






"""simple docstring"""



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

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


A__	:    str			 = logging.get_logger(__name__)


A__	:    List[str]			 = {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

A__	:    List[Any]			 = {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

A__	:    Optional[Any]			 = '</w>'
A__	:    int			 = '@@ '

def 			_snake_case    (					lowerCamelCase__     :   Any  )    ->     Optional[int]:
							lowerCamelCase_	:							Union[str, Any]									=set()
							lowerCamelCase_	:							int									=word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)  )
														lowerCamelCase_	:							List[Any]									=char
							return pairs


# Speech2Text2 has no max input length
A__	:    int			 = {'facebook/s2t-wav2vec2-large-en-de': 1_024}







class        snake_case__      (							_A       ):
			_UpperCAmelCase		:Any             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Union[str, Any]             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:str             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Optional[int]             =			["input_ids", "attention_mask"]


			def __init__(				self					:  Tuple       ,	snake_case__					:  int       ,	snake_case__					:  Optional[Any]="<s>"       ,	snake_case__					:  str="<pad>"       ,	snake_case__					:  Any="</s>"       ,	snake_case__					:  Optional[int]="<unk>"       ,	snake_case__					:  Dict=False       ,	snake_case__					:  str=None       ,	**snake_case__					:  Union[str, Any]       ,	):
										super().__init__(
										    unk_token=__SCREAMING_SNAKE_CASE       ,	bos_token=__SCREAMING_SNAKE_CASE       ,	eos_token=__SCREAMING_SNAKE_CASE       ,	pad_token=__SCREAMING_SNAKE_CASE       ,	do_lower_case=__SCREAMING_SNAKE_CASE       ,	**__SCREAMING_SNAKE_CASE       ,	)

										lowerCamelCase_	:							Optional[Any]									=do_lower_case

										with open(__SCREAMING_SNAKE_CASE       ,	encoding="utf-8" ) as vocab_handle:
																	lowerCamelCase_	:							Any									=json.load(__SCREAMING_SNAKE_CASE )
										lowerCamelCase_	:							Union[str, Any]									={v: k for k, v in self.encoder.items()}

										if merges_file is None:
																	logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding.""" )

																	lowerCamelCase_	:							Tuple									=None
																	lowerCamelCase_	:							Union[str, Any]									=None
										else:
																	with open(__SCREAMING_SNAKE_CASE       ,	encoding="utf-8" ) as merges_handle:
																								lowerCamelCase_	:							List[Any]									=merges_handle.read().split("\n" )[:-1]

																	lowerCamelCase_	:							Any									=[tuple(merge.split()[:2] ) for merge in merges]
																	lowerCamelCase_	:							Tuple									=dict(zip(__SCREAMING_SNAKE_CASE       ,	range(len(__SCREAMING_SNAKE_CASE ) ) ) )
																	lowerCamelCase_	:							int									={}


			@property
			def      UpperCAmelCase__						(				self					:  str ):
										return len(self.decoder )


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


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  str ):
										lowerCamelCase_	:							Any									=tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
										if token in self.cache:
																	return self.cache[token]
										lowerCamelCase_	:							Dict									=get_pairs(__SCREAMING_SNAKE_CASE )

										if not pairs:
																	return token

										while True:
																	lowerCamelCase_	:							int									=min(__SCREAMING_SNAKE_CASE       ,	key=lambda snake_case__ : self.bpe_ranks.get(__SCREAMING_SNAKE_CASE       ,	float("inf" ) ) )
																	if bigram not in self.bpe_ranks:
																								break
																	lowerCamelCase_							,						lowerCamelCase_	:							str									=bigram
																	lowerCamelCase_	:							Union[str, Any]									=[]
																	lowerCamelCase_	:							Optional[int]									=0
																	while i < len(__SCREAMING_SNAKE_CASE ):
																								try:
																															lowerCamelCase_	:							Optional[int]									=word.index(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE )
																								except ValueError:
																															new_word.extend(word[i:] )
																															break
																								else:
																															new_word.extend(word[i:j] )
																															lowerCamelCase_	:							List[Any]									=j

