

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


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

_a:    List[Any] 							=			{"""vocab_file""": """spm_char.model"""}

_a:    Union[str, Any] 							=			{
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

_a:    Any 							=			{
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__    				=     ['input_ids', 'attention_mask']




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Dict="<s>"		,	lowerCAmelCase	:						Dict="</s>"		,	lowerCAmelCase	:						str="<unk>"		,	lowerCAmelCase	:						str="<pad>"		,	lowerCAmelCase	:						Optional[Dict[str, Any]] = None		,	**lowerCAmelCase	:						int		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			{} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    bos_token=lowerCAmelCase		,	eos_token=lowerCAmelCase		,	unk_token=lowerCAmelCase		,	pad_token=lowerCAmelCase		,	sp_model_kwargs=self.sp_model_kwargs		,	**lowerCAmelCase		,	)

		UpperCAmelCase_       				=			vocab_file

		UpperCAmelCase_       				=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(lowerCAmelCase							)




	@property
	def       __A					(		self	:						int							):


		'''simple docstring'''







		return self.sp_model.get_piece_size()




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{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	:						List[str]							):


		'''simple docstring'''







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




	def __setstate__(		self	:						Tuple		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			d

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

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




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







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




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







		return self.sp_model.piece_to_id(lowerCAmelCase							)




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.sp_model.IdToPiece(lowerCAmelCase							)
		return token




	def       __A					(		self	:						str		,	lowerCAmelCase	:						List[Any]							):


		'''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:
				out_string += self.sp_model.decode(lowerCAmelCase							) + token
				UpperCAmelCase_       				=			[]
			else:
				current_sub_tokens.append(lowerCAmelCase							)
		out_string += self.sp_model.decode(lowerCAmelCase							)
		return out_string.strip()




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Union[str, Any]=None							):


		'''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       __A					(		self	:						str		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None		,	lowerCAmelCase	:						bool = False							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			[1]
		if token_ids_a is None:
			return ([0] * len(lowerCAmelCase							)) + suffix_ones
		return ([0] * len(lowerCAmelCase							)) + ([0] * len(lowerCAmelCase							)) + suffix_ones




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[str] = None							):


		'''simple docstring'''







		if not os.path.isdir(lowerCAmelCase							):
			logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
			return
		UpperCAmelCase_       				=			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:
				UpperCAmelCase_       				=			self.sp_model.serialized_model_proto()
				fi.write(lowerCAmelCase							)

		return (out_vocab_file,)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_a:    List[str] 							=			{
    """configuration_encodec""": [
        """ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EncodecConfig""",
    ],
    """feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    List[Any] 							=			[
				    """ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """EncodecModel""",
				    """EncodecPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				_a:    int 							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)