





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       (			lowercase__			):



      """simple docstring"""






      UpperCAmelCase_  :					List[str]      			='''AutoTokenizer'''
      UpperCAmelCase_  :					Optional[Any]      			=['''tokenizer''']

      UpperCAmelCase_  :					int      			={
          '''semantic_prompt''': 1,
          '''coarse_prompt''': 2,
          '''fine_prompt''': 2,
      }







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






        '''simple docstring'''






        super().__init__(SCREAMING_SNAKE_CASE__      )

        __snake_case      : Tuple						=				speaker_embeddings







      @classmethod
      def   UpperCAmelCase						(       cls ,  UpperCAmelCase ,  UpperCAmelCase="speaker_embeddings_path.json" ,  **UpperCAmelCase      )				->      Optional[Any]:






        '''simple docstring'''






        if speaker_embeddings_dict_path is not None:
          __snake_case      : int						=				get_file_from_repo(
              SCREAMING_SNAKE_CASE__ ,  SCREAMING_SNAKE_CASE__ ,  subfolder=kwargs.pop("subfolder" ,  SCREAMING_SNAKE_CASE__      ) ,  cache_dir=kwargs.pop("cache_dir" ,  SCREAMING_SNAKE_CASE__      ) ,  force_download=kwargs.pop("force_download" ,  SCREAMING_SNAKE_CASE__      ) ,  proxies=kwargs.pop("proxies" ,  SCREAMING_SNAKE_CASE__      ) ,  resume_download=kwargs.pop("resume_download" ,  SCREAMING_SNAKE_CASE__      ) ,  local_files_only=kwargs.pop("local_files_only" ,  SCREAMING_SNAKE_CASE__      ) ,  use_auth_token=kwargs.pop("use_auth_token" ,  SCREAMING_SNAKE_CASE__      ) ,  revision=kwargs.pop("revision" ,  SCREAMING_SNAKE_CASE__      ) ,  )
          if speaker_embeddings_path is None:
            logger.warning(
                F"""`{os.path.join(SCREAMING_SNAKE_CASE__ ,  SCREAMING_SNAKE_CASE__      )}` 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`."""      )
            __snake_case      : Any						=				None
          else:
            with open(SCREAMING_SNAKE_CASE__      ) as speaker_embeddings_json:
              __snake_case      : int						=				json.load(SCREAMING_SNAKE_CASE__      )
        else:
          __snake_case      : str						=				None

        __snake_case      : List[str]						=				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ,  **SCREAMING_SNAKE_CASE__      )

        return cls(tokenizer=SCREAMING_SNAKE_CASE__ ,  speaker_embeddings=SCREAMING_SNAKE_CASE__      )







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase="speaker_embeddings_path.json" ,  UpperCAmelCase="speaker_embeddings" ,  UpperCAmelCase = False ,  **UpperCAmelCase ,  )				->      Union[str, Any]:






        '''simple docstring'''






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

          __snake_case      : Union[str, Any]						=				{}

          __snake_case      : List[str]						=				save_directory

          for prompt_key in self.speaker_embeddings:
            if prompt_key != "repo_or_path":
              __snake_case      : Tuple						=				self._load_voice_preset(SCREAMING_SNAKE_CASE__      )

              __snake_case      : Optional[int]						=				{}
              for key in self.speaker_embeddings[prompt_key]:
                np.save(
                    os.path.join(
                        embeddings_dict["repo_or_path"] ,  SCREAMING_SNAKE_CASE__ ,  F"""{prompt_key}_{key}"""      ) ,  voice_preset[key] ,  allow_pickle=SCREAMING_SNAKE_CASE__ ,  )
                __snake_case      : List[str]						=				os.path.join(SCREAMING_SNAKE_CASE__ ,  F"""{prompt_key}_{key}.npy"""      )

