

'''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


__lowerCamelCase		       =    logging.get_logger(__name__)


class 					A__      (      _snake_case			):
    lowercase						=		"AutoTokenizer"
    lowercase						=		["tokenizer"]

    lowercase						=		{
        "semantic_prompt": 1,
        "coarse_prompt": 2,
        "fine_prompt": 2,
    }



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      Tuple:





           '''simple docstring'''
           super().__init__(UpperCamelCase__       )

           A_						=      speaker_embeddings



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





           '''simple docstring'''

           if speaker_embeddings_dict_path is not None:
                  A_						=      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
                    , 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`.'''       )
                         A_						=      None
                  else:
                         with open(UpperCamelCase__       ) as speaker_embeddings_json:
                                A_						=      json.load(UpperCamelCase__       )
           else:
                  A_						=      None

           A_						=      AutoTokenizer.from_pretrained(UpperCamelCase__		,				**UpperCamelCase__       )

           return cls(tokenizer=UpperCamelCase__		,				speaker_embeddings=UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__="speaker_embeddings_path.json"		,				UpperCamelCase__="speaker_embeddings"		,				UpperCamelCase__ = False		,				**UpperCamelCase__		,				)						->      Optional[int]:





           '''simple docstring'''
           if self.speaker_embeddings is not None:
                  os.makedirs(os.path.join(UpperCamelCase__		,				UpperCamelCase__		,				"""v2"""       )		,				exist_ok=UpperCamelCase__       )

                  A_						=      {}

                  A_						=      save_directory

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

                                A_						=      {}
                                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__		,				)
                                       A_						=      os.path.join(UpperCamelCase__		,				f'''{prompt_key}_{key}.npy'''       )

                                A_						=      tmp_dict

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

           super().save_pretrained(UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__ = None		,				**UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           A_						=      self.speaker_embeddings[voice_preset]

           A_						=      {}
           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}].'''       )

                  A_						=      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
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''       )

                  A_						=      np.load(UpperCamelCase__       )

           return voice_preset_dict



    def 					snake_case_			(					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[Any]:





           '''simple docstring'''
           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
                  ):
                         A_						=      self._load_voice_preset(UpperCamelCase__       )

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

                         A_						=      np.load(UpperCamelCase__       )

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

           A_						=      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:
                  A_						=      voice_preset

           return encoded_text



'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowerCamelCase		       =    imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase		       =    cvtColor(img, COLOR_BGR2GRAY)
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      cn.convert_to_negative(UpperCAmelCase__				)
       # assert negative_img array for at least one True
       assert negative_img.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
              # Work around assertion for response
              assert str(cc.change_contrast(UpperCAmelCase__,       1_10				)				).startswith(
                  """<PIL.Image.Image image mode=RGB size=100x100 at"""				)
def       UpperCAmelCase__    (      )			->			str:
       A_						=      canny.gen_gaussian_kernel(9,       sigma=1.4				)
       # Assert ambiguous array
       assert resp.all()
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      imread("""digital_image_processing/image_data/lena_small.jpg""",       0				)
       # assert ambiguous array for all == True
       assert canny_img.all()
       A_						=      canny.canny(UpperCAmelCase__				)
       # assert canny array for at least one True
       assert canny_array.any()
def       UpperCAmelCase__    (      )			->			Dict:
       assert gg.gaussian_filter(UpperCAmelCase__,       5,       sigma=0.9				).all()
def       UpperCAmelCase__    (      )			->			int:
       # laplace diagonals
       A_						=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
       A_						=      conv.img_convolve(UpperCAmelCase__,       UpperCAmelCase__				).astype(UpperCAmelCase__				)
       assert res.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       assert med.median_filter(UpperCAmelCase__,       3				).any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       A_					,			A_						=      sob.sobel_filter(UpperCAmelCase__				)
       assert grad.any() and theta.any()
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      sp.make_sepia(UpperCAmelCase__,       20				)
       assert sepia.all()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"				)			->			List[Any]:
       A_						=      bs.Burkes(imread(UpperCAmelCase__,       1				),       1_20				)
       burkes.process()
       assert burkes.output_img.any()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg",       )			->			Optional[int]:
       A_						=      rs.NearestNeighbour(imread(UpperCAmelCase__,       1				),       4_00,       2_00				)
       nn.process()
       assert nn.output.any()







def       UpperCAmelCase__    (      )			->			Optional[int]:
       A_						=      """digital_image_processing/image_data/lena.jpg"""

       # Reading the image and converting it to grayscale.
       A_						=      imread(UpperCAmelCase__,       0				)

       # Test for get_neighbors_pixel function() return not None
       A_						=      0
       A_						=      0
       A_						=      image[x_coordinate][y_coordinate]

       A_						=      lbp.get_neighbors_pixel(
           UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert neighbors_pixels is not None

       # Test for local_binary_pattern function()
       # Create a numpy array as the same height and width of read image
       A_						=      np.zeros((image.shape[0], image.shape[1])				)

       # Iterating through the image and calculating the local binary pattern value
       # for each pixel.
       for i in range(0,       image.shape[0]				):
              for j in range(0,       image.shape[1]				):
                     A_						=      lbp.local_binary_value(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert lbp_image.any()
