



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 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							"AutoTokenizer"
	a__				: Dict		       =							["tokenizer"]

	a__				: Optional[int]		       =							{
	    "semantic_prompt": 1,
	    "coarse_prompt": 2,
	    "fine_prompt": 2,
	}




	def __init__(     self     :						Optional[int]						,  __UpperCAmelCase     :						Any						,  __UpperCAmelCase     :						Tuple=None					)      ->			Optional[int]:
							super().__init__(__UpperCAmelCase					)

							UpperCAmelCase_=       speaker_embeddings




	@classmethod
	def   _SCREAMING_SNAKE_CASE		(     cls     :						Optional[int]						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						str="speaker_embeddings_path.json"						,  **__UpperCAmelCase     :						Optional[int]					)      ->			int:

							if speaker_embeddings_dict_path is not None:
													UpperCAmelCase_=       get_file_from_repo(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  subfolder=kwargs.pop("""subfolder"""						,  __UpperCAmelCase					)						,  cache_dir=kwargs.pop("""cache_dir"""						,  __UpperCAmelCase					)						,  force_download=kwargs.pop("""force_download"""						,  __UpperCAmelCase					)						,  proxies=kwargs.pop("""proxies"""						,  __UpperCAmelCase					)						,  resume_download=kwargs.pop("""resume_download"""						,  __UpperCAmelCase					)						,  local_files_only=kwargs.pop("""local_files_only"""						,  __UpperCAmelCase					)						,  use_auth_token=kwargs.pop("""use_auth_token"""						,  __UpperCAmelCase					)						,  revision=kwargs.pop("""revision"""						,  __UpperCAmelCase					)						,  )
													if speaker_embeddings_path is None:
																			logger.warning(
																			    F"""`{os.path.join(__UpperCAmelCase						,  __UpperCAmelCase					)}` 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_=       None
													else:
																			with open(__UpperCAmelCase					) as speaker_embeddings_json:
																									UpperCAmelCase_=       json.load(__UpperCAmelCase					)
							else:
													UpperCAmelCase_=       None

							UpperCAmelCase_=       AutoTokenizer.from_pretrained(__UpperCAmelCase						,  **__UpperCAmelCase					)

							return cls(tokenizer=__UpperCAmelCase						,  speaker_embeddings=__UpperCAmelCase					)




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

													UpperCAmelCase_=       {}

													UpperCAmelCase_=       save_directory

													for prompt_key in self.speaker_embeddings:
																			if prompt_key != "repo_or_path":
																									UpperCAmelCase_=       self._load_voice_preset(__UpperCAmelCase					)

																									UpperCAmelCase_=       {}
																									for key in self.speaker_embeddings[prompt_key]:
																															np.save(
																															    os.path.join(
																															        embeddings_dict["""repo_or_path"""]						,  __UpperCAmelCase						,  F"""{prompt_key}_{key}"""					)						,  voice_preset[key]						,  allow_pickle=__UpperCAmelCase						,  )
																															UpperCAmelCase_=       os.path.join(__UpperCAmelCase						,  F"""{prompt_key}_{key}.npy"""					)

																									UpperCAmelCase_=       tmp_dict

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

							super().save_pretrained(__UpperCAmelCase						,  __UpperCAmelCase						,  **__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						str = None						,  **__UpperCAmelCase     :						List[str]					)      ->			List[Any]:
							UpperCAmelCase_=       self.speaker_embeddings[voice_preset]

							UpperCAmelCase_=       {}
							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_=       get_file_from_repo(
													    self.speaker_embeddings.get("""repo_or_path"""						,  """/"""					)						,  voice_preset_paths[key]						,  subfolder=kwargs.pop("""subfolder"""						,  __UpperCAmelCase					)						,  cache_dir=kwargs.pop("""cache_dir"""						,  __UpperCAmelCase					)						,  force_download=kwargs.pop("""force_download"""						,  __UpperCAmelCase					)						,  proxies=kwargs.pop("""proxies"""						,  __UpperCAmelCase					)						,  resume_download=kwargs.pop("""resume_download"""						,  __UpperCAmelCase					)						,  local_files_only=kwargs.pop("""local_files_only"""						,  __UpperCAmelCase					)						,  use_auth_token=kwargs.pop("""use_auth_token"""						,  __UpperCAmelCase					)						,  revision=kwargs.pop("""revision"""						,  __UpperCAmelCase					)						,  )
													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_=       np.load(__UpperCAmelCase					)

							return voice_preset_dict




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

													else:
																			if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) and not voice_preset.endswith(""".npz"""					):
																									UpperCAmelCase_=       voice_preset + """.npz"""

