'''simple docstring'''





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


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Any  			=   '''AutoTokenizer'''
   lowerCAmelCase      :    List[str]  			=   ['''tokenizer''']

   lowerCAmelCase      :    str  			=   {
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }



   def __init__(	self					:  Tuple		,  _lowercase					:  Tuple		,  _lowercase					:  str=None							):



    """simple docstring"""



    super().__init__(A__							)

    _UpperCamelCase:     str							= speaker_embeddings



   @classmethod
   def   lowerCAmelCase						(	cls					:  Dict		,  _lowercase					:  List[str]		,  _lowercase					:  Optional[Any]="speaker_embeddings_path.json"		,  **_lowercase					:  Any							):



    """simple docstring"""



    if speaker_embeddings_dict_path is not None:
     _UpperCamelCase:     Union[str, Any]							= get_file_from_repo(
         A__		,  A__		,  subfolder=kwargs.pop('''subfolder'''		,  A__							)		,  cache_dir=kwargs.pop('''cache_dir'''		,  A__							)		,  force_download=kwargs.pop('''force_download'''		,  A__							)		,  proxies=kwargs.pop('''proxies'''		,  A__							)		,  resume_download=kwargs.pop('''resume_download'''		,  A__							)		,  local_files_only=kwargs.pop('''local_files_only'''		,  A__							)		,  use_auth_token=kwargs.pop('''use_auth_token'''		,  A__							)		,  revision=kwargs.pop('''revision'''		,  A__							)		,  )
     if speaker_embeddings_path is None:
      logger.warning(
          f"""`{os.path.join(A__		,  A__							)}` 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:     Optional[Any]							= None
     else:
      with open(A__							) as speaker_embeddings_json:
       _UpperCamelCase:     List[Any]							= json.load(A__							)
    else:
     _UpperCamelCase:     List[Any]							= None

    _UpperCamelCase:     Dict							= AutoTokenizer.from_pretrained(A__		,  **A__							)

    return cls(tokenizer=A__		,  speaker_embeddings=A__							)



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  List[Any]		,  _lowercase					:  List[str]="speaker_embeddings_path.json"		,  _lowercase					:  int="speaker_embeddings"		,  _lowercase					:  Optional[int] = False		,  **_lowercase					:  Any		,  ):



    """simple docstring"""



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

     _UpperCamelCase:     List[str]							= {}

     _UpperCamelCase:     Dict							= save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       _UpperCamelCase:     Union[str, Any]							= self._load_voice_preset(A__							)

       _UpperCamelCase:     Any							= {}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict['''repo_or_path''']		,  A__		,  f"""{prompt_key}_{key}"""							)		,  voice_preset[key]		,  allow_pickle=A__		,  )
        _UpperCamelCase:     Tuple							= os.path.join(A__		,  f"""{prompt_key}_{key}.npy"""							)

       _UpperCamelCase:     List[Any]							= tmp_dict

     with open(os.path.join(A__		,  A__							)		,  '''w'''							) as fp:
      json.dump(A__		,  A__							)

    super().save_pretrained(A__		,  A__		,  **A__							)



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  Any = None		,  **_lowercase					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= self.speaker_embeddings[voice_preset]

    _UpperCamelCase:     Union[str, Any]							= {}
    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:     List[str]							= get_file_from_repo(
         self.speaker_embeddings.get('''repo_or_path'''		,  '''/'''							)		,  voice_preset_paths[key]		,  subfolder=kwargs.pop('''subfolder'''		,  A__							)		,  cache_dir=kwargs.pop('''cache_dir'''		,  A__							)		,  force_download=kwargs.pop('''force_download'''		,  A__							)		,  proxies=kwargs.pop('''proxies'''		,  A__							)		,  resume_download=kwargs.pop('''resume_download'''		,  A__							)		,  local_files_only=kwargs.pop('''local_files_only'''		,  A__							)		,  use_auth_token=kwargs.pop('''use_auth_token'''		,  A__							)		,  revision=kwargs.pop('''revision'''		,  A__							)		,  )
     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:     Tuple							= np.load(A__							)

    return voice_preset_dict



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  Dict = None							):



    """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					:  Optional[int]		,  _lowercase					:  List[Any]=None		,  _lowercase					:  List[str]=None		,  _lowercase					:  Dict="pt"		,  _lowercase					:  Union[str, Any]=256		,  _lowercase					:  Dict=False		,  _lowercase					:  Optional[int]=True		,  _lowercase					:  str=False		,  **_lowercase					:  Union[str, Any]		,  ):



    """simple docstring"""



    if voice_preset is not None and not isinstance(A__		,  A__							):
     if (
         isinstance(A__		,  A__							)
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      _UpperCamelCase:     Union[str, Any]							= self._load_voice_preset(A__							)

     else:
      if isinstance(A__		,  A__							) and not voice_preset.endswith('''.npz'''							):
       _UpperCamelCase:     str							= voice_preset + """.npz"""

      _UpperCamelCase:     str							= np.load(A__							)

    if voice_preset is not None:
     self._validate_voice_preset_dict(A__		,  **A__							)
     _UpperCamelCase:     Tuple							= BatchFeature(data=A__		,  tensor_type=A__							)

    _UpperCamelCase:     Tuple							= self.tokenizer(
        A__		,  return_tensors=A__		,  padding='''max_length'''		,  max_length=A__		,  return_attention_mask=A__		,  return_token_type_ids=A__		,  add_special_tokens=A__		,  **A__		,  )

    if voice_preset is not None:
     _UpperCamelCase:     Any							= voice_preset

    return encoded_text
from __future__ import annotations




def       lowerCAmelCase_     (		lowercase:							int = 4		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= abs(lowercase		) or 4
 return [[1 + x + y * row_size for x in range(lowercase		)] for y in range(lowercase		)]




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(transpose(lowercase		)		)
 # OR.. transpose(reverse_column(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(reverse_column(lowercase		)		)
 # OR.. reverse_column(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_column(transpose(lowercase		)		)
 # OR.. transpose(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     int							= [list(lowercase		) for x in zip(*lowercase		)]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= matrix[::-1]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= [x[::-1] for x in matrix]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				None:







 '''simple docstring'''
 for i in matrix:
  print(*lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 90 counterclockwise:\n''')
  print_matrix(rotate_aa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 180:\n''')
  print_matrix(rotate_aaa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 270 counterclockwise:\n''')
  print_matrix(rotate_aaa(matrix))