              __snake_case      : int						=				tmp_dict

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

        super().save_pretrained(SCREAMING_SNAKE_CASE__ ,  SCREAMING_SNAKE_CASE__ ,  **SCREAMING_SNAKE_CASE__      )







      def   UpperCAmelCase						(       self ,  UpperCAmelCase = None ,  **UpperCAmelCase      )				->      int:






        '''simple docstring'''






        __snake_case      : Union[str, Any]						=				self.speaker_embeddings[voice_preset]

        __snake_case      : str						=				{}
        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}]."""      )

          __snake_case      : Dict						=				get_file_from_repo(
              self.speaker_embeddings.get("repo_or_path" ,  "/"      ) ,  voice_preset_paths[key] ,  subfolder=kwargs.pop("subfolder" ,  SCREAMING_SNAKE_CASE__      ) ,  cache_dir=kwargs.pop("cache_dir" ,  SCREAMING_SNAKE_CASE__      ) ,  force_download=kwargs.pop("force_download" ,  SCREAMING_SNAKE_CASE__      ) ,  proxies=kwargs.pop("proxies" ,  SCREAMING_SNAKE_CASE__      ) ,  resume_download=kwargs.pop("resume_download" ,  SCREAMING_SNAKE_CASE__      ) ,  local_files_only=kwargs.pop("local_files_only" ,  SCREAMING_SNAKE_CASE__      ) ,  use_auth_token=kwargs.pop("use_auth_token" ,  SCREAMING_SNAKE_CASE__      ) ,  revision=kwargs.pop("revision" ,  SCREAMING_SNAKE_CASE__      ) ,  )
          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."""      )

          __snake_case      : str						=				np.load(SCREAMING_SNAKE_CASE__      )

        return voice_preset_dict







      def   UpperCAmelCase						(       self ,  UpperCAmelCase = None      )				->      int:






        '''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 ,  UpperCAmelCase=None ,  UpperCAmelCase=None ,  UpperCAmelCase="pt" ,  UpperCAmelCase=256 ,  UpperCAmelCase=False ,  UpperCAmelCase=True ,  UpperCAmelCase=False ,  **UpperCAmelCase ,  )				->      List[str]:






        '''simple docstring'''






        if voice_preset is not None and not isinstance(SCREAMING_SNAKE_CASE__ ,  SCREAMING_SNAKE_CASE__      ):
          if (
              isinstance(SCREAMING_SNAKE_CASE__ ,  SCREAMING_SNAKE_CASE__      )
              and self.speaker_embeddings is not None
              and voice_preset in self.speaker_embeddings
          ):
            __snake_case      : Optional[int]						=				self._load_voice_preset(SCREAMING_SNAKE_CASE__      )

          else:
            if isinstance(SCREAMING_SNAKE_CASE__ ,  SCREAMING_SNAKE_CASE__      ) and not voice_preset.endswith(".npz"      ):
              __snake_case      : Optional[Any]						=				voice_preset + '.npz'

            __snake_case      : Any						=				np.load(SCREAMING_SNAKE_CASE__      )

        if voice_preset is not None:
          self._validate_voice_preset_dict(SCREAMING_SNAKE_CASE__ ,  **SCREAMING_SNAKE_CASE__      )
          __snake_case      : Dict						=				BatchFeature(data=SCREAMING_SNAKE_CASE__ ,  tensor_type=SCREAMING_SNAKE_CASE__      )

