



'''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


snake_case_ :	List[Any]         =  logging.get_logger(__name__)






class 							lowercase__	(							lowercase   ):
	lowercase__  				=			"""AutoTokenizer"""
	lowercase__  				=			["""tokenizer"""]

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





	def __init__( self					:				List[str] ,lowerCamelCase__					:				Tuple ,lowerCamelCase__					:				Tuple=None ):







							'''simple docstring'''
							super().__init__(lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   speaker_embeddings





	@classmethod
	def 	UpperCamelCase_  ( cls					:				Union[str, Any] ,lowerCamelCase__					:				int ,lowerCamelCase__					:				str="speaker_embeddings_path.json" ,**lowerCamelCase__					:				Optional[Any] ):







							'''simple docstring'''

							if speaker_embeddings_dict_path is not None:
													_UpperCamelCase					:		Optional[Any] 				=   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\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`.' )
																			_UpperCamelCase					:		Union[str, Any] 				=   None
													else:
																			with open(lowerCamelCase__ ) as speaker_embeddings_json:
																									_UpperCamelCase					:		Optional[int] 				=   json.load(lowerCamelCase__ )
							else:
													_UpperCamelCase					:		Tuple 				=   None

							_UpperCamelCase					:		Tuple 				=   AutoTokenizer.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )

							return cls(tokenizer=lowerCamelCase__ ,speaker_embeddings=lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Tuple ,lowerCamelCase__					:				Union[str, Any] ,lowerCamelCase__					:				int="speaker_embeddings_path.json" ,lowerCamelCase__					:				Dict="speaker_embeddings" ,lowerCamelCase__					:				bool = False ,**lowerCamelCase__					:				Tuple ,):







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

													_UpperCamelCase					:		Tuple 				=   {}

													_UpperCamelCase					:		Optional[Any] 				=   save_directory

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

																									_UpperCamelCase					:		Union[str, Any] 				=   {}
																									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__ ,)
																															_UpperCamelCase					:		List[str] 				=   os.path.join(lowerCamelCase__ ,F'{prompt_key}_{key}.npy' )

																									_UpperCamelCase					:		str 				=   tmp_dict

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

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





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ,lowerCamelCase__					:				str = None ,**lowerCamelCase__					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   self.speaker_embeddings[voice_preset]

							_UpperCamelCase					:		Union[str, 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}].' )

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

													_UpperCamelCase					:		List[str] 				=   np.load(lowerCamelCase__ )

							return voice_preset_dict





	def 	UpperCamelCase_  ( self					:				Any ,lowerCamelCase__					:				Optional[dict] = None ):







							'''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					:				Any ,lowerCamelCase__					:				Optional[Any]=None ,lowerCamelCase__					:				Union[str, Any]=None ,lowerCamelCase__					:				Any="pt" ,lowerCamelCase__					:				Dict=256 ,lowerCamelCase__					:				int=False ,lowerCamelCase__					:				int=True ,lowerCamelCase__					:				List[str]=False ,**lowerCamelCase__					:				Union[str, Any] ,):







							'''simple docstring'''
							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
													):
																			_UpperCamelCase					:		Optional[int] 				=   self._load_voice_preset(lowerCamelCase__ )

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

																			_UpperCamelCase					:		str 				=   np.load(lowerCamelCase__ )

							if voice_preset is not None:
													self._validate_voice_preset_dict(lowerCamelCase__ ,**lowerCamelCase__ )
													_UpperCamelCase					:		Union[str, Any] 				=   BatchFeature(data=lowerCamelCase__ ,tensor_type=lowerCamelCase__ )

							_UpperCamelCase					:		Union[str, Any] 				=   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:
													_UpperCamelCase					:		Optional[Any] 				=   voice_preset

							return encoded_text









"""simple docstring"""
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							RoFormerTokenizer
					UpperCamelCase      =							RoFormerTokenizerFast
					UpperCamelCase      =							True
					UpperCamelCase      =							True


					def 	lowerCamelCase       (					self			:List[str]		):
												super().setUp()


					def 	lowerCamelCase       (					self			:int     ,       **__UpperCamelCase			:List[Any]		):
												return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Tuple     ,       **__UpperCamelCase			:Optional[int]		):
												return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												A		=  "永和服装饰品有限公司,今天天气非常好"
												A		=  "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
												return input_text, output_text


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.get_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)

												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)

												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												A		=  self.get_rust_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)
												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												pass


					def 	lowerCamelCase       (					self			:Tuple		):
												pass


					def 	lowerCamelCase       (					self			:List[str]		):
												pass


