



"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


A:     List[str]										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     List[Any]		=		['pixel_values']

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = 1 / 255   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = True   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   None:





    '''simple docstring'''




    super().__init__(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			size if size is not None else {"""shortest_edge""": 224}
    UpperCAmelCase			:      Optional[Any]			=			get_size_dict(_SCREAMING_SNAKE_CASE   ,  default_to_square=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
    UpperCAmelCase			:      Optional[int]			=			get_size_dict(_SCREAMING_SNAKE_CASE   ,  param_name="""crop_size"""		)

    UpperCAmelCase			:      List[str]			=			do_resize
    UpperCAmelCase			:      str			=			size
    UpperCAmelCase			:      List[Any]			=			resample
    UpperCAmelCase			:      Union[str, Any]			=			do_rescale
    UpperCAmelCase			:      List[Any]			=			rescale_factor
    UpperCAmelCase			:      List[Any]			=			do_center_crop
    UpperCAmelCase			:      Dict			=			crop_size
    UpperCAmelCase			:      Optional[Any]			=			do_flip_channel_order

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = PIL.Image.BILINEAR   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   np.ndarray:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			get_size_dict(_SCREAMING_SNAKE_CASE   ,  default_to_square=_SCREAMING_SNAKE_CASE		)
    if "shortest_edge" not in size:
     raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"		)
    UpperCAmelCase			:      Optional[int]			=			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   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   np.ndarray:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			get_size_dict(_SCREAMING_SNAKE_CASE		)
    if "height" not in size or "width" not in size:
     raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"		)
    return center_crop(_SCREAMING_SNAKE_CASE   ,  size=(size["""height"""], size["""width"""])   ,  data_format=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Any:





    '''simple docstring'''




    return rescale(_SCREAMING_SNAKE_CASE   ,  scale=_SCREAMING_SNAKE_CASE   ,  data_format=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   np.ndarray:





    '''simple docstring'''




    return flip_channel_order(_SCREAMING_SNAKE_CASE   ,  data_format=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   PIL.Image.Image:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			do_resize if do_resize is not None else self.do_resize
    UpperCAmelCase			:      Any			=			resample if resample is not None else self.resample
    UpperCAmelCase			:      str			=			do_rescale if do_rescale is not None else self.do_rescale
    UpperCAmelCase			:      Any			=			rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCAmelCase			:      Union[str, Any]			=			do_center_crop if do_center_crop is not None else self.do_center_crop
    UpperCAmelCase			:      int			=			(
        do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
    )

    UpperCAmelCase			:      List[Any]			=			size if size is not None else self.size
    UpperCAmelCase			:      str			=			get_size_dict(_SCREAMING_SNAKE_CASE   ,  default_to_square=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			crop_size if crop_size is not None else self.crop_size
    UpperCAmelCase			:      Dict			=			get_size_dict(_SCREAMING_SNAKE_CASE   ,  param_name="""crop_size"""		)

    UpperCAmelCase			:      Optional[Any]			=			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_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

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

    # All transformations expect numpy arrays.
    UpperCAmelCase			:      str			=			[to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

    if do_resize:
     UpperCAmelCase			:      List[Any]			=			[self.resize(image=_SCREAMING_SNAKE_CASE   ,  size=_SCREAMING_SNAKE_CASE   ,  resample=_SCREAMING_SNAKE_CASE		) for image in images]

    if do_center_crop:
     UpperCAmelCase			:      Union[str, Any]			=			[self.center_crop(image=_SCREAMING_SNAKE_CASE   ,  size=_SCREAMING_SNAKE_CASE		) for image in images]

    if do_rescale:
     UpperCAmelCase			:      Dict			=			[self.rescale(image=_SCREAMING_SNAKE_CASE   ,  scale=_SCREAMING_SNAKE_CASE		) for image in images]

    # the pretrained checkpoints assume images are BGR, not RGB
    if do_flip_channel_order:
     UpperCAmelCase			:      Tuple			=			[self.flip_channel_order(image=_SCREAMING_SNAKE_CASE		) for image in images]

    UpperCAmelCase			:      List[Any]			=			[to_channel_dimension_format(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) for image in images]

    UpperCAmelCase			:      List[str]			=			{"""pixel_values""": images}
    return BatchFeature(data=_SCREAMING_SNAKE_CASE   ,  tensor_type=_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			outputs.logits

    # Resize logits and compute semantic segmentation maps
    if target_sizes is not None:
     if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
      raise ValueError(
          """Make sure that you pass in as many target sizes as the batch dimension of the logits"""		)

     if is_torch_tensor(_SCREAMING_SNAKE_CASE		):
      UpperCAmelCase			:      List[Any]			=			target_sizes.numpy()

     UpperCAmelCase			:      str			=			[]

     for idx in range(len(_SCREAMING_SNAKE_CASE		)		):
      UpperCAmelCase			:      List[str]			=			torch.nn.functional.interpolate(
          logits[idx].unsqueeze(dim=0		)   ,  size=target_sizes[idx]   ,  mode="""bilinear"""   ,  align_corners=_SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Any			=			resized_logits[0].argmax(dim=0		)
      semantic_segmentation.append(_SCREAMING_SNAKE_CASE		)
    else:
     UpperCAmelCase			:      Tuple			=			logits.argmax(dim=1		)
     UpperCAmelCase			:      List[str]			=			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

    return semantic_segmentation





"""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


A:     List[Any]										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[int]		=		'AutoTokenizer'
   __lowerCAmelCase						:     str		=		['tokenizer']

   __lowerCAmelCase						:     Any		=		{
       'semantic_prompt': 1,
       'coarse_prompt': 2,
       'fine_prompt': 2,
   }

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   int:





    '''simple docstring'''




    super().__init__(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			speaker_embeddings

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    if speaker_embeddings_dict_path is not None:
     UpperCAmelCase			:      Any			=			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\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`."		)
      UpperCAmelCase			:      Optional[int]			=			None
     else:
      with open(_SCREAMING_SNAKE_CASE		) as speaker_embeddings_json:
       UpperCAmelCase			:      List[str]			=			json.load(_SCREAMING_SNAKE_CASE		)
    else:
     UpperCAmelCase			:      List[str]			=			None

    UpperCAmelCase			:      List[Any]			=			AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

    return cls(tokenizer=_SCREAMING_SNAKE_CASE   ,  speaker_embeddings=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings"   ,  _SCREAMING_SNAKE_CASE = False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   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		)

     UpperCAmelCase			:      str			=			{}

     UpperCAmelCase			:      Union[str, Any]			=			save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       UpperCAmelCase			:      Optional[Any]			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

       UpperCAmelCase			:      Any			=			{}
       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   ,  )
        UpperCAmelCase			:      Optional[int]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}.npy"		)

       UpperCAmelCase			:      Tuple			=			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 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




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

    UpperCAmelCase			:      List[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"""   ,  _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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."		)

     UpperCAmelCase			:      List[str]			=			np.load(_SCREAMING_SNAKE_CASE		)

    return voice_preset_dict

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[str]:





    '''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   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="pt"   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[int]:





    '''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
     ):
      UpperCAmelCase			:      Dict			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

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

      UpperCAmelCase			:      Union[str, Any]			=			np.load(_SCREAMING_SNAKE_CASE		)

    if voice_preset is not None:
     self._validate_voice_preset_dict(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[Any]			=			BatchFeature(data=_SCREAMING_SNAKE_CASE   ,  tensor_type=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[str]			=			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:
     UpperCAmelCase			:      List[Any]			=			voice_preset

    return encoded_text
