


"""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	:  str            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 ="""AutoTokenizer"""
							__UpperCAmelCase		:       int							 =["""tokenizer"""]

							__UpperCAmelCase		:       str							 ={
							    """semantic_prompt""": 1,
							    """coarse_prompt""": 2,
							    """fine_prompt""": 2,
							}

							def __init__(	self       ,  __a       ,  __a=None      ):
										super().__init__(__a      )

										__lowerCAmelCase							       =     speaker_embeddings

							@classmethod
							def      snake_case							(	cls       ,  __a       ,  __a="speaker_embeddings_path.json"       ,  **__a      ):

										if speaker_embeddings_dict_path is not None:
													__lowerCAmelCase							       =     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\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(__a      ) as speaker_embeddings_json:
																			__lowerCAmelCase							       =     json.load(__a      )
										else:
													__lowerCAmelCase							       =     None

										__lowerCAmelCase							       =     AutoTokenizer.from_pretrained(__a       ,  **__a      )

										return cls(tokenizer=__a       ,  speaker_embeddings=__a      )

							def      snake_case							(	self       ,  __a       ,  __a="speaker_embeddings_path.json"       ,  __a="speaker_embeddings"       ,  __a = False       ,  **__a       ,  ):
										if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(__a       ,  __a       ,  "v2"      )       ,  exist_ok=__a      )

													__lowerCAmelCase							       =     {}

													__lowerCAmelCase							       =     save_directory

													for prompt_key in self.speaker_embeddings:
																if prompt_key != "repo_or_path":
																			__lowerCAmelCase							       =     self._load_voice_preset(__a      )

																			__lowerCAmelCase							       =     {}
																			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							       =     os.path.join(__a       ,  f"{prompt_key}_{key}.npy"      )

																			__lowerCAmelCase							       =     tmp_dict

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

										super().save_pretrained(__a       ,  __a       ,  **__a      )

							def      snake_case							(	self       ,  __a = None       ,  **__a      ):
										__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"       ,  __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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."      )

													__lowerCAmelCase							       =     np.load(__a      )

										return voice_preset_dict

							def      snake_case							(	self       ,  __a = 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       ,  __a=None       ,  __a=None       ,  __a="pt"       ,  __a=2_56       ,  __a=False       ,  __a=True       ,  __a=False       ,  **__a       ,  ):
										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							       =     self._load_voice_preset(__a      )

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

																__lowerCAmelCase							       =     np.load(__a      )

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

										__lowerCAmelCase							       =     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							       =     voice_preset

										return encoded_text








"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


A	:  Optional[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  *__a       ,  **__a      ):
										warnings.warn(
										    "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
										    " use YolosImageProcessor instead."       ,  __a       ,  )
										super().__init__(*__a       ,  **__a      )




