





'''simple docstring'''



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

import regex as re

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


snake_case_ :	List[str]             =   logging.get_logger(__name__)


snake_case_ :	str             =   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

snake_case_ :	str             =   {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

snake_case_ :	List[Any]             =   {'facebook/blenderbot_small-90M': 512}



def 			lowercase__(		_UpperCamelCase	:			Union[str, Any]					)-> str:






					"""simple docstring"""

					_UpperCamelCase									= set()
					_UpperCamelCase									= word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)					)
										_UpperCamelCase									= char

					_UpperCamelCase									= set(__lowercase					)
					return pairs

class      A_			(	__snake_case	):

			'''simple docstring'''





			_lowerCAmelCase							=     VOCAB_FILES_NAMES
			_lowerCAmelCase							=     PRETRAINED_VOCAB_FILES_MAP
			_lowerCAmelCase							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_lowerCAmelCase							=     ["input_ids", "attention_mask"]




			def __init__(       self      ,  A_      ,  A_      ,  A_="__start__"      ,  A_="__end__"      ,  A_="__unk__"      ,  A_="__null__"      ,  **A_      ,  ):
								super().__init__(unk_token=A__      ,  bos_token=A__      ,  eos_token=A__      ,  pad_token=A__      ,  **A__ )

								with open(A__      ,  encoding="utf-8" ) as vocab_handle:
													_UpperCamelCase									= json.load(A__ )
								_UpperCamelCase									= {v: k for k, v in self.encoder.items()}
								with open(A__      ,  encoding="utf-8" ) as merges_handle:
													_UpperCamelCase									= merges_handle.read().split("\n" )[1:-1]
								_UpperCamelCase									= [tuple(merge.split() ) for merge in merges]
								_UpperCamelCase									= dict(zip(A__      ,  range(len(A__ ) ) ) )
								_UpperCamelCase									= {}




			@property
			def    a						(       self ):
								return len(self.encoder )




			def    a						(       self ):
								return dict(self.encoder      ,  **self.added_tokens_encoder )




			def    a						(       self      ,  A_ ):
								if token in self.cache:
													return self.cache[token]
								_UpperCamelCase									= re.sub("([.,!?()])"      ,  r" \1"      ,  A__ )
								_UpperCamelCase									= re.sub("(\')"      ,  r" \1 "      ,  A__ )
								_UpperCamelCase									= re.sub(r"\s{2,}"      ,  " "      ,  A__ )
								if "\n" in token:
													_UpperCamelCase									= token.replace("\n"      ,  " __newln__" )

								_UpperCamelCase									= token.split(" " )
								_UpperCamelCase									= []
								for token in tokens:
													if not len(A__ ):
																		continue

													_UpperCamelCase									= token.lower()
													_UpperCamelCase									= tuple(A__ )
													_UpperCamelCase									= tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
													_UpperCamelCase									= get_pairs(A__ )

													if not pairs:
																		words.append(A__ )
																		continue

													while True:
																		_UpperCamelCase									= min(A__      ,  key=lambda A_ : self.bpe_ranks.get(A__      ,  float("inf" ) ) )
																		if bigram not in self.bpe_ranks:
																							break
																		_UpperCamelCase									= bigram
																		_UpperCamelCase									= []
																		_UpperCamelCase									= 0

																		while i < len(A__ ):
																							try:
																												_UpperCamelCase									= word.index(A__      ,  A__ )
																												new_word.extend(word[i:j] )
																												_UpperCamelCase									= j
																							except ValueError:
																												new_word.extend(word[i:] )
																												break

																							if word[i] == first and i < len(A__ ) - 1 and word[i + 1] == second:
																												new_word.append(first + second )
																												i += 2
																							else:
																												new_word.append(word[i] )
																												i += 1
																		_UpperCamelCase									= tuple(A__ )
																		_UpperCamelCase									= new_word
																		if len(A__ ) == 1:
																							break
																		else:
																							_UpperCamelCase									= get_pairs(A__ )
													_UpperCamelCase									= '''@@ '''.join(A__ )
													_UpperCamelCase									= word[:-4]

