


'''simple docstring'''
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


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {'''vocab_file''': '''spm_char.model'''}

__snake_case							      =     {
    '''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''',
    }
}

__snake_case							      =     {
    '''microsoft/speecht5_asr''': 1024,
    '''microsoft/speecht5_tts''': 1024,
    '''microsoft/speecht5_vc''': 1024,
}
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				VOCAB_FILES_NAMES
		_a     =				PRETRAINED_VOCAB_FILES_MAP
		_a     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a     =				['input_ids', 'attention_mask']




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

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= {} if sp_model_kwargs is None else sp_model_kwargs

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

							UpperCamelCase__   :Any												= vocab_file

							UpperCamelCase__   :Optional[Any]												= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(UpperCamelCase_							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.sp_model.get_piece_size()




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= {self.convert_ids_to_tokens(UpperCamelCase_							): i for i in range(self.vocab_size							)}
							vocab.update(self.added_tokens_encoder							)
							return vocab




		def __getstate__(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= self.__dict__.copy()
							UpperCamelCase__   :Union[str, Any]												= None
							return state




		def __setstate__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :str												= d

							# for backward compatibility
							if not hasattr(self    ,   '''sp_model_kwargs'''							):
												UpperCamelCase__   :Optional[Any]												= {}

							UpperCamelCase__   :Dict												= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(self.vocab_file							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							return self.sp_model.piece_to_id(UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.sp_model.IdToPiece(UpperCamelCase_							)
							return token




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= []
							UpperCamelCase__   :Optional[Any]												= ''''''
							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(UpperCamelCase_							) + token
																	UpperCamelCase__   :Optional[Any]												= []
												else:
																	current_sub_tokens.append(UpperCamelCase_							)
							out_string += self.sp_model.decode(UpperCamelCase_							)
							return out_string.strip()




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=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     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False							):

							'''simple docstring'''


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

							UpperCamelCase__   :Optional[Any]												= [1]
							if token_ids_a is None:
												return ([0] * len(UpperCamelCase_							)) + suffix_ones
							return ([0] * len(UpperCamelCase_							)) + ([0] * len(UpperCamelCase_							)) + suffix_ones




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							if not os.path.isdir(UpperCamelCase_							):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
												return
							UpperCamelCase__   :int												= os.path.join(
							    UpperCamelCase_    ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

							if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCamelCase_							) and os.path.isfile(self.vocab_file							):
												copyfile(self.vocab_file    ,   UpperCamelCase_							)
							elif not os.path.isfile(self.vocab_file							):
												with open(UpperCamelCase_    ,   '''wb'''							) as fi:
																	UpperCamelCase__   :List[Any]												= self.sp_model.serialized_model_proto()
																	fi.write(UpperCamelCase_							)

							return (out_vocab_file,)




import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'vision-encoder-decoder'
							lowerCamelCase__   			=		True







							def __init__(   self,			**__a):

														'''simple docstring'''

														super().__init__(**__a)
														if "encoder" not in kwargs or "decoder" not in kwargs:
																					raise ValueError(
																					    f"A configuraton of type {self.model_type} cannot be instantiated because "
																					    f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")

														_lowerCAmelCase     :							str    =    kwargs.pop("encoder")
														_lowerCAmelCase     :							Any    =    encoder_config.pop("model_type")
														_lowerCAmelCase     :							str    =    kwargs.pop("decoder")
														_lowerCAmelCase     :							List[str]    =    decoder_config.pop("model_type")

														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[int]    =    True







							@classmethod
							def    snake_case__      (   cls,			__a,			__a,			**__a):

														'''simple docstring'''

														logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
														_lowerCAmelCase     :							Optional[Any]    =    True
														_lowerCAmelCase     :							str    =    True

														return cls(encoder=encoder_config.to_dict(),			decoder=decoder_config.to_dict(),			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    copy.deepcopy(self.__dict__)
														_lowerCAmelCase     :							List[str]    =    self.encoder.to_dict()
														_lowerCAmelCase     :							List[str]    =    self.decoder.to_dict()
														_lowerCAmelCase     :							Any    =    self.__class__.model_type
														return output



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()
														_lowerCAmelCase     :							Any    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							List[str]    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							Optional[Any]    =    {0: "batch", 1: "encoder_sequence"}

														return common_inputs







							def    snake_case__      (   self,			__a,			__a = -1,			__a = -1,			__a = False,			__a = None,			):

														'''simple docstring'''

														import torch

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()

														_lowerCAmelCase     :							List[str]    =    super().generate_dummy_inputs(
														    __a,			batch_size=__a,			seq_length=__a,			is_pair=__a,			framework=__a)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    dummy_input["input_ids"].shape
														_lowerCAmelCase     :							str    =    (batch, encoder_sequence, self._config.encoder_hidden_size)
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("input_ids")
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("attention_mask")
														_lowerCAmelCase     :							Optional[int]    =    torch.zeros(__a)

														return common_inputs



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return VisionEncoderDecoderEncoderOnnxConfig(__a)







							def    snake_case__      (   self,			__a,			__a,			__a = "default"):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    encoder_config.hidden_size
														return VisionEncoderDecoderDecoderOnnxConfig(__a,			__a)