																			UpperCAmelCase_=       np.load(__UpperCAmelCase					)

							if voice_preset is not None:
													self._validate_voice_preset_dict(__UpperCAmelCase						,  **__UpperCAmelCase					)
													UpperCAmelCase_=       BatchFeature(data=__UpperCAmelCase						,  tensor_type=__UpperCAmelCase					)

							UpperCAmelCase_=       self.tokenizer(
							    __UpperCAmelCase						,  return_tensors=__UpperCAmelCase						,  padding="""max_length"""						,  max_length=__UpperCAmelCase						,  return_attention_mask=__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase						,  **__UpperCAmelCase						,  )

							if voice_preset is not None:
													UpperCAmelCase_=       voice_preset

							return encoded_text









import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm
	import torch_xla.debug.metrics as met






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Any						,  *__UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]=None						,  __UpperCAmelCase     :						Optional[int]=None						,  **__UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							super().__init__(*__UpperCAmelCase						,  **__UpperCAmelCase					)
							UpperCAmelCase_=       eval_examples
							UpperCAmelCase_=       post_process_function




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Optional[Dataset] = None						,  __UpperCAmelCase     :						Optional[Any]=None						,  __UpperCAmelCase     :						Optional[List[str]] = None						,  __UpperCAmelCase     :						str = "eval"						,  **__UpperCAmelCase     :						Any						,  )      ->			Dict[str, float]:
							UpperCAmelCase_=       gen_kwargs.copy()
							UpperCAmelCase_=       (
							    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""					) is not None else self.args.generation_max_length
							)
							UpperCAmelCase_=       (
							    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
							)
							UpperCAmelCase_=       gen_kwargs

							UpperCAmelCase_=       self.eval_dataset if eval_dataset is None else eval_dataset
							UpperCAmelCase_=       self.get_eval_dataloader(__UpperCAmelCase					)
							UpperCAmelCase_=       self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_=       self.compute_metrics
							UpperCAmelCase_=       None
							UpperCAmelCase_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Evaluation"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							finally:
													UpperCAmelCase_=       compute_metrics
							UpperCAmelCase_=       self.args.eval_batch_size * self.args.world_size
							if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
													start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
							output.metrics.update(
							    speed_metrics(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size					)						,  )					)

							if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
													# Only the main node write the results by default
													UpperCAmelCase_=       self.post_process_function(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.compute_metrics(__UpperCAmelCase					)

													# Prefix all keys with metric_key_prefix + '_'
													for key in list(metrics.keys()					):
																			if not key.startswith(F"""{metric_key_prefix}_"""					):
																									UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)

													metrics.update(output.metrics					)
							else:
													UpperCAmelCase_=       output.metrics

							if self.args.should_log:
													# Only the main node log the results by default
													self.log(__UpperCAmelCase					)

							if self.args.tpu_metrics_debug or self.args.debug:
													# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
													xm.master_print(met.metrics_report()					)

							UpperCAmelCase_=       self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  __UpperCAmelCase					)
							return metrics




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Optional[int]=None						,  __UpperCAmelCase     :						str = "test"						,  **__UpperCAmelCase     :						List[str]					)      ->			Tuple:
							UpperCAmelCase_=       gen_kwargs.copy()

							UpperCAmelCase_=       self.get_test_dataloader(__UpperCAmelCase					)

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_=       self.compute_metrics
							UpperCAmelCase_=       None
							UpperCAmelCase_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Prediction"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							finally:
													UpperCAmelCase_=       compute_metrics

							UpperCAmelCase_=       self.args.eval_batch_size * self.args.world_size
							if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
													start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
							output.metrics.update(
							    speed_metrics(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size					)						,  )					)
							if self.post_process_function is None or self.compute_metrics is None:
													return output

							UpperCAmelCase_=       self.post_process_function(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  """predict"""					)
							UpperCAmelCase_=       self.compute_metrics(__UpperCAmelCase					)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()					):
													if not key.startswith(F"""{metric_key_prefix}_"""					):
																			UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)
							metrics.update(output.metrics					)
							return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=__UpperCAmelCase					)




