

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							:  str   	=						logging.get_logger(__name__)



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Dict										=     '''AutoTokenizer'''
			_UpperCAmelCase   :							Optional[Any]										=     ['''tokenizer''']

			_UpperCAmelCase   :							Dict										=     {
			    '''semantic_prompt''': 1,
			    '''coarse_prompt''': 2,
			    '''fine_prompt''': 2,
			}



			def __init__( self  :     str   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Dict=None):
									super().__init__(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[str]  		=       speaker_embeddings



			@classmethod
			def        _SCREAMING_SNAKE_CASE			( cls  :     Tuple   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Tuple="speaker_embeddings_path.json"   ,						**lowerCAmelCase__  :     int):

									if speaker_embeddings_dict_path is not None:
															SCREAMING_SNAKE_CASE_: int  		=       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`.")
																					SCREAMING_SNAKE_CASE_: Any  		=       None
															else:
																					with open(lowerCAmelCase__) as speaker_embeddings_json:
																											SCREAMING_SNAKE_CASE_: int  		=       json.load(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_: Dict  		=       None

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       AutoTokenizer.from_pretrained(lowerCAmelCase__   ,						**lowerCAmelCase__)

									return cls(tokenizer=lowerCAmelCase__   ,						speaker_embeddings=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     int="speaker_embeddings_path.json"   ,						lowerCAmelCase__  :     Optional[int]="speaker_embeddings"   ,						lowerCAmelCase__  :     bool = False   ,						**lowerCAmelCase__  :     Any   ,						):
									if self.speaker_embeddings is not None:
															os.makedirs(os.path.join(lowerCAmelCase__   ,						lowerCAmelCase__   ,						"v2")   ,						exist_ok=lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Any  		=       {}

															SCREAMING_SNAKE_CASE_: List[str]  		=       save_directory

															for prompt_key in self.speaker_embeddings:
																					if prompt_key != "repo_or_path":
																											SCREAMING_SNAKE_CASE_: Tuple  		=       self._load_voice_preset(lowerCAmelCase__)

																											SCREAMING_SNAKE_CASE_: Optional[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__   ,						)
																																	SCREAMING_SNAKE_CASE_: Tuple  		=       os.path.join(lowerCAmelCase__   ,						F"{prompt_key}_{key}.npy")

																											SCREAMING_SNAKE_CASE_: int  		=       tmp_dict

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

									super().save_pretrained(lowerCAmelCase__   ,						lowerCAmelCase__   ,						**lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     str = None   ,						**lowerCAmelCase__  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.speaker_embeddings[voice_preset]

									SCREAMING_SNAKE_CASE_: 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}].")

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       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.")

															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.load(lowerCAmelCase__)

									return voice_preset_dict



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     Optional[dict] = 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  :     Tuple   ,						lowerCAmelCase__  :     Dict=None   ,						lowerCAmelCase__  :     Tuple=None   ,						lowerCAmelCase__  :     List[str]="pt"   ,						lowerCAmelCase__  :     Union[str, Any]=256   ,						lowerCAmelCase__  :     Tuple=False   ,						lowerCAmelCase__  :     str=True   ,						lowerCAmelCase__  :     List[str]=False   ,						**lowerCAmelCase__  :     str   ,						):
									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
															):
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._load_voice_preset(lowerCAmelCase__)

															else:
																					if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) and not voice_preset.endswith(".npz"):
																											SCREAMING_SNAKE_CASE_: List[str]  		=       voice_preset + ".npz"

																					SCREAMING_SNAKE_CASE_: Tuple  		=       np.load(lowerCAmelCase__)

									if voice_preset is not None:
															self._validate_voice_preset_dict(lowerCAmelCase__   ,						**lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Tuple  		=       BatchFeature(data=lowerCAmelCase__   ,						tensor_type=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[str]  		=       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:
															SCREAMING_SNAKE_CASE_: int  		=       voice_preset

									return encoded_text









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint



@require_torch
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									# For consistency across different places the DisjunctiveConstraint is called,
									# dc.token_ids is a list of integers. It is also initialized only by integers.

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [[1, 2, 4], [1, 2, 3, 4]]
									SCREAMING_SNAKE_CASE_: Any  		=       DisjunctiveConstraint(lowerCAmelCase__)
									self.assertTrue(isinstance(dc.token_ids   ,						lowerCAmelCase__))

									with self.assertRaises(lowerCAmelCase__):
															DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

									with self.assertRaises(lowerCAmelCase__):
															DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									# We can't have constraints that are complete subsets of another. This leads to a preverse
									# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
									# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
									# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
									# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [[1, 2], [1, 2, 3, 4]]

									with self.assertRaises(lowerCAmelCase__):
															DisjunctiveConstraint(lowerCAmelCase__)  # fails here



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       [[1, 2, 3], [1, 2, 4]]

									SCREAMING_SNAKE_CASE_: Tuple  		=       DisjunctiveConstraint(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       dc.update(1)
									SCREAMING_SNAKE_CASE_: Dict  		=       stepped is True and completed is False and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       dc.update(2)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       stepped is True and completed is False and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       dc.update(3)
									SCREAMING_SNAKE_CASE_: Tuple  		=       stepped is True and completed is True and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.current_seq == [1, 2, 3])




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

									SCREAMING_SNAKE_CASE_: List[Any]  		=       DisjunctiveConstraint(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       dc.update(1)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       dc.update(2)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       dc.update(4)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2, 4])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       dc.update(5)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.current_seq == [1, 2, 4, 5])

									dc.reset()

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       dc.update(1)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.remaining() == 3)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       dc.update(2)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.remaining() == 2)
									self.assertTrue(dc.current_seq == [1, 2])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       dc.update(5)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.remaining() == 0)
									self.assertTrue(dc.current_seq == [1, 2, 5])