        __snake_case      : List[Any]						=				self.tokenizer(
            SCREAMING_SNAKE_CASE__ ,  return_tensors=SCREAMING_SNAKE_CASE__ ,  padding="max_length" ,  max_length=SCREAMING_SNAKE_CASE__ ,  return_attention_mask=SCREAMING_SNAKE_CASE__ ,  return_token_type_ids=SCREAMING_SNAKE_CASE__ ,  add_special_tokens=SCREAMING_SNAKE_CASE__ ,  **SCREAMING_SNAKE_CASE__ ,  )

        if voice_preset is not None:
          __snake_case      : Optional[int]						=				voice_preset

        return encoded_text





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_      : Optional[int]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Union[str, Any]        =						['''pixel_values''']
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict[str, int]] = None     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BILINEAR     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Union[int, float] = 1 / 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			None:
             super().__init__(**SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  size if size is not None else {'shortest_edge': 2_5_6}
             a_					:		Any							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  do_resize
             a_					:		Dict							=  size
             a_					:		Optional[Any]							=  resample
             a_					:		Optional[int]							=  do_center_crop
             a_					:		Dict							=  crop_size
             a_					:		int							=  do_rescale
             a_					:		int							=  rescale_factor
             a_					:		Tuple							=  do_normalize
             a_					:		int							=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             a_					:		Tuple							=  image_std if image_std is not None else IMAGENET_STANDARD_STD
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BICUBIC     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		List[Any]							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             if "shortest_edge" not in size:
                    raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
             a_					:		Tuple							=  get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,							size=size['shortest_edge']     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             return resize(SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ,							resample=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		str							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             return center_crop(SCREAMING_SNAKE_CASE__     ,							size=(size['height'], size['width'])     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       float     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			np.ndarray:
             return rescale(SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Union[float, List[float]]     ,							SCREAMING_SNAKE_CASE__				:       Union[float, List[float]]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     ,							)		->			np.ndarray:
             return normalize(SCREAMING_SNAKE_CASE__     ,							mean=SCREAMING_SNAKE_CASE__     ,							std=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       ImageInput     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = None     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[float] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[float, List[float]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, TensorType]] = None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			Union[str, Any]:
             a_					:		List[str]							=  do_resize if do_resize is not None else self.do_resize
             a_					:		Dict							=  size if size is not None else self.size
             a_					:		Dict							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  resample if resample is not None else self.resample
             a_					:		List[Any]							=  do_center_crop if do_center_crop is not None else self.do_center_crop
             a_					:		int							=  crop_size if crop_size is not None else self.crop_size
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  do_rescale if do_rescale is not None else self.do_rescale
             a_					:		Dict							=  rescale_factor if rescale_factor is not None else self.rescale_factor
             a_					:		Any							=  do_normalize if do_normalize is not None else self.do_normalize
             a_					:		str							=  image_mean if image_mean is not None else self.image_mean
             a_					:		Dict							=  image_std if image_std is not None else self.image_std

             a_					:		Optional[int]							=  make_list_of_images(SCREAMING_SNAKE_CASE__     )

             if not valid_images(SCREAMING_SNAKE_CASE__     ):
                    raise ValueError(
                        'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                        'torch.Tensor, tf.Tensor or jax.ndarray.'     )

             if do_resize and size is None:
                    raise ValueError('Size must be specified if do_resize is True.'     )

             if do_center_crop and crop_size is None:
                    raise ValueError('Crop size must be specified if do_center_crop is True.'     )

             if do_rescale and rescale_factor is None:
                    raise ValueError('Rescale factor must be specified if do_rescale is True.'     )

             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError('Image mean and std must be specified if do_normalize is True.'     )

             # All transformations expect numpy arrays.
             a_					:		Any							=  [to_numpy_array(SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_resize:
                    a_					:		str							=  [self.resize(image=SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ,							resample=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_center_crop:
                    a_					:		int							=  [self.center_crop(image=SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_rescale:
                    a_					:		Optional[Any]							=  [self.rescale(image=SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ) for image in images]

             if do_normalize:
                    a_					:		List[Any]							=  [self.normalize(image=SCREAMING_SNAKE_CASE__     ,							mean=SCREAMING_SNAKE_CASE__     ,							std=SCREAMING_SNAKE_CASE__     ) for image in images]

             a_					:		Dict							=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) for image in images]

             a_					:		Tuple							=  {'pixel_values': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,							tensor_type=SCREAMING_SNAKE_CASE__     )


