




"""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


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{"""vocab_file""": """spm_char.model"""}

lowercase__     						=						{
    """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""",
    }
}

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






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			VOCAB_FILES_NAMES
			lowerCamelCase__      =			PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__      =			["""input_ids""", """attention_mask"""]
			def __init__(  self  ,      lowercase  ,      lowercase="<s>"  ,      lowercase="</s>"  ,      lowercase="<unk>"  ,      lowercase="<pad>"  ,      lowercase = None  ,      **lowercase  ,      ):
							_lowerCamelCase     :			List[str]    =    {} if sp_model_kwargs is None else sp_model_kwargs

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

							_lowerCamelCase     :			str    =    vocab_file

							_lowerCamelCase     :			Union[str, Any]    =    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(lowercase  )
			@property
			def 					A_    (  self  ):
							return self.sp_model.get_piece_size()
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    {self.convert_ids_to_tokens(lowercase  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab
			def __getstate__(  self  ):
							_lowerCamelCase     :			Optional[Any]    =    self.__dict__.copy()
							_lowerCamelCase     :			Tuple    =    None
							return state
			def __setstate__(  self  ,      lowercase  ):
							_lowerCamelCase     :			int    =    d

							# for backward compatibility
							if not hasattr(self  ,      'sp_model_kwargs'  ):
											_lowerCamelCase     :			Tuple    =    {}

							_lowerCamelCase     :			List[str]    =    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(self.vocab_file  )
			def 					A_    (  self  ,      lowercase  ):
							return self.sp_model.encode(lowercase  ,      out_type=lowercase  )
			def 					A_    (  self  ,      lowercase  ):
							return self.sp_model.piece_to_id(lowercase  )
			def 					A_    (  self  ,      lowercase  ):
							_lowerCamelCase     :			Dict    =    self.sp_model.IdToPiece(lowercase  )
							return token
			def 					A_    (  self  ,      lowercase  ):
							_lowerCamelCase     :			Dict    =    []
							_lowerCamelCase     :			List[str]    =    ''
							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(lowercase  ) + token
															_lowerCamelCase     :			List[Any]    =    []
											else:
															current_sub_tokens.append(lowercase  )
							out_string += self.sp_model.decode(lowercase  )
							return out_string.strip()
			def 					A_    (  self  ,      lowercase  ,      lowercase=None  ):
							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  ,      lowercase  ,      lowercase = None  ,      lowercase = False  ):
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase  ,      token_ids_a=lowercase  ,      already_has_special_tokens=lowercase  )

							_lowerCamelCase     :			Optional[int]    =    [1]
							if token_ids_a is None:
											return ([0] * len(lowercase  )) + suffix_ones
							return ([0] * len(lowercase  )) + ([0] * len(lowercase  )) + suffix_ones






			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ):
							if not os.path.isdir(lowercase  ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
											return
							_lowerCamelCase     :			Tuple    =    os.path.join(
							    lowercase  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase  ) and os.path.isfile(self.vocab_file  ):
											copyfile(self.vocab_file  ,      lowercase  )
							elif not os.path.isfile(self.vocab_file  ):
											with open(lowercase  ,      'wb'  ) as fi:
															_lowerCamelCase     :			str    =    self.sp_model.serialized_model_proto()
															fi.write(lowercase  )

							return (out_vocab_file,)





"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

							# uncondition for scaling
							_lowerCamelCase     :			str    =    nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states