

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_   :   str        =       logging.get_logger(__name__)







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						'''AutoTokenizer'''
			SCREAMING_SNAKE_CASE__     				=						['''tokenizer''']

			SCREAMING_SNAKE_CASE__     				=						{
			    '''semantic_prompt''': 1,
			    '''coarse_prompt''': 2,
			    '''fine_prompt''': 2,
			}







			def __init__(			self			: Union[str, Any]			,  lowercase			: List[Any]			,  lowercase			: Union[str, Any]=None  ):





								'''simple docstring'''



								super().__init__(lowercase  )

								UpperCAmelCase			:   Optional[int]      =				speaker_embeddings







			@classmethod
			def 			__lowerCAmelCase				(			cls			: Optional[int]			,  lowercase			: List[Any]			,  lowercase			: Dict="speaker_embeddings_path.json"			,  **lowercase			: Optional[int]  ):





								'''simple docstring'''



								if speaker_embeddings_dict_path is not None:
													UpperCAmelCase			:   Union[str, Any]      =				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`."""  )
																		UpperCAmelCase			:   Union[str, Any]      =				None
													else:
																		with open(lowercase  ) as speaker_embeddings_json:
																							UpperCAmelCase			:   Tuple      =				json.load(lowercase  )
								else:
													UpperCAmelCase			:   List[str]      =				None

								UpperCAmelCase			:   List[Any]      =				AutoTokenizer.from_pretrained(lowercase			,  **lowercase  )

								return cls(tokenizer=lowercase			,  speaker_embeddings=lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: int			,  lowercase			: Dict="speaker_embeddings_path.json"			,  lowercase			: Dict="speaker_embeddings"			,  lowercase			: bool = False			,  **lowercase			: Dict			,  ):





								'''simple docstring'''



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

													UpperCAmelCase			:   List[str]      =				{}

													UpperCAmelCase			:   int      =				save_directory

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

																							UpperCAmelCase			:   Dict      =				{}
																							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			,  )
																												UpperCAmelCase			:   Optional[int]      =				os.path.join(lowercase			,  f"""{prompt_key}_{key}.npy"""  )

																							UpperCAmelCase			:   Union[str, Any]      =				tmp_dict

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

								super().save_pretrained(lowercase			,  lowercase			,  **lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: str = None			,  **lowercase			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				self.speaker_embeddings[voice_preset]

								UpperCAmelCase			:   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}]."""  )

													UpperCAmelCase			:   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."""  )

													UpperCAmelCase			:   Any      =				np.load(lowercase  )

								return voice_preset_dict







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: 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			: Optional[int]			,  lowercase			: Optional[int]=None			,  lowercase			: Tuple=None			,  lowercase			: int="pt"			,  lowercase			: int=2_56			,  lowercase			: List[Any]=False			,  lowercase			: List[str]=True			,  lowercase			: Tuple=False			,  **lowercase			: Tuple			,  ):





								'''simple docstring'''



								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
													):
																		UpperCAmelCase			:   Dict      =				self._load_voice_preset(lowercase  )

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

																		UpperCAmelCase			:   Dict      =				np.load(lowercase  )

								if voice_preset is not None:
													self._validate_voice_preset_dict(lowercase			,  **lowercase  )
													UpperCAmelCase			:   List[str]      =				BatchFeature(data=lowercase			,  tensor_type=lowercase  )

								UpperCAmelCase			:   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:
													UpperCAmelCase			:   int      =				voice_preset

								return encoded_text





"""simple docstring"""






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case_   :   Union[str, Any]        =       abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					config.addinivalue_line(
					    "markers"							,      "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"	)
					config.addinivalue_line(
					    "markers"							,      "is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested"	)
					config.addinivalue_line("markers"							,      "is_pipeline_test: mark test to run only when pipelines are tested"	)
					config.addinivalue_line("markers"							,      "is_staging_test: mark test to run only in the staging environment"	)
					config.addinivalue_line("markers"							,      "accelerate_tests: mark test that require accelerate"	)
					config.addinivalue_line("markers"							,      "tool_tests: mark the tool tests that are run on their specific schedule"	)



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(_lowercase	)



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					from transformers.testing_utils import pytest_terminal_summary_main

					UpperCAmelCase			:   Optional[int]      =				terminalreporter.config.getoption("--make-reports"	)
					if make_reports:
										pytest_terminal_summary_main(_lowercase							,      id=_lowercase	)



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		Dict	):




					'''simple docstring'''






					if exitstatus == 5:
										UpperCAmelCase			:   List[str]      =				0


# Doctest custom flag to ignore output.
snake_case_   :   Union[str, Any]        =       doctest.register_optionflag("""IGNORE_RESULT""")

snake_case_   :   Optional[int]        =       doctest.OutputChecker







class       snake_case__  ( lowerCAmelCase_     ):







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Tuple			,  lowercase			: Optional[Any]			,  lowercase			: int  ):





								'''simple docstring'''



								if IGNORE_RESULT & optionflags:
													return True
								return OutputChecker.check_output(self			,  lowercase			,  lowercase			,  lowercase  )

snake_case_   :   List[str]        =       CustomOutputChecker
snake_case_   :   Optional[Any]        =       HfDoctestModule




snake_case_   :   List[str]        =       HfDocTestParser


