


import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


__a     =			logging.get_logger(__name__)






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''AutoTokenizer'''
      UpperCamelCase								=       ['''tokenizer''']

      UpperCamelCase								=       {
          '''semantic_prompt''': 1,
          '''coarse_prompt''': 2,
          '''fine_prompt''': 2,
      }





      def __init__(		self						:					Optional[int]					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					Any=None       )    ->   Any:






         """simple docstring"""







         super().__init__(__lowerCamelCase       )

         UpperCAmelCase							=     speaker_embeddings





      @classmethod
      def        _lowercase				(		cls						:					Tuple					,       __lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					str="speaker_embeddings_path.json"					,       **__lowerCamelCase						:					Any       )    ->   str:






         """simple docstring"""







         if speaker_embeddings_dict_path is not None:
            UpperCAmelCase							=     get_file_from_repo(
                __lowerCamelCase					,       __lowerCamelCase					,       subfolder=kwargs.pop("""subfolder"""					,       __lowerCamelCase       )					,       cache_dir=kwargs.pop("""cache_dir"""					,       __lowerCamelCase       )					,       force_download=kwargs.pop("""force_download"""					,       __lowerCamelCase       )					,       proxies=kwargs.pop("""proxies"""					,       __lowerCamelCase       )					,       resume_download=kwargs.pop("""resume_download"""					,       __lowerCamelCase       )					,       local_files_only=kwargs.pop("""local_files_only"""					,       __lowerCamelCase       )					,       use_auth_token=kwargs.pop("""use_auth_token"""					,       __lowerCamelCase       )					,       revision=kwargs.pop("""revision"""					,       __lowerCamelCase       )					,       )
            if speaker_embeddings_path is None:
               logger.warning(
                   F"""`{os.path.join(__lowerCamelCase					,       __lowerCamelCase       )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""       )
               UpperCAmelCase							=     None
            else:
               with open(__lowerCamelCase       ) as speaker_embeddings_json:
                  UpperCAmelCase							=     json.load(__lowerCamelCase       )
         else:
            UpperCAmelCase							=     None

         UpperCAmelCase							=     AutoTokenizer.from_pretrained(__lowerCamelCase					,       **__lowerCamelCase       )

         return cls(tokenizer=__lowerCamelCase					,       speaker_embeddings=__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					Optional[Any]					,       __lowerCamelCase						:					int="speaker_embeddings_path.json"					,       __lowerCamelCase						:					Any="speaker_embeddings"					,       __lowerCamelCase						:					bool = False					,       **__lowerCamelCase						:					Any					,       )    ->   str:






         """simple docstring"""







         if self.speaker_embeddings is not None:
            os.makedirs(os.path.join(__lowerCamelCase					,       __lowerCamelCase					,       """v2"""       )					,       exist_ok=__lowerCamelCase       )

            UpperCAmelCase							=     {}

            UpperCAmelCase							=     save_directory

            for prompt_key in self.speaker_embeddings:
               if prompt_key != "repo_or_path":
                  UpperCAmelCase							=     self._load_voice_preset(__lowerCamelCase       )

                  UpperCAmelCase							=     {}
                  for key in self.speaker_embeddings[prompt_key]:
                     np.save(
                         os.path.join(
                             embeddings_dict["""repo_or_path"""]					,       __lowerCamelCase					,       F"""{prompt_key}_{key}"""       )					,       voice_preset[key]					,       allow_pickle=__lowerCamelCase					,       )
                     UpperCAmelCase							=     os.path.join(__lowerCamelCase					,       F"""{prompt_key}_{key}.npy"""       )

                  UpperCAmelCase							=     tmp_dict

            with open(os.path.join(__lowerCamelCase					,       __lowerCamelCase       )					,       """w"""       ) as fp:
               json.dump(__lowerCamelCase					,       __lowerCamelCase       )

         super().save_pretrained(__lowerCamelCase					,       __lowerCamelCase					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					int					,       __lowerCamelCase						:					str = None					,       **__lowerCamelCase						:					Tuple       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.speaker_embeddings[voice_preset]

         UpperCAmelCase							=     {}
         for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
            if key not in voice_preset_paths:
               raise ValueError(
                   F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""       )

            UpperCAmelCase							=     get_file_from_repo(
                self.speaker_embeddings.get("""repo_or_path"""					,       """/"""       )					,       voice_preset_paths[key]					,       subfolder=kwargs.pop("""subfolder"""					,       __lowerCamelCase       )					,       cache_dir=kwargs.pop("""cache_dir"""					,       __lowerCamelCase       )					,       force_download=kwargs.pop("""force_download"""					,       __lowerCamelCase       )					,       proxies=kwargs.pop("""proxies"""					,       __lowerCamelCase       )					,       resume_download=kwargs.pop("""resume_download"""					,       __lowerCamelCase       )					,       local_files_only=kwargs.pop("""local_files_only"""					,       __lowerCamelCase       )					,       use_auth_token=kwargs.pop("""use_auth_token"""					,       __lowerCamelCase       )					,       revision=kwargs.pop("""revision"""					,       __lowerCamelCase       )					,       )
            if path is None:
               raise ValueError(
                   F"""`{os.path.join(self.speaker_embeddings.get('repo_or_path'					,       '/'       )					,       voice_preset_paths[key]       )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""       )

