






'''simple docstring'''


from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					"EncodecFeatureExtractor"
			__a							:							Union[str, Any]					     =					("T5Tokenizer", "T5TokenizerFast")






			def __init__(   self							:						Any ,      lowercase							:						Any ,      lowercase							:						str			)							->    Dict:

									'''simple docstring'''







									super().__init__(lowercase ,      lowercase			)
									UpperCamelCase__         = self.feature_extractor
									UpperCamelCase__         = False






			def 				A      (   self							:						str ,      lowercase							:						Tuple=None ,      lowercase							:						Any=None ,      lowercase							:						int=True			)							->    Dict:

									'''simple docstring'''







									return self.tokenizer.get_decoder_prompt_ids(task=lowercase ,      language=lowercase ,      no_timestamps=lowercase			)






			def __call__(   self							:						Tuple ,      *lowercase							:						Optional[Any] ,      **lowercase							:						Union[str, Any]			)							->    List[Any]:

									'''simple docstring'''







									if self._in_target_context_manager:
															return self.current_processor(*lowercase ,      **lowercase			)

									UpperCamelCase__         = kwargs.pop("""audio""" ,      lowercase			)
									UpperCamelCase__         = kwargs.pop("""sampling_rate""" ,      lowercase			)
									UpperCamelCase__         = kwargs.pop("""text""" ,      lowercase			)
									if len(lowercase			) > 0:
															UpperCamelCase__         = args[0]
															UpperCamelCase__         = args[1:]

									if audio is None and text is None:
															raise ValueError("""You need to specify either an `audio` or `text` input to process."""			)

									if text is not None:
															UpperCamelCase__         = self.tokenizer(lowercase ,      **lowercase			)

									if audio is not None:
															UpperCamelCase__         = self.feature_extractor(lowercase ,      *lowercase ,      sampling_rate=lowercase ,      **lowercase			)

									if audio is None:
															return inputs

									elif text is None:
															return audio_inputs

									else:
															UpperCamelCase__         = audio_inputs["""input_values"""]
															if "padding_mask" in audio_inputs:
																					UpperCamelCase__         = audio_inputs["""padding_mask"""]
															return inputs






			def 				A      (   self							:						List[str] ,      *lowercase							:						Optional[int] ,      **lowercase							:						int			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = kwargs.pop("""audio""" ,      lowercase			)
									UpperCamelCase__         = kwargs.pop("""padding_mask""" ,      lowercase			)

									if len(lowercase			) > 0:
															UpperCamelCase__         = args[0]
															UpperCamelCase__         = args[1:]

									if audio_values is not None:
															return self._decode_audio(lowercase ,      padding_mask=lowercase			)
									else:
															return self.tokenizer.batch_decode(*lowercase ,      **lowercase			)






			def 				A      (   self							:						Optional[int] ,      *lowercase							:						Tuple ,      **lowercase							:						int			)							->    Tuple:

									'''simple docstring'''







									return self.tokenizer.decode(*lowercase ,      **lowercase			)







			def 				A      (   self							:						int ,      lowercase							:						int ,      lowercase							:						Optional = None			)							->    List[np.ndarray]:

									'''simple docstring'''







									UpperCamelCase__         = to_numpy(lowercase			)
									UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = audio_values.shape

									if padding_mask is None:
															return list(lowercase			)

									UpperCamelCase__         = to_numpy(lowercase			)

									# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
									# token (so that the generated audio values are **not** treated as padded tokens)
									UpperCamelCase__         = seq_len - padding_mask.shape[-1]
									UpperCamelCase__         = 1 - self.feature_extractor.padding_value
									UpperCamelCase__         = np.pad(lowercase ,      ((0, 0), (0, difference)) ,      """constant""" ,      constant_values=lowercase			)

									UpperCamelCase__         = audio_values.tolist()
									for i in range(lowercase			):
															UpperCamelCase__         = np.asarray(audio_values[i]			)[
															    padding_mask[i][None, :] != self.feature_extractor.padding_value
															]
															UpperCamelCase__         = sliced_audio.reshape(lowercase ,      -1			)

									return audio_values








'''simple docstring'''


# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						Optional[int] ,      lowercase							:						Optional[int] ,      lowercase							:						Optional[Any] ,      lowercase							:						bool = True ,      lowercase							:						bool = False			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = scheduler
									UpperCamelCase__         = optimizers if isinstance(lowercase ,      (list, tuple)			) else [optimizers]
									UpperCamelCase__         = split_batches
									UpperCamelCase__         = step_with_optimizer
									UpperCamelCase__         = GradientState()






			def 				A      (   self							:						int ,      *lowercase							:						int ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									if not self.step_with_optimizer:
															# No link between scheduler and optimizer -> just step
															self.scheduler.step(*lowercase ,      **lowercase			)
															return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
															if self.gradient_state.adjust_scheduler:
																					self.scheduler._step_count += 1
															return

									for opt in self.optimizers:
															if opt.step_was_skipped:
																					return
									if self.split_batches:
															# Split batches -> the training dataloader batch size is not changed so one step per training step
															self.scheduler.step(*lowercase ,      **lowercase			)
									else:
															# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
															# num_processes steps per training step
															UpperCamelCase__         = AcceleratorState().num_processes
															for _ in range(lowercase			):
																					# Special case when using OneCycle and `drop_last` was not used
																					if hasattr(self.scheduler ,      """total_steps"""			):
																											if self.scheduler._step_count <= self.scheduler.total_steps:
																																	self.scheduler.step(*lowercase ,      **lowercase			)
																					else:
																											self.scheduler.step(*lowercase ,      **lowercase			)






			def 				A      (   self							:						List[str]			)							->    Optional[Any]:

									'''simple docstring'''







									return self.scheduler.get_last_lr()






			def 				A      (   self							:						Any			)							->    int:

									'''simple docstring'''







									return self.scheduler.state_dict()






			def 				A      (   self							:						Any ,      lowercase							:						int			)							->    Optional[int]:

									'''simple docstring'''







									self.scheduler.load_state_dict(lowercase			)






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									return self.scheduler.get_lr()







			def 				A      (   self							:						Union[str, Any] ,      *lowercase							:						List[Any] ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									return self.scheduler.print_lr(*lowercase ,      **lowercase			)
