'''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


lowerCAmelCase_ : Optional[int]						  =      logging.get_logger(__name__)

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

lowerCAmelCase_ : 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",
    }
}

lowerCAmelCase_ : List[str]						  =      {
    "microsoft/speecht5_asr": 1_024,
    "microsoft/speecht5_tts": 1_024,
    "microsoft/speecht5_vc": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Optional[Any]="<s>"   ,		__lowerCAmelCase				:	Optional[int]="</s>"   ,		__lowerCAmelCase				:	int="<unk>"   ,		__lowerCAmelCase				:	Union[str, Any]="<pad>"   ,		__lowerCAmelCase				:	Optional[Dict[str, Any]] = None   ,		**__lowerCAmelCase				:	str   ,		)      ->					None:
         lowercase_     =      {} 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   ,		)

         lowercase_     =      vocab_file

         lowercase_     =      spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(__lowerCAmelCase)


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					Dict:
         return self.sp_model.get_piece_size()


   def __UpperCAmelCase       (   self				:	Any)      ->					Union[str, Any]:
         lowercase_     =      {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])      ->					Tuple:
         lowercase_     =      self.__dict__.copy()
         lowercase_     =      None
         return state


   def __setstate__(   self				:	Tuple   ,		__lowerCAmelCase				:	Any)      ->					int:
         lowercase_     =      d

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

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


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	str)      ->					List[str]:
         return self.sp_model.encode(__lowerCAmelCase   ,		out_type=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Optional[int])      ->					int:
         return self.sp_model.piece_to_id(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	Union[str, Any])      ->					List[str]:
         lowercase_     =      self.sp_model.IdToPiece(__lowerCAmelCase)
         return token


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Dict)      ->					Optional[int]:
         lowercase_     =      []
         lowercase_     =      ""
         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
                     lowercase_     =      []
               else:
                     current_sub_tokens.append(__lowerCAmelCase)
         out_string += self.sp_model.decode(__lowerCAmelCase)
         return out_string.strip()


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Dict=None)      ->					List[int]:
         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 __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	List[int]   ,		__lowerCAmelCase				:	Optional[List[int]] = None   ,		__lowerCAmelCase				:	bool = False)      ->					List[int]:
         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__lowerCAmelCase   ,		token_ids_a=__lowerCAmelCase   ,		already_has_special_tokens=__lowerCAmelCase)

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


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         if not os.path.isdir(__lowerCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
         lowercase_     =      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:
                     lowercase_     =      self.sp_model.serialized_model_proto()
                     fi.write(__lowerCAmelCase)

         return (out_vocab_file,)







'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def __a (	__lowerCamelCase			:						List[Any]				)	->							Tuple:
      '''simple docstring'''


      return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						str				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      {}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            lowercase_     =      key.replace("heads.cmd.mim_head.cls.predictions" ,		"mmm_image_head"				)
            lowercase_     =      key.replace("heads.cmd.mlm_head.cls.predictions" ,		"mmm_text_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.cls" ,		"itm_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.pooler" ,		"itm_head.pooler"				)
            lowercase_     =      key.replace("heads.cmd.clip_head.logit_scale" ,		"flava.logit_scale"				)
            lowercase_     =      key.replace("heads.fairseq_mlm.cls.predictions" ,		"mlm_head"				)
            lowercase_     =      key.replace("heads.imagenet.mim_head.cls.predictions" ,		"mim_head"				)
            lowercase_     =      key.replace("mm_text_projection" ,		"flava.text_to_mm_projection"				)
            lowercase_     =      key.replace("mm_image_projection" ,		"flava.image_to_mm_projection"				)
            lowercase_     =      key.replace("image_encoder.module" ,		"flava.image_model"				)
            lowercase_     =      key.replace("text_encoder.module" ,		"flava.text_model"				)
            lowercase_     =      key.replace("mm_encoder.module.encoder.cls_token" ,		"flava.multimodal_model.cls_token"				)
            lowercase_     =      key.replace("mm_encoder.module" ,		"flava.multimodal_model"				)
            lowercase_     =      key.replace("text_projection" ,		"flava.text_projection"				)
            lowercase_     =      key.replace("image_projection" ,		"flava.image_projection"				)

            lowercase_     =      value.float()

      for key, value in codebook_state_dict.items():
            lowercase_     =      value

      return upgrade


@torch.no_grad()
def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Tuple=None				)	->							Optional[int]:
      '''simple docstring'''


      if config_path is not None:
            lowercase_     =      FlavaConfig.from_pretrained(__lowerCamelCase				)
      else:
            lowercase_     =      FlavaConfig()

      lowercase_     =      FlavaForPreTraining(__lowerCamelCase				).eval()

      lowercase_     =      convert_dalle_checkpoint(__lowerCamelCase ,		__lowerCamelCase ,		save_checkpoint=__lowerCamelCase				)

      if os.path.exists(__lowerCamelCase				):
            lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)
      else:
            lowercase_     =      torch.hub.load_state_dict_from_url(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      upgrade_state_dict(__lowerCamelCase ,		__lowerCamelCase				)
      hf_model.load_state_dict(__lowerCamelCase				)
      lowercase_     =      hf_model.state_dict()
      lowercase_     =      count_parameters(__lowerCamelCase				)
      lowercase_     =      count_parameters(__lowerCamelCase				) + count_parameters(__lowerCamelCase				)

      assert torch.allclose(__lowerCamelCase ,		__lowerCamelCase ,		atol=1e-3				)

      hf_model.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[str]						  =      argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
       parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_ : List[Any]						  =      parser.parse_args()

       convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






