

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 A__    ( A ):



 """simple docstring"""







 _lowercase     :					str      =						'''AutoTokenizer'''
 _lowercase     :					Any      =						['''tokenizer''']

 _lowercase     :					Union[str, Any]      =						{
     '''semantic_prompt''': 1,
     '''coarse_prompt''': 2,
     '''fine_prompt''': 2,
 }

 def __init__(		self :	Dict     ,						A_ :	List[str]     ,						A_ :	Tuple=None       ):






      '''simple docstring'''




      super().__init__(A_       )

      _lowerCAmelCase   :					Optional[Any]        =    speaker_embeddings

 @classmethod
 def    __magic_name__       (		cls :	int     ,						A_ :	int     ,						A_ :	List[str]="speaker_embeddings_path.json"     ,						**A_ :	Optional[int]       ):






      '''simple docstring'''




      if speaker_embeddings_dict_path is not None:
           _lowerCAmelCase   :					Optional[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`."""       )
                _lowerCAmelCase   :					Union[str, Any]        =    None
           else:
                with open(A_       ) as speaker_embeddings_json:
                     _lowerCAmelCase   :					str        =    json.load(A_       )
      else:
           _lowerCAmelCase   :					Optional[Any]        =    None

      _lowerCAmelCase   :					int        =    AutoTokenizer.from_pretrained(A_     ,						**A_       )

      return cls(tokenizer=A_     ,						speaker_embeddings=A_       )

 def    __magic_name__       (		self :	Any     ,						A_ :	Union[str, Any]     ,						A_ :	List[Any]="speaker_embeddings_path.json"     ,						A_ :	Any="speaker_embeddings"     ,						A_ :	bool = False     ,						**A_ :	Optional[Any]     ,						):






      '''simple docstring'''




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

           _lowerCAmelCase   :					Optional[Any]        =    {}

           _lowerCAmelCase   :					List[str]        =    save_directory

           for prompt_key in self.speaker_embeddings:
                if prompt_key != "repo_or_path":
                     _lowerCAmelCase   :					Any        =    self._load_voice_preset(A_       )

                     _lowerCAmelCase   :					str        =    {}
                     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_     ,						)
                          _lowerCAmelCase   :					Optional[int]        =    os.path.join(A_     ,						F"""{prompt_key}_{key}.npy"""       )

                     _lowerCAmelCase   :					Any        =    tmp_dict

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

      super().save_pretrained(A_     ,						A_     ,						**A_       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	str = None     ,						**A_ :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.speaker_embeddings[voice_preset]

      _lowerCAmelCase   :					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}]."""       )

           _lowerCAmelCase   :					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."""       )

           _lowerCAmelCase   :					List[str]        =    np.load(A_       )

      return voice_preset_dict

 def    __magic_name__       (		self :	Any     ,						A_ :	Optional[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 :	List[Any]     ,						A_ :	Union[str, Any]=None     ,						A_ :	Optional[Any]=None     ,						A_ :	List[str]="pt"     ,						A_ :	str=2_5_6     ,						A_ :	Tuple=False     ,						A_ :	Optional[int]=True     ,						A_ :	int=False     ,						**A_ :	Tuple     ,						):






      '''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
           ):
                _lowerCAmelCase   :					str        =    self._load_voice_preset(A_       )

           else:
                if isinstance(A_     ,						A_       ) and not voice_preset.endswith(".npz"       ):
                     _lowerCAmelCase   :					Dict        =    voice_preset + ".npz"

                _lowerCAmelCase   :					int        =    np.load(A_       )

      if voice_preset is not None:
           self._validate_voice_preset_dict(A_     ,						**A_       )
           _lowerCAmelCase   :					str        =    BatchFeature(data=A_     ,						tensor_type=A_       )

      _lowerCAmelCase   :					Optional[int]        =    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:
           _lowerCAmelCase   :					List[Any]        =    voice_preset

      return encoded_text









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


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = True     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else {"shortest_edge": 2_2_4}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Any        =    crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						param_name="crop_size"       )

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

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						default_to_square=A_       )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Union[str, Any]        =    get_resize_output_image_size(A_     ,						size=size["shortest_edge"]     ,						default_to_square=A_       )
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    get_size_dict(A_       )
      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(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Any     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Optional[Union[str, ChannelDimension]] = None       ):






      '''simple docstring'''




      return flip_channel_order(A_     ,						data_format=A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	float = None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Dict        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					str        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					List[Any]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					Tuple        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					str        =    (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Optional[Any]        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Dict        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           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.
      _lowerCAmelCase   :					Optional[int]        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					Any        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Tuple        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					Any        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

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

      _lowerCAmelCase   :					Optional[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )


 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




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

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

           if is_torch_tensor(A_       ):
                _lowerCAmelCase   :					Dict        =    target_sizes.numpy()

           _lowerCAmelCase   :					List[Any]        =    []

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

      return semantic_segmentation






