



"""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


lowerCAmelCase_   						=   logging.get_logger(__name__)
class        __A       (	a_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Optional[int]   	=				"AutoTokenizer"
		lowerCAmelCase			:					Any   	=				["tokenizer"]

		lowerCAmelCase			:					int   	=				{
		    "semantic_prompt": 1,
		    "coarse_prompt": 2,
		    "fine_prompt": 2,
		}







		def __init__(	self						:  Any			,_snake_case						:  int			,_snake_case						:  Dict=None						)					->		int:






				"""simple docstring"""



				super().__init__(_A						)

				lowercase__   :    Any				=			speaker_embeddings







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






				"""simple docstring"""



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

				lowercase__   :    List[Any]				=			AutoTokenizer.from_pretrained(_A			,**_A						)

				return cls(tokenizer=_A			,speaker_embeddings=_A						)







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






				"""simple docstring"""



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

						lowercase__   :    List[Any]				=			{}

						lowercase__   :    int				=			save_directory

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

										lowercase__   :    Optional[int]				=			{}
										for key in self.speaker_embeddings[prompt_key]:
												np.save(
												    os.path.join(
												        embeddings_dict['''repo_or_path''']			,_A			,f"""{prompt_key}_{key}"""						)			,voice_preset[key]			,allow_pickle=_A			,)
												lowercase__   :    Union[str, Any]				=			os.path.join(_A			,f"""{prompt_key}_{key}.npy"""						)

										lowercase__   :    Optional[Any]				=			tmp_dict

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

				super().save_pretrained(_A			,_A			,**_A						)







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  int = None			,**_snake_case						:  Any						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.speaker_embeddings[voice_preset]

				lowercase__   :    Optional[int]				=			{}
				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__   :    List[Any]				=			get_file_from_repo(
						    self.speaker_embeddings.get('''repo_or_path'''			,'''/'''						)			,voice_preset_paths[key]			,subfolder=kwargs.pop('''subfolder'''			,_A						)			,cache_dir=kwargs.pop('''cache_dir'''			,_A						)			,force_download=kwargs.pop('''force_download'''			,_A						)			,proxies=kwargs.pop('''proxies'''			,_A						)			,resume_download=kwargs.pop('''resume_download'''			,_A						)			,local_files_only=kwargs.pop('''local_files_only'''			,_A						)			,use_auth_token=kwargs.pop('''use_auth_token'''			,_A						)			,revision=kwargs.pop('''revision'''			,_A						)			,)
						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."""						)

						lowercase__   :    Union[str, Any]				=			np.load(_A						)

				return voice_preset_dict







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Optional[Any] = 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						:  List[Any]			,_snake_case						:  List[str]=None			,_snake_case						:  List[str]=None			,_snake_case						:  Dict="pt"			,_snake_case						:  Union[str, Any]=256			,_snake_case						:  Tuple=False			,_snake_case						:  Any=True			,_snake_case						:  str=False			,**_snake_case						:  Optional[int]			,)					->		str:






				"""simple docstring"""



				if voice_preset is not None and not isinstance(_A			,_A						):
						if (
						    isinstance(_A			,_A						)
						    and self.speaker_embeddings is not None
						    and voice_preset in self.speaker_embeddings
						):
								lowercase__   :    List[str]				=			self._load_voice_preset(_A						)

						else:
								if isinstance(_A			,_A						) and not voice_preset.endswith('''.npz'''						):
										lowercase__   :    List[str]				=			voice_preset + '''.npz'''

								lowercase__   :    List[Any]				=			np.load(_A						)

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

				lowercase__   :    Dict				=			self.tokenizer(
				    _A			,return_tensors=_A			,padding='''max_length'''			,max_length=_A			,return_attention_mask=_A			,return_token_type_ids=_A			,add_special_tokens=_A			,**_A			,)

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

				return encoded_text







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
				import torch

				from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 	A_       (unittest.TestCase			):
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-base'''		)
				UpperCAmelCase   		=				torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
				# The dog is cute and lives in the garden house

				UpperCAmelCase   		=				torch.Size((1, 1_2, 7_6_8)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]		)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
							UpperCAmelCase   		=				model(_A		)['''last_hidden_state'''].detach()
				self.assertEqual(output.shape							,					_A		)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1]							,					_A							,					atol=1E-3		)		)







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-large'''		)
				UpperCAmelCase   		=				torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
				# The dog is cute and lives in the garden house

				UpperCAmelCase   		=				torch.Size((1, 1_2, 1_0_2_4)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]		)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
							UpperCAmelCase   		=				model(_A		)['''last_hidden_state'''].detach()
				self.assertEqual(output.shape							,					_A		)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1]							,					_A							,					atol=1E-3		)		)



