
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     :	Any    = logging.get_logger(__name__)







class 			__A( a			):
				snake_case_						   =    '''AutoTokenizer'''
				snake_case_						   =    ['''tokenizer''']

				snake_case_						   =    {
				    '''semantic_prompt''': 1,
				    '''coarse_prompt''': 2,
				    '''fine_prompt''': 2,
				}




				def __init__(     self		,     _snake_case		,     _snake_case=None				)    ->      str:


								'''simple docstring'''




								super().__init__(_snake_case				)

								__a										=    speaker_embeddings




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     _snake_case="speaker_embeddings_path.json"		,     **_snake_case				)    ->      List[str]:


								'''simple docstring'''




								if speaker_embeddings_dict_path is not None:
												__a										=    get_file_from_repo(
												    _snake_case		,     _snake_case		,     subfolder=kwargs.pop('''subfolder'''		,     _snake_case				)		,     cache_dir=kwargs.pop('''cache_dir'''		,     _snake_case				)		,     force_download=kwargs.pop('''force_download'''		,     _snake_case				)		,     proxies=kwargs.pop('''proxies'''		,     _snake_case				)		,     resume_download=kwargs.pop('''resume_download'''		,     _snake_case				)		,     local_files_only=kwargs.pop('''local_files_only'''		,     _snake_case				)		,     use_auth_token=kwargs.pop('''use_auth_token'''		,     _snake_case				)		,     revision=kwargs.pop('''revision'''		,     _snake_case				)		,     )
												if speaker_embeddings_path is None:
																logger.warning(
																    F"""`{os.path.join(_snake_case		,     _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`."""				)
																__a										=    None
												else:
																with open(_snake_case				) as speaker_embeddings_json:
																				__a										=    json.load(_snake_case				)
								else:
												__a										=    None

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

								return cls(tokenizer=_snake_case		,     speaker_embeddings=_snake_case				)




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


								'''simple docstring'''




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

												__a										=    {}

												__a										=    save_directory

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

																				__a										=    {}
																				for key in self.speaker_embeddings[prompt_key]:
																								np.save(
																								    os.path.join(
																								        embeddings_dict['''repo_or_path''']		,     _snake_case		,     F"""{prompt_key}_{key}"""				)		,     voice_preset[key]		,     allow_pickle=_snake_case		,     )
																								__a										=    os.path.join(_snake_case		,     F"""{prompt_key}_{key}.npy"""				)

																				__a										=    tmp_dict

												with open(os.path.join(_snake_case		,     _snake_case				)		,     '''w'''				) as fp:
																json.dump(_snake_case		,     _snake_case				)

								super().save_pretrained(_snake_case		,     _snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case = None		,     **_snake_case				)    ->      List[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'''		,     _snake_case				)		,     cache_dir=kwargs.pop('''cache_dir'''		,     _snake_case				)		,     force_download=kwargs.pop('''force_download'''		,     _snake_case				)		,     proxies=kwargs.pop('''proxies'''		,     _snake_case				)		,     resume_download=kwargs.pop('''resume_download'''		,     _snake_case				)		,     local_files_only=kwargs.pop('''local_files_only'''		,     _snake_case				)		,     use_auth_token=kwargs.pop('''use_auth_token'''		,     _snake_case				)		,     revision=kwargs.pop('''revision'''		,     _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."""				)

												__a										=    np.load(_snake_case				)

								return voice_preset_dict




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case = None				)    ->      List[Any]:


								'''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		,     _snake_case=None		,     _snake_case=None		,     _snake_case="pt"		,     _snake_case=256		,     _snake_case=False		,     _snake_case=True		,     _snake_case=False		,     **_snake_case		,     )    ->      Tuple:


								'''simple docstring'''




								if voice_preset is not None and not isinstance(_snake_case		,     _snake_case				):
												if (
												    isinstance(_snake_case		,     _snake_case				)
												    and self.speaker_embeddings is not None
												    and voice_preset in self.speaker_embeddings
												):
																__a										=    self._load_voice_preset(_snake_case				)

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

																__a										=    np.load(_snake_case				)

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

								__a										=    self.tokenizer(
								    _snake_case		,     return_tensors=_snake_case		,     padding='''max_length'''		,     max_length=_snake_case		,     return_attention_mask=_snake_case		,     return_token_type_ids=_snake_case		,     add_special_tokens=_snake_case		,     **_snake_case		,     )

								if voice_preset is not None:
												__a										=    voice_preset

								return encoded_text

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)