																								if word[i] == first and i < len(__SCREAMING_SNAKE_CASE ) - 1 and word[i + 1] == second:
																															new_word.append(first + second )
																															i += 2
																								else:
																															new_word.append(word[i] )
																															i += 1
																	lowerCamelCase_	:							int									=tuple(__SCREAMING_SNAKE_CASE )
																	lowerCamelCase_	:							Any									=new_word
																	if len(__SCREAMING_SNAKE_CASE ) == 1:
																								break
																	else:
																								lowerCamelCase_	:							int									=get_pairs(__SCREAMING_SNAKE_CASE )
										lowerCamelCase_	:							str									=" ".join(__SCREAMING_SNAKE_CASE )
										if word == "\n  " + BPE_TOKEN_MERGES:
																	lowerCamelCase_	:							Tuple									="\n" + BPE_TOKEN_MERGES

										if word.endswith(__SCREAMING_SNAKE_CASE ):
																	lowerCamelCase_	:							List[str]									=word.replace(__SCREAMING_SNAKE_CASE       ,	"" )

										lowerCamelCase_	:							List[Any]									=word.replace(" "       ,	__SCREAMING_SNAKE_CASE )
										lowerCamelCase_	:							Optional[Any]									=word
										return word


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  List[str] ):

										if self.bpe_ranks is None:
																	raise ValueError(
																	    "This tokenizer was instantiated without a `merges.txt` file, so"
																	    " that it can only be used for decoding, not for encoding."
																	    "Make sure to provide `merges.txt` file at instantiation to enable "
																	    "encoding." )

										if self.do_lower_case:
																	lowerCamelCase_	:							Any									=text.lower()

										lowerCamelCase_	:							str									=text.split()

										lowerCamelCase_	:							Optional[int]									=[]
										for token in text:
																	if token:
																								split_tokens.extend(list(self.bpe(__SCREAMING_SNAKE_CASE ).split(" " ) ) )

										return split_tokens


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  str ):
										return self.encoder.get(__SCREAMING_SNAKE_CASE       ,	self.encoder.get(self.unk_token ) )


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  int ):
										lowerCamelCase_	:							int									=self.decoder.get(__SCREAMING_SNAKE_CASE       ,	self.unk_token )
										return result


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  List[str] ):
										lowerCamelCase_	:							Optional[Any]									=" ".join(__SCREAMING_SNAKE_CASE )

										# make sure @@ tokens are concatenated
										lowerCamelCase_	:							Dict									="".join(string.split(__SCREAMING_SNAKE_CASE ) )

										return string


			def      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  str       ,	snake_case__					:  Optional[str] = None ):
										if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
																	return
										lowerCamelCase_	:							Any									=os.path.join(
										    __SCREAMING_SNAKE_CASE       ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
										lowerCamelCase_	:							str									=os.path.join(
										    __SCREAMING_SNAKE_CASE       ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

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

										lowerCamelCase_	:							str									=0
										if self.bpe_ranks is None:
																	return (vocab_file,)

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

										return (vocab_file, merges_file)











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


A__	:    Optional[int]			 = {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Dict			 = ['Speech2TextTokenizer']

try:
						if not is_speech_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Optional[Any]			 = ['Speech2TextFeatureExtractor']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Dict			 = [
						    'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFSpeech2TextForConditionalGeneration',
						    'TFSpeech2TextModel',
						    'TFSpeech2TextPreTrainedModel',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Optional[int]			 = [
						    'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Speech2TextForConditionalGeneration',
						    'Speech2TextModel',
						    'Speech2TextPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
						from .processing_speech_to_text import SpeechaTextProcessor

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_speech_to_text import SpeechaTextTokenizer

						try:
												if not is_speech_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_speech_to_text import (
												    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSpeechaTextForConditionalGeneration,
												    TFSpeechaTextModel,
												    TFSpeechaTextPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_to_text import (
												    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechaTextForConditionalGeneration,
												    SpeechaTextModel,
												    SpeechaTextPreTrainedModel,
												)

else:
						import sys

						A__	:    List[Any]			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



