




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     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   'AutoTokenizer'
      UpperCAmelCase__      :	Union[str, Any]    		=   ['tokenizer']

      UpperCAmelCase__      :	Optional[Any]    		=   {
          'semantic_prompt': 1,
          'coarse_prompt': 2,
          'fine_prompt': 2,
      }
      def __init__(				self: str    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Optional[Any]=None   ):
             super().__init__(UpperCamelCase_   )

             __lowerCamelCase   =						speaker_embeddings
      @classmethod
      def 			lowerCAmelCase__						(				cls: List[str]    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Tuple="speaker_embeddings_path.json"    ,       **UpperCamelCase_: Dict   ):

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

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

             return cls(tokenizer=UpperCamelCase_    ,       speaker_embeddings=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: int    ,       UpperCamelCase_: int="speaker_embeddings_path.json"    ,       UpperCamelCase_: Optional[Any]="speaker_embeddings"    ,       UpperCamelCase_: bool = False    ,       **UpperCamelCase_: Union[str, Any]    ,       ):
             if self.speaker_embeddings is not None:
                    os.makedirs(os.path.join(UpperCamelCase_    ,       UpperCamelCase_    ,       """v2"""   )    ,       exist_ok=UpperCamelCase_   )

                    __lowerCamelCase   =						{}

                    __lowerCamelCase   =						save_directory

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

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

                                  __lowerCamelCase   =						tmp_dict

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

             super().save_pretrained(UpperCamelCase_    ,       UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: str = None    ,       **UpperCamelCase_: List[Any]   ):
             __lowerCamelCase   =						self.speaker_embeddings[voice_preset]

             __lowerCamelCase   =						{}
             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}].'   )

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

                    __lowerCamelCase   =						np.load(UpperCamelCase_   )

             return voice_preset_dict
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Optional[dict] = None   ):
             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: Any    ,       UpperCamelCase_: Optional[int]=None    ,       UpperCamelCase_: Optional[Any]=None    ,       UpperCamelCase_: List[Any]="pt"    ,       UpperCamelCase_: str=2_56    ,       UpperCamelCase_: List[Any]=False    ,       UpperCamelCase_: Union[str, Any]=True    ,       UpperCamelCase_: Optional[Any]=False    ,       **UpperCamelCase_: Dict    ,       ):
             if voice_preset is not None and not isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    if (
                        isinstance(UpperCamelCase_    ,       UpperCamelCase_   )
                        and self.speaker_embeddings is not None
                        and voice_preset in self.speaker_embeddings
                    ):
                           __lowerCamelCase   =						self._load_voice_preset(UpperCamelCase_   )

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

                           __lowerCamelCase   =						np.load(UpperCamelCase_   )

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

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

             if voice_preset is not None:
                    __lowerCamelCase   =						voice_preset

             return encoded_text







from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_									=	[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
UpperCAmelCase_									=	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for i in range(len(A__	)	):
              __lowerCamelCase   =						[]
              for j in range(len(cells[i]	)	):
                     # Get the number of live neighbours
                     __lowerCamelCase   =						0
                     if i > 0 and j > 0:
                            neighbour_count += cells[i - 1][j - 1]
                     if i > 0:
                            neighbour_count += cells[i - 1][j]
                     if i > 0 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i - 1][j + 1]
                     if j > 0:
                            neighbour_count += cells[i][j - 1]
                     if j < len(cells[i]	) - 1:
                            neighbour_count += cells[i][j + 1]
                     if i < len(A__	) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(A__	) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(A__	) - 1 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i + 1][j + 1]

                     # Rules of the game of life (excerpt from Wikipedia):
                     # 1. Any live cell with two or three live neighbours survives.
                     # 2. Any dead cell with three live neighbours becomes a live cell.
                     # 3. All other live cells die in the next generation.
                     #    Similarly, all other dead cells stay dead.
                     __lowerCamelCase   =						cells[i][j] == 1
                     if (
                         (alive and 2 <= neighbour_count <= 3)
                         or not alive
                         and neighbour_count == 3
                     ):
                            next_generation_row.append(1	)
                     else:
                            next_generation_row.append(0	)

              next_generation.append(A__	)
       return next_generation




def 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for _ in range(A__	):
              # Create output image
              __lowerCamelCase   =						Image.new("""RGB"""  ,			(len(cells[0]	), len(A__	))	)
              __lowerCamelCase   =						img.load()

              # Save cells to image
              for x in range(len(A__	)	):
                     for y in range(len(cells[0]	)	):
                            __lowerCamelCase   =						255 - cells[y][x] * 255
                            __lowerCamelCase   =						(colour, colour, colour)

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						new_generation(A__	)
       return images


if __name__ == "__main__":
       UpperCAmelCase_									=	generate_images(GLIDER, 16)
       images[0].save('out.gif', save_all=True, append_images=images[1:])

