



# 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 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: Tuple       ,       a_							: Optional[int]       ,       a_							: Optional[Any]       ,       a_							: bool = True       ,       a_							: bool = False ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	scheduler
					__UpperCAmelCase    :     List[str]													=	optimizers if isinstance(lowerCAmelCase__       ,       (list, tuple) ) else [optimizers]
					__UpperCAmelCase    :     str													=	split_batches
					__UpperCAmelCase    :     Optional[Any]													=	step_with_optimizer
					__UpperCAmelCase    :     Dict													=	GradientState()




			def  snake_case__     (   self							: Union[str, Any]       ,       *a_							: Tuple       ,       **a_							: List[Any] ):
					'''simple docstring'''





					if not self.step_with_optimizer:
							# No link between scheduler and optimizer -> just step
							self.scheduler.step(*lowerCAmelCase__       ,       **lowerCAmelCase__ )
							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(*lowerCAmelCase__       ,       **lowerCAmelCase__ )
					else:
							# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
							# num_processes steps per training step
							__UpperCAmelCase    :     Union[str, Any]													=	AcceleratorState().num_processes
							for _ in range(lowerCAmelCase__ ):
									# 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(*lowerCAmelCase__       ,       **lowerCAmelCase__ )
									else:
											self.scheduler.step(*lowerCAmelCase__       ,       **lowerCAmelCase__ )




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					return self.scheduler.get_last_lr()




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					return self.scheduler.state_dict()




			def  snake_case__     (   self							: Dict       ,       a_							: Optional[Any] ):
					'''simple docstring'''





					self.scheduler.load_state_dict(lowerCAmelCase__ )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					return self.scheduler.get_lr()




			def  snake_case__     (   self							: Optional[int]       ,       *a_							: List[str]       ,       **a_							: Optional[int] ):
					'''simple docstring'''





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








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate



@dataclass(frozen=UpperCAmelCase_	)
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     field(default='''automatic-speech-recognition'''		,       metadata={'''include_in_asdict_even_if_is_default''': True}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''audio''': Audio()}	)
			_UpperCAmelCase   :							ClassVar[Features]										=     Features({'''transcription''': Value('''string'''	)}	)
			_UpperCAmelCase   :							str										=     "audio"
			_UpperCAmelCase   :							str										=     "transcription"



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									if self.audio_column not in features:
															raise ValueError(F"Column {self.audio_column} is not present in features.")
									if not isinstance(features[self.audio_column]   ,						lowerCAmelCase__):
															raise ValueError(F"Column {self.audio_column} is not an Audio type.")
									SCREAMING_SNAKE_CASE_: Tuple  		=       copy.deepcopy(self)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.input_schema.copy()
									SCREAMING_SNAKE_CASE_: Dict  		=       features[self.audio_column]
									SCREAMING_SNAKE_CASE_: int  		=       input_schema
									return task_template




			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return {self.audio_column: "audio", self.transcription_column: "transcription"}






