




"""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


a_				:    Any										=     logging.get_logger(__name__)


a_				:    Optional[int]										=     {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a_				:    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'''
        )
    },
}

a_				:    str										=     {'''facebook/blenderbot_small-90M''': 5_12}





def 						UpperCAmelCase				(			A__:    Tuple     )  ->     int:

			'''simple docstring'''





			__lowerCamelCase					:	Any	       =				set()
			__lowerCamelCase					:	List[Any]	       =				word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)     )
						__lowerCamelCase					:	int	       =				char

			__lowerCamelCase					:	List[Any]	       =				set(lowerCamelCase__     )
			return pairs






class __lowercase(      _UpperCamelCase				):




					'''simple docstring'''


					__a						:						List[str]     		= VOCAB_FILES_NAMES
					__a						:						str     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						Optional[int]     		= ['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:
											__lowerCamelCase					:	str	       =				json.load(__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				{v: k for k, v in self.encoder.items()}
								with open(__a						,	encoding='utf-8'     ) as merges_handle:
											__lowerCamelCase					:	Optional[int]	       =				merges_handle.read().split('\n'     )[1:-1]
								__lowerCamelCase					:	str	       =				[tuple(merge.split()     ) for merge in merges]
								__lowerCamelCase					:	List[str]	       =				dict(zip(__a						,	range(len(__a     )     )     )     )
								__lowerCamelCase					:	Optional[Any]	       =				{}
					@property
					def     snake_case_				(    self     ):
								return len(self.encoder     )
					def     snake_case_				(    self     ):
								return dict(self.encoder						,	**self.added_tokens_encoder     )
					def     snake_case_				(    self						,	__a     ):
								if token in self.cache:
											return self.cache[token]
								__lowerCamelCase					:	Optional[int]	       =				re.sub('([.,!?()])'						,	r' \1'						,	__a     )
								__lowerCamelCase					:	List[Any]	       =				re.sub('(\')'						,	r' \1 '						,	__a     )
								__lowerCamelCase					:	Optional[int]	       =				re.sub(r'\s{2,}'						,	' '						,	__a     )
								if "\n" in token:
											__lowerCamelCase					:	Union[str, Any]	       =				token.replace('\n'						,	' __newln__'     )

								__lowerCamelCase					:	str	       =				token.split(' '     )
								__lowerCamelCase					:	int	       =				[]
								for token in tokens:
											if not len(__a     ):
														continue

											__lowerCamelCase					:	List[Any]	       =				token.lower()
											__lowerCamelCase					:	Optional[Any]	       =				tuple(__a     )
											__lowerCamelCase					:	Optional[Any]	       =				tuple(list(word[:-1]     ) + [word[-1] + '</w>']     )
											__lowerCamelCase					:	str	       =				get_pairs(__a     )

											if not pairs:
														words.append(__a     )
														continue

											while True:
														__lowerCamelCase					:	List[Any]	       =				min(__a						,	key=lambda __a     : self.bpe_ranks.get(__a						,	float('inf'     )     )     )
														if bigram not in self.bpe_ranks:
																	break
														__lowerCamelCase					:	Optional[Any]	       =				bigram
														__lowerCamelCase					:	str	       =				[]
														__lowerCamelCase					:	List[Any]	       =				0

														while i < len(__a     ):
																	try:
																				__lowerCamelCase					:	Dict	       =				word.index(__a						,	__a     )
																				new_word.extend(word[i:j]     )
																				__lowerCamelCase					:	int	       =				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
														__lowerCamelCase					:	Union[str, Any]	       =				tuple(__a     )
														__lowerCamelCase					:	Optional[Any]	       =				new_word
														if len(__a     ) == 1:
																	break
														else:
																	__lowerCamelCase					:	List[Any]	       =				get_pairs(__a     )
											__lowerCamelCase					:	Optional[Any]	       =				"@@ ".join(__a     )
											__lowerCamelCase					:	Any	       =				word[:-4]

											__lowerCamelCase					:	Dict	       =				word
											words.append(__a     )
								return " ".join(__a     )
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Any	       =				[]

								__lowerCamelCase					:	List[Any]	       =				re.findall(r'\S+\n?'						,	__a     )

								for token in words:
											split_tokens.extend(list(self.bpe(__a     ).split(' '     )     )     )
								return split_tokens
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Union[str, Any]	       =				token.lower()
								return self.encoder.get(__a						,	self.encoder.get(self.unk_token     )     )
					def     snake_case_				(    self						,	__a     ):
								return self.decoder.get(__a						,	self.unk_token     )
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				" ".join(__a     ).replace('@@ '						,	''     ).strip()
								return out_string





					def     snake_case_				(    self						,	__a						,	__a = None     ):
								if not os.path.isdir(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	int	       =				os.path.join(
								    __a						,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
								__lowerCamelCase					:	str	       =				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'     )

								__lowerCamelCase					:	Optional[Any]	       =				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!'     )
																	__lowerCamelCase					:	Dict	       =				token_index
														writer.write(' '.join(__a     ) + '\n'     )
														index += 1

								return vocab_file, merge_file









"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						List[str]     		= 'Speech2TextFeatureExtractor'
					__a						:						Optional[int]     		= 'Speech2TextTokenizer'
					def __init__(    self						,	__a						,	__a     ):
								super().__init__(__a						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False
					def __call__(    self						,	*__a						,	**__a     ):
								# For backward compatibility
								if self._in_target_context_manager:
											return self.current_processor(*__a						,	**__a     )

								if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'     )
											__lowerCamelCase					:	str	       =				kwargs.pop('raw_speech'     )
								else:
											__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('audio'						,	__a     )
								__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('sampling_rate'						,	__a     )
								__lowerCamelCase					:	Optional[int]	       =				kwargs.pop('text'						,	__a     )
								if len(__a     ) > 0:
											__lowerCamelCase					:	Optional[int]	       =				args[0]
											__lowerCamelCase					:	str	       =				args[1:]

								if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'     )

								if audio is not None:
											__lowerCamelCase					:	Dict	       =				self.feature_extractor(__a						,	*__a						,	sampling_rate=__a						,	**__a     )
								if text is not None:
											__lowerCamelCase					:	Optional[int]	       =				self.tokenizer(__a						,	**__a     )

								if text is None:
											return inputs
								elif audio is None:
											return encodings
								else:
											__lowerCamelCase					:	Any	       =				encodings['input_ids']
											return inputs
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.batch_decode(*__a						,	**__a     )
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.decode(*__a						,	**__a     )





					@contextmanager
					def     snake_case_				(    self     ):
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.'     )
								__lowerCamelCase					:	int	       =				True
								__lowerCamelCase					:	Union[str, Any]	       =				self.tokenizer
								yield
								__lowerCamelCase					:	Tuple	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False



