





'''simple docstring'''



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE   =      '''▁'''

_SCREAMING_SNAKE_CASE   =      {'''vocab_file''': '''spiece.model'''}

_SCREAMING_SNAKE_CASE   =      {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

_SCREAMING_SNAKE_CASE   =      {
    '''google/pegasus-xsum''': 5_1_2,
}


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Optional[int]     =    VOCAB_FILES_NAMES

	a      :			Tuple     =    VOCAB_FILES_NAMES
	a      :			Any     =    PRETRAINED_VOCAB_FILES_MAP
	a      :			int     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a      :			Any     =    ["input_ids", "attention_mask"]


	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase="<pad>"       ,_lowerCamelCase="</s>"       ,_lowerCamelCase="<unk>"       ,_lowerCamelCase="<mask_2>"       ,_lowerCamelCase="<mask_1>"       ,_lowerCamelCase=None       ,_lowerCamelCase=103       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							None:







				'''simple docstring'''





				__lowercase			  =							offset
				if additional_special_tokens is not None:
							if not isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										raise TypeError(
										    f"additional_special_tokens should be of type {type(_lowerCamelCase	)}, but is"
										    f" {type(_lowerCamelCase	)}"	)

							__lowercase			  =							(
							    ([mask_token_sent] + additional_special_tokens)
							    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
							    else additional_special_tokens
							)
							# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
							additional_special_tokens_extended += [
							    f"<unk_{i}>" for i in range(len(_lowerCamelCase	)       ,self.offset - 1	)
							]

							if len(set(_lowerCamelCase	)	) != len(_lowerCamelCase	):
										raise ValueError(
										    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
										    f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."	)
							__lowercase			  =							additional_special_tokens_extended
				else:
							__lowercase			  =							[mask_token_sent] if mask_token_sent is not None else []
							additional_special_tokens += [f"<unk_{i}>" for i in range(2       ,self.offset	)]

				__lowercase			  =							{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    eos_token=_lowerCamelCase       ,unk_token=_lowerCamelCase       ,mask_token=_lowerCamelCase       ,pad_token=_lowerCamelCase       ,mask_token_sent=_lowerCamelCase       ,offset=_lowerCamelCase       ,additional_special_tokens=_lowerCamelCase       ,sp_model_kwargs=self.sp_model_kwargs       ,**_lowerCamelCase       ,)
				__lowercase			  =							mask_token_sent
				__lowercase			  =							vocab_file
				__lowercase			  =							spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.Load(_lowerCamelCase	)

				# add special tokens to encoder dict
				__lowercase			  =							{
				    0: self.pad_token,
				    1: self.eos_token,
				}

				if self.mask_token_sent is not None:
							self.encoder.update(
							    {
							        2: self.mask_token_sent,
							        3: self.mask_token,
							    }	)

				if self.offset > 0:
							# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
							# mask_token_sent is already added to list -> so start at 1
							self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,self.offset - 1	)}	)

				__lowercase			  =							{v: k for k, v in self.encoder.items()}


	@property
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				return len(self.sp_model	) + self.offset


	def   _UpperCAmelCase       (self	)							->							Dict[str, int]:







				'''simple docstring'''





				__lowercase			  =							{self.convert_ids_to_tokens(_lowerCamelCase	): i for i in range(self.vocab_size	)}
				vocab.update(self.added_tokens_encoder	)
				return vocab


	def __getstate__(self	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							self.__dict__.copy()
				__lowercase			  =							None
				return state


	def __setstate__(self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





				__lowercase			  =							d

				# for backward compatibility
				if not hasattr(self       ,'''sp_model_kwargs'''	):
							__lowercase			  =							{}

				__lowercase			  =							spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.Load(self.vocab_file	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





				return self.sp_model.encode(_lowerCamelCase       ,out_type=_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				if token in self.decoder:
							return self.decoder[token]
				elif token in self.added_tokens_decoder:
							return self.added_tokens_decoder[token]
				__lowercase			  =							self.sp_model.piece_to_id(_lowerCamelCase	)
				return sp_id + self.offset


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





				if index in self.encoder:
							return self.encoder[index]
				elif index in self.added_tokens_encoder:
							return self.added_tokens_encoder[index]
				else:
							__lowercase			  =							self.sp_model.IdToPiece(index - self.offset	)
				return token


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							[]
				__lowercase			  =							''''''
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										out_string += self.sp_model.decode(_lowerCamelCase	) + token
										__lowercase			  =							[]
							else:
										current_sub_tokens.append(_lowerCamelCase	)
				out_string += self.sp_model.decode(_lowerCamelCase	)
				return out_string.strip()


	def   _UpperCAmelCase       (self       ,_lowerCamelCase=False	)							->							List[str]:







				'''simple docstring'''





				return 1


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





				__lowercase			  =							set(self.all_special_ids	)  # call it once instead of inside list comp
				all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

				return [1 if x in all_special_ids else 0 for x in seq]


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase = False	)							->							List[int]:







				'''simple docstring'''





				if already_has_special_tokens:
							return self._special_token_mask(_lowerCamelCase	)
				elif token_ids_a is None:
							return self._special_token_mask(_lowerCamelCase	) + [1]
				else:
							return self._special_token_mask(token_ids_a + token_ids_a	) + [1]


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							List[int]:







				'''simple docstring'''





				if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							Tuple[str]:







				'''simple docstring'''





				if not os.path.isdir(_lowerCamelCase	):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory"	)
							return
				__lowercase			  =							os.path.join(
				    _lowerCamelCase       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

				if os.path.abspath(self.vocab_file	) != os.path.abspath(_lowerCamelCase	) and os.path.isfile(self.vocab_file	):
							copyfile(self.vocab_file       ,_lowerCamelCase	)
				elif not os.path.isfile(self.vocab_file	):
							with open(_lowerCamelCase       ,'''wb'''	) as fi:
										__lowercase			  =							self.sp_model.serialized_model_proto()
										fi.write(_lowerCamelCase	)

				return (out_vocab_file,)










'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			__lowercase			  =							len(lowerCamelCase_			) + 1
			__lowercase			  =							len(lowerCamelCase_			) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			__lowercase			  =							[[0 for i in range(lowerCamelCase_			)] for j in range(lowerCamelCase_			)]

			# since string of zero length match pattern of zero length
			__lowercase			  =							1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1						,    lowerCamelCase_			):
						__lowercase			  =							0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1						,    lowerCamelCase_			):
						__lowercase			  =							dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1						,    lowerCamelCase_			):
						for j in range(1						,    lowerCamelCase_			):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												__lowercase			  =							dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															__lowercase			  =							1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															__lowercase			  =							dp[i - 1][j]
												else:
															__lowercase			  =							0
									else:
												__lowercase			  =							0

			return bool(dp[-1][-1]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# inputing the strings
	# input_string = input("input a string :")
	# pattern = input("input a pattern :")

	_SCREAMING_SNAKE_CASE   =      '''aab'''
	_SCREAMING_SNAKE_CASE   =      '''c*a*b'''

	# using function to check whether given string matches the given pattern
	if match_pattern(input_string, pattern):
		print(f'''{input_string} matches the given pattern {pattern}''')
	else:
		print(f'''{input_string} does not match with the given pattern {pattern}''')





