASE__		  =				(
																					    encoded_inputs['global_attention_mask'] + [-1] * difference
																					)
																	elif self.padding_side == "left":
																					SCREAMING_SNAKE_CASE__		  =				[-1] * difference + encoded_inputs[
																					    'global_attention_mask'
																					]
																	else:
																					raise ValueError("Invalid padding strategy:" + str(self.padding_side		)		)

									return encoded_inputs





"""simple docstring"""






import sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




