



"""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


__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      logging.get_logger(__name__)


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       List[Any]										= 'AutoTokenizer'
					lowercase__   :       Dict										= ['tokenizer']

					lowercase__   :       str										= {
					    'semantic_prompt': 1,
					    'coarse_prompt': 2,
					    'fine_prompt': 2,
					}

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

									_lowerCamelCase        =				speaker_embeddings

					@classmethod
					def 			snake_case__      (    cls				,       lowerCamelCase__				,       lowerCamelCase__="speaker_embeddings_path.json"				,       **lowerCamelCase__   ):

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

									_lowerCamelCase        =				AutoTokenizer.from_pretrained(lowerCamelCase__				,       **lowerCamelCase__   )

									return cls(tokenizer=lowerCamelCase__				,       speaker_embeddings=lowerCamelCase__   )

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

													_lowerCamelCase        =				{}

													_lowerCamelCase        =				save_directory

													for prompt_key in self.speaker_embeddings:
																	if prompt_key != "repo_or_path":
																					_lowerCamelCase        =				self._load_voice_preset(lowerCamelCase__   )

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

																					_lowerCamelCase        =				tmp_dict

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

									super().save_pretrained(lowerCamelCase__				,       lowerCamelCase__				,       **lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__ = None				,       **lowerCamelCase__   ):
									_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'''				,       lowerCamelCase__   )				,       cache_dir=kwargs.pop('''cache_dir'''				,       lowerCamelCase__   )				,       force_download=kwargs.pop('''force_download'''				,       lowerCamelCase__   )				,       proxies=kwargs.pop('''proxies'''				,       lowerCamelCase__   )				,       resume_download=kwargs.pop('''resume_download'''				,       lowerCamelCase__   )				,       local_files_only=kwargs.pop('''local_files_only'''				,       lowerCamelCase__   )				,       use_auth_token=kwargs.pop('''use_auth_token'''				,       lowerCamelCase__   )				,       revision=kwargs.pop('''revision'''				,       lowerCamelCase__   )				,       )
													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        =				np.load(lowerCamelCase__   )

									return voice_preset_dict

					def 			snake_case__      (    self				,       lowerCamelCase__ = 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				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__="pt"				,       lowerCamelCase__=2_5_6				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       **lowerCamelCase__				,       ):
									if voice_preset is not None and not isinstance(lowerCamelCase__				,       lowerCamelCase__   ):
													if (
													    isinstance(lowerCamelCase__				,       lowerCamelCase__   )
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																	_lowerCamelCase        =				self._load_voice_preset(lowerCamelCase__   )

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

																	_lowerCamelCase        =				np.load(lowerCamelCase__   )

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

									_lowerCamelCase        =				self.tokenizer(
									    lowerCamelCase__				,       return_tensors=lowerCamelCase__				,       padding='''max_length'''				,       max_length=lowerCamelCase__				,       return_attention_mask=lowerCamelCase__				,       return_token_type_ids=lowerCamelCase__				,       add_special_tokens=lowerCamelCase__				,       **lowerCamelCase__				,       )

									if voice_preset is not None:
													_lowerCamelCase        =				voice_preset

									return encoded_text





"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
			from .pipeline_kandinsky import KandinskyPipeline
			from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
			from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
			from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
			from .text_encoder import MultilingualCLIP
