


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       '▁'

__UpperCamelCase    		=       {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

__UpperCamelCase    		=       {
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

__UpperCamelCase    		=       {
    'facebook/s2t-small-librispeech-asr': 1_0_2_4,
}

__UpperCamelCase    		=       ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

__UpperCamelCase    		=       {'mustc': MUSTC_LANGS}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Dict    =		VOCAB_FILES_NAMES
			_UpperCamelCase		:					str    =		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:					Tuple    =		MAX_MODEL_INPUT_SIZES
			_UpperCamelCase		:					List[str]    =		['input_ids', 'attention_mask']

			_UpperCamelCase		:					List[int]    =		[]




			def __init__(							self      ,						snake_case      ,						snake_case      ,						snake_case="<s>"      ,						snake_case="</s>"      ,						snake_case="<pad>"      ,						snake_case="<unk>"      ,						snake_case=False      ,						snake_case=False      ,						snake_case=None      ,						snake_case=None      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=snake_case      ,						eos_token=snake_case      ,						unk_token=snake_case      ,						pad_token=snake_case      ,						do_upper_case=snake_case      ,						do_lower_case=snake_case      ,						tgt_lang=snake_case      ,						lang_codes=snake_case      ,						sp_model_kwargs=self.sp_model_kwargs      ,						**snake_case      ,						)
						UpperCamelCase__					=       do_upper_case
						UpperCamelCase__					=       do_lower_case

						UpperCamelCase__					=       load_json(snake_case     )
						UpperCamelCase__					=       {v: k for k, v in self.encoder.items()}
						UpperCamelCase__					=       spm_file
						UpperCamelCase__					=       load_spm(snake_case      ,						self.sp_model_kwargs     )

						if lang_codes is not None:
									UpperCamelCase__					=       lang_codes
									UpperCamelCase__					=       LANGUAGES[lang_codes]
									UpperCamelCase__					=       [F'''<lang:{lang}>''' for lang in self.langs]
									UpperCamelCase__					=       {lang: self.sp_model.PieceToId(F'''<lang:{lang}>'''     ) for lang in self.langs}

									UpperCamelCase__					=       self.lang_tokens
									UpperCamelCase__					=       tgt_lang if tgt_lang is not None else self.langs[0]

									self.set_tgt_lang_special_tokens(self._tgt_lang     )
						else:
									UpperCamelCase__					=       {}




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return len(self.encoder     )




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self._tgt_lang




			@tgt_lang.setter
			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       new_tgt_lang
						self.set_tgt_lang_special_tokens(snake_case     )




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.lang_code_to_id[tgt_lang]
						UpperCamelCase__					=       [lang_code_id]




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						return self.encoder.get(snake_case      ,						self.encoder[self.unk_token]     )




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						return self.decoder.get(snake_case      ,						self.unk_token     )




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case=None     ):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None      ,						snake_case = False     ):

						'''simple docstring'''


						if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=snake_case      ,						token_ids_a=snake_case      ,						already_has_special_tokens=snake_case     )

						UpperCamelCase__					=       [1] * len(self.prefix_tokens     )
						UpperCamelCase__					=       [1]
						if token_ids_a is None:
									return prefix_ones + ([0] * len(snake_case     )) + suffix_ones
						return prefix_ones + ([0] * len(snake_case     )) + ([0] * len(snake_case     )) + suffix_ones




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.encoder.copy()
						vocab.update(self.added_tokens_encoder     )
						return vocab




			def __getstate__(							self     ):

						'''simple docstring'''


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




			def __setstate__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       d

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

						UpperCamelCase__					=       load_spm(self.spm_file      ,						self.sp_model_kwargs     )







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       Path(snake_case     )
						assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
						UpperCamelCase__					=       save_dir / (
						    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
						)
						UpperCamelCase__					=       save_dir / (
						    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
						)

						save_json(self.encoder      ,						snake_case     )

						if os.path.abspath(self.spm_file     ) != os.path.abspath(snake_case     ) and os.path.isfile(self.spm_file     ):
									copyfile(self.spm_file      ,						snake_case     )
						elif not os.path.isfile(self.spm_file     ):
									with open(snake_case      ,						"wb"     ) as fi:
												UpperCamelCase__					=       self.sp_model.serialized_model_proto()
												fi.write(snake_case     )

						return (str(snake_case     ), str(snake_case     ))



def        UpperCamelCase_(				_A   :str				,     _A   :Dict[str, Any]  )->		sentencepiece.SentencePieceProcessor:
			UpperCamelCase__					=       sentencepiece.SentencePieceProcessor(**_A  )
			spm.Load(str(_A  )  )
			return spm
def        UpperCamelCase_(				_A   :str  )->		Union[Dict, List]:
			with open(_A				,     "r"  ) as f:
						return json.load(_A  )







def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :str  )->		None:
			with open(_A				,     "w"  ) as f:
						json.dump(_A				,     _A				,     indent=2  )






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase    		=       {
    'configuration_mobilenet_v2': [
        'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV2Config',
        'MobileNetV2OnnxConfig',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       ['MobileNetV2FeatureExtractor']
						__UpperCamelCase    		=       ['MobileNetV2ImageProcessor']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MobileNetV2ForImageClassification',
						    'MobileNetV2ForSemanticSegmentation',
						    'MobileNetV2Model',
						    'MobileNetV2PreTrainedModel',
						    'load_tf_weights_in_mobilenet_v2',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

						__UpperCamelCase    		=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


