


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							(			UpperCamelCase__			):
							_lowercase	:			Dict        = '''AutoTokenizer'''
							_lowercase	:			Union[str, Any]        = ['''tokenizer''']

							_lowercase	:			Any        = {
							    '''semantic_prompt''': 1,
							    '''coarse_prompt''': 2,
							    '''fine_prompt''': 2,
							}

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	List[Any]=None							)			->   str:





									"""simple docstring"""







									super().__init__(UpperCamelCase_							)

									lowercase__            =      speaker_embeddings

							@classmethod
							def       lowerCamelCase_ (							cls:	int    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	Any="speaker_embeddings_path.json"    ,						**UpperCamelCase_:	Union[str, Any]							)			->   Tuple:





									"""simple docstring"""







									if speaker_embeddings_dict_path is not None:
											lowercase__            =      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\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`.'							)
													lowercase__            =      None
											else:
													with open(UpperCamelCase_							) as speaker_embeddings_json:
															lowercase__            =      json.load(UpperCamelCase_							)
									else:
											lowercase__            =      None

									lowercase__            =      AutoTokenizer.from_pretrained(UpperCamelCase_    ,						**UpperCamelCase_							)

									return cls(tokenizer=UpperCamelCase_    ,						speaker_embeddings=UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	Any="speaker_embeddings_path.json"    ,						UpperCamelCase_:	List[str]="speaker_embeddings"    ,						UpperCamelCase_:	bool = False    ,						**UpperCamelCase_:	Union[str, Any]    ,						)			->   Optional[Any]:





									"""simple docstring"""







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

											lowercase__            =      {}

											lowercase__            =      save_directory

											for prompt_key in self.speaker_embeddings:
													if prompt_key != "repo_or_path":
															lowercase__            =      self._load_voice_preset(UpperCamelCase_							)

															lowercase__            =      {}
															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_    ,						)
																	lowercase__            =      os.path.join(UpperCamelCase_    ,						f'{prompt_key}_{key}.npy'							)

															lowercase__            =      tmp_dict

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

									super().save_pretrained(UpperCamelCase_    ,						UpperCamelCase_    ,						**UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	str = None    ,						**UpperCamelCase_:	Optional[int]							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      self.speaker_embeddings[voice_preset]

									lowercase__            =      {}
									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}].'							)

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

											lowercase__            =      np.load(UpperCamelCase_							)

									return voice_preset_dict

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	Optional[dict] = None							)			->   List[str]:





									"""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:	str    ,						UpperCamelCase_:	Optional[Any]=None    ,						UpperCamelCase_:	Tuple=None    ,						UpperCamelCase_:	List[Any]="pt"    ,						UpperCamelCase_:	Any=256    ,						UpperCamelCase_:	Tuple=False    ,						UpperCamelCase_:	int=True    ,						UpperCamelCase_:	str=False    ,						**UpperCamelCase_:	Any    ,						)			->   List[str]:





									"""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
											):
													lowercase__            =      self._load_voice_preset(UpperCamelCase_							)

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

													lowercase__            =      np.load(UpperCamelCase_							)

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

									lowercase__            =      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:
											lowercase__            =      voice_preset

									return encoded_text






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase       =							{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							['DPTFeatureExtractor']
			lowerCAmelCase       =							['DPTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPTForDepthEstimation',
			    'DPTForSemanticSegmentation',
			    'DPTModel',
			    'DPTPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_dpt import DPTFeatureExtractor
						from .image_processing_dpt import DPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dpt import (
						    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPTForDepthEstimation,
						    DPTForSemanticSegmentation,
						    DPTModel,
						    DPTPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