            UpperCAmelCase							=     np.load(__lowerCamelCase       )

         return voice_preset_dict





      def        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					Optional[dict] = None       )    ->   Any:






         """simple docstring"""







         for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
            if key not in voice_preset:
               raise ValueError(F"""Voice preset unrecognized, missing {key} as a key."""       )

            if not isinstance(voice_preset[key]					,       np.ndarray       ):
               raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]       )}D ndarray."""       )

            if len(voice_preset[key].shape       ) != self.preset_shape[key]:
               raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]       )}D ndarray."""       )





      def __call__(		self						:					int					,       __lowerCamelCase						:					Optional[Any]=None					,       __lowerCamelCase						:					List[str]=None					,       __lowerCamelCase						:					str="pt"					,       __lowerCamelCase						:					Any=2_5_6					,       __lowerCamelCase						:					List[str]=False					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					Optional[Any]=False					,       **__lowerCamelCase						:					Optional[int]					,       )    ->   List[Any]:






         """simple docstring"""







         if voice_preset is not None and not isinstance(__lowerCamelCase					,       __lowerCamelCase       ):
            if (
                isinstance(__lowerCamelCase					,       __lowerCamelCase       )
                and self.speaker_embeddings is not None
                and voice_preset in self.speaker_embeddings
            ):
               UpperCAmelCase							=     self._load_voice_preset(__lowerCamelCase       )

            else:
               if isinstance(__lowerCamelCase					,       __lowerCamelCase       ) and not voice_preset.endswith(""".npz"""       ):
                  UpperCAmelCase							=     voice_preset + """.npz"""

               UpperCAmelCase							=     np.load(__lowerCamelCase       )

         if voice_preset is not None:
            self._validate_voice_preset_dict(__lowerCamelCase					,       **__lowerCamelCase       )
            UpperCAmelCase							=     BatchFeature(data=__lowerCamelCase					,       tensor_type=__lowerCamelCase       )

         UpperCAmelCase							=     self.tokenizer(
             __lowerCamelCase					,       return_tensors=__lowerCamelCase					,       padding="""max_length"""					,       max_length=__lowerCamelCase					,       return_attention_mask=__lowerCamelCase					,       return_token_type_ids=__lowerCamelCase					,       add_special_tokens=__lowerCamelCase					,       **__lowerCamelCase					,       )

         if voice_preset is not None:
            UpperCAmelCase							=     voice_preset

         return encoded_text







import math






class   __lowercase				:





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					list[list[float]]					,       __lowerCamelCase						:					list[int]       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     0.0
         UpperCAmelCase							=     0.0
         for i in range(len(__lowerCamelCase       )       ):
            da += math.pow((sample[i] - weights[0][i])					,       2       )
            da += math.pow((sample[i] - weights[1][i])					,       2       )
            return 0 if da > da else 1
         return 0





      def        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					list[list[int | float]]					,       __lowerCamelCase						:					list[int]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					float       )    ->   list[list[int | float]]:






         """simple docstring"""







         for i in range(len(__lowerCamelCase       )       ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights

def   _UpperCamelCase				(						)						->None:
   # Training Examples ( m, n )
   UpperCAmelCase							=     [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

   # weight initialization ( n, C )
   UpperCAmelCase							=     [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

   # training
   UpperCAmelCase							=     SelfOrganizingMap()
   UpperCAmelCase							=     3
   UpperCAmelCase							=     0.5

   for _ in range(lowerCAmelCase_   ):
      for j in range(len(lowerCAmelCase_   )   ):
         # training sample
         UpperCAmelCase							=     training_samples[j]

         # Compute the winning vector
         UpperCAmelCase							=     self_organizing_map.get_winner(lowerCAmelCase_  ,						lowerCAmelCase_   )

         # Update the winning vector
         UpperCAmelCase							=     self_organizing_map.update(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

    # classify test sample
   UpperCAmelCase							=     [0, 0, 0, 1]
   UpperCAmelCase							=     self_organizing_map.get_winner(lowerCAmelCase_  ,						lowerCAmelCase_   )

   # results
   print(F"""Clusters that the test sample belongs to : {winner}"""   )
   print(F"""Weights that have been trained : {weights}"""   )


# running the main() function
if __name__ == "__main__":
 main()



