



"""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		=						logging.get_logger(__name__)

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """AutoTokenizer"""
			__magic_name__     					=     ["""tokenizer"""]

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



			def __init__(			self							:				Dict   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				List[Any]=None		)							->					Union[str, Any]:
									super().__init__(_lowercase		)

									snake_case   :			List[Any]						    = speaker_embeddings



			@classmethod
			def 				__lowercase					(			cls							:				Dict   ,	_lowercase							:				Dict   ,	_lowercase							:				List[Any]="speaker_embeddings_path.json"   ,	**_lowercase							:				Dict		)							->					List[Any]:

									if speaker_embeddings_dict_path is not None:
															snake_case   :			str						    = get_file_from_repo(
															    _lowercase   ,	_lowercase   ,	subfolder=kwargs.pop("subfolder"   ,	_lowercase		)   ,	cache_dir=kwargs.pop("cache_dir"   ,	_lowercase		)   ,	force_download=kwargs.pop("force_download"   ,	_lowercase		)   ,	proxies=kwargs.pop("proxies"   ,	_lowercase		)   ,	resume_download=kwargs.pop("resume_download"   ,	_lowercase		)   ,	local_files_only=kwargs.pop("local_files_only"   ,	_lowercase		)   ,	use_auth_token=kwargs.pop("use_auth_token"   ,	_lowercase		)   ,	revision=kwargs.pop("revision"   ,	_lowercase		)   ,	)
															if speaker_embeddings_path is None:
																					logger.warning(
																					    F'''`{os.path.join(_lowercase   ,	_lowercase		)}` 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`.'''		)
																					snake_case   :			int						    = None
															else:
																					with open(_lowercase		) as speaker_embeddings_json:
																											snake_case   :			List[Any]						    = json.load(_lowercase		)
									else:
															snake_case   :			Union[str, Any]						    = None

									snake_case   :			Any						    = AutoTokenizer.from_pretrained(_lowercase   ,	**_lowercase		)

									return cls(tokenizer=_lowercase   ,	speaker_embeddings=_lowercase		)



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				int   ,	_lowercase							:				Tuple="speaker_embeddings_path.json"   ,	_lowercase							:				Union[str, Any]="speaker_embeddings"   ,	_lowercase							:				bool = False   ,	**_lowercase							:				List[str]   ,	)							->					Optional[Any]:
									if self.speaker_embeddings is not None:
															os.makedirs(os.path.join(_lowercase   ,	_lowercase   ,	"v2"		)   ,	exist_ok=_lowercase		)

															snake_case   :			Any						    = {}

															snake_case   :			int						    = save_directory

															for prompt_key in self.speaker_embeddings:
																					if prompt_key != "repo_or_path":
																											snake_case   :			Dict						    = self._load_voice_preset(_lowercase		)

																											snake_case   :			Union[str, Any]						    = {}
																											for key in self.speaker_embeddings[prompt_key]:
																																	np.save(
																																	    os.path.join(
																																	        embeddings_dict["repo_or_path"]   ,	_lowercase   ,	F'''{prompt_key}_{key}'''		)   ,	voice_preset[key]   ,	allow_pickle=_lowercase   ,	)
																																	snake_case   :			List[Any]						    = os.path.join(_lowercase   ,	F'''{prompt_key}_{key}.npy'''		)

																											snake_case   :			List[Any]						    = tmp_dict

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

									super().save_pretrained(_lowercase   ,	_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				Union[str, Any]   ,	_lowercase							:				str = None   ,	**_lowercase							:				Optional[Any]		)							->					Union[str, Any]:
									snake_case   :			List[Any]						    = self.speaker_embeddings[voice_preset]

									snake_case   :			Any						    = {}
									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}].'''		)

															snake_case   :			Dict						    = get_file_from_repo(
															    self.speaker_embeddings.get("repo_or_path"   ,	"/"		)   ,	voice_preset_paths[key]   ,	subfolder=kwargs.pop("subfolder"   ,	_lowercase		)   ,	cache_dir=kwargs.pop("cache_dir"   ,	_lowercase		)   ,	force_download=kwargs.pop("force_download"   ,	_lowercase		)   ,	proxies=kwargs.pop("proxies"   ,	_lowercase		)   ,	resume_download=kwargs.pop("resume_download"   ,	_lowercase		)   ,	local_files_only=kwargs.pop("local_files_only"   ,	_lowercase		)   ,	use_auth_token=kwargs.pop("use_auth_token"   ,	_lowercase		)   ,	revision=kwargs.pop("revision"   ,	_lowercase		)   ,	)
															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.'''		)

															snake_case   :			int						    = np.load(_lowercase		)

									return voice_preset_dict



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				Optional[dict] = None		)							->					Optional[Any]:
									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							:				Any   ,	_lowercase							:				Dict=None   ,	_lowercase							:				Optional[Any]=None   ,	_lowercase							:				List[Any]="pt"   ,	_lowercase							:				Tuple=256   ,	_lowercase							:				Optional[int]=False   ,	_lowercase							:				List[Any]=True   ,	_lowercase							:				Tuple=False   ,	**_lowercase							:				Any   ,	)							->					Optional[int]:
									if voice_preset is not None and not isinstance(_lowercase   ,	_lowercase		):
															if (
															    isinstance(_lowercase   ,	_lowercase		)
															    and self.speaker_embeddings is not None
															    and voice_preset in self.speaker_embeddings
															):
																					snake_case   :			Optional[int]						    = self._load_voice_preset(_lowercase		)

															else:
																					if isinstance(_lowercase   ,	_lowercase		) and not voice_preset.endswith(".npz"		):
																											snake_case   :			Any						    = voice_preset + ".npz"

																					snake_case   :			Any						    = np.load(_lowercase		)

									if voice_preset is not None:
															self._validate_voice_preset_dict(_lowercase   ,	**_lowercase		)
															snake_case   :			Optional[Any]						    = BatchFeature(data=_lowercase   ,	tensor_type=_lowercase		)

									snake_case   :			Optional[int]						    = self.tokenizer(
									    _lowercase   ,	return_tensors=_lowercase   ,	padding="max_length"   ,	max_length=_lowercase   ,	return_attention_mask=_lowercase   ,	return_token_type_ids=_lowercase   ,	add_special_tokens=_lowercase   ,	**_lowercase   ,	)

									if voice_preset is not None:
															snake_case   :			List[Any]						    = voice_preset

									return encoded_text








"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
A		=						'\nHuman: <<task>>\n\nAssistant: '


A		=						'huggingface-tools/default-prompts'
A		=						{'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Tuple="run" ):
						"""simple docstring"""






						if prompt_or_repo_id is None:
												snake_case   :			Any						    = DEFAULT_PROMPTS_REPO

						# prompt is considered a repo ID when it does not contain any kind of space
						if re.search("\\s"     ,		lowerCamelCase_ ) is not None:
												return prompt_or_repo_id

						snake_case   :			Optional[int]						    = cached_file(
						    lowerCamelCase_     ,		PROMPT_FILES[mode]     ,		repo_type="dataset"     ,		user_agent={"agent": agent_name} )
						with open(lowerCamelCase_     ,		"r"     ,		encoding="utf-8" ) as f:
												return f.read()