													_UpperCamelCase									= word
													words.append(A__ )
								return " ".join(A__ )




			def    a						(       self      ,  A_ ):
								_UpperCamelCase									= []

								_UpperCamelCase									= re.findall(r"\S+\n?"      ,  A__ )

								for token in words:
													split_tokens.extend(list(self.bpe(A__ ).split(" " ) ) )
								return split_tokens




			def    a						(       self      ,  A_ ):
								_UpperCamelCase									= token.lower()
								return self.encoder.get(A__      ,  self.encoder.get(self.unk_token ) )




			def    a						(       self      ,  A_ ):
								return self.decoder.get(A__      ,  self.unk_token )




			def    a						(       self      ,  A_ ):
								_UpperCamelCase									= ''' '''.join(A__ ).replace("@@ "      ,  "" ).strip()
								return out_string




			def    a						(       self      ,  A_      ,  A_ = None ):
								if not os.path.isdir(A__ ):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
													return
								_UpperCamelCase									= os.path.join(
								    A__      ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
								_UpperCamelCase									= os.path.join(
								    A__      ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

								with open(A__      ,  "w"      ,  encoding="utf-8" ) as f:
													f.write(json.dumps(self.encoder      ,  indent=2      ,  sort_keys=A__      ,  ensure_ascii=A__ ) + "\n" )

								_UpperCamelCase									= 0
								with open(A__      ,  "w"      ,  encoding="utf-8" ) as writer:
													writer.write("#version: 0.2\n" )
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,  key=lambda A_ : kv[1] ):
																		if index != token_index:
																							logger.warning(
																							    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																							    " Please check that the tokenizer is not corrupted!" )
																							_UpperCamelCase									= token_index
																		writer.write(" ".join(A__ ) + "\n" )
																		index += 1

								return vocab_file, merge_file











from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_UpperCamelCase			:	Optional[Any]								={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   "pegasus"
		__snake_case      :       List[str]     =   ["past_key_values"]
		__snake_case      :       Union[str, Any]     =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(					self       ,A__=50265       ,A__=1024       ,A__=12       ,A__=4096       ,A__=16       ,A__=12       ,A__=4096       ,A__=16       ,A__=0.0       ,A__=0.0       ,A__=True       ,A__=True       ,A__="gelu"       ,A__=1024       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=0       ,A__=False       ,A__=0       ,A__=1       ,A__=1       ,**A__       ,):
						_A	:				List[str]   =      vocab_size
						_A	:				List[Any]   =      max_position_embeddings
						_A	:				Union[str, Any]   =      d_model
						_A	:				Union[str, Any]   =      encoder_ffn_dim
						_A	:				Union[str, Any]   =      encoder_layers
						_A	:				int   =      encoder_attention_heads
						_A	:				List[Any]   =      decoder_ffn_dim
						_A	:				Optional[int]   =      decoder_layers
						_A	:				Tuple   =      decoder_attention_heads
						_A	:				List[str]   =      dropout
						_A	:				Tuple   =      attention_dropout
						_A	:				Union[str, Any]   =      activation_dropout
						_A	:				int   =      activation_function
						_A	:				Union[str, Any]   =      init_std
						_A	:				Any   =      encoder_layerdrop
						_A	:				int   =      decoder_layerdrop
						_A	:				Tuple   =      use_cache
						_A	:				Dict   =      encoder_layers
						_A	:				List[str]   =      scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=A__       ,eos_token_id=A__       ,is_encoder_decoder=A__       ,decoder_start_token_id=A__       ,forced_eos_token_id=A__       ,**A__       ,)







		@property
		def  A__      (					self		):
						return self.encoder_attention_heads







		@property
		def  A__      (					self		):
						return self.d_model



