



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCAmelCase_			      =  logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE      (				SCREAMING_SNAKE_CASE_      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  ['input_values', 'attention_mask']







				def __init__(				self						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=1_60_00						,	SCREAMING_SNAKE_CASE__=1_28						,	SCREAMING_SNAKE_CASE__=10_24						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=-4.267_7393						,	SCREAMING_SNAKE_CASE__=4.568_9974						,	SCREAMING_SNAKE_CASE__=False						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					super().__init__(feature_size=UpperCamelCase__						,	sampling_rate=UpperCamelCase__						,	padding_value=UpperCamelCase__						,	**UpperCamelCase__      )
					_snake_case					:							List[Any]					     =   num_mel_bins
					_snake_case					:							int					     =   max_length
					_snake_case					:							Tuple					     =   do_normalize
					_snake_case					:							str					     =   mean
					_snake_case					:							Optional[Any]					     =   std
					_snake_case					:							Union[str, Any]					     =   return_attention_mask







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					_snake_case					:							Any					     =   torch.from_numpy(UpperCamelCase__      ).unsqueeze(0      )
					_snake_case					:							Any					     =   ta_kaldi.fbank(
					    UpperCamelCase__						,	htk_compat=UpperCamelCase__						,	sample_frequency=self.sampling_rate						,	use_energy=UpperCamelCase__						,	window_type="""hanning"""						,	num_mel_bins=self.num_mel_bins						,	dither=0.0						,	frame_shift=10						,	)

					_snake_case					:							List[str]					     =   fbank.shape[0]
					_snake_case					:							Tuple					     =   max_length - n_frames

					# pad or truncate, depending on difference
					if difference > 0:
						_snake_case					:							int					     =   torch.nn.ZeroPadad((0, 0, 0, difference)      )
						_snake_case					:							Union[str, Any]					     =   pad_module(UpperCamelCase__      )
					elif difference < 0:
						_snake_case					:							List[str]					     =   fbank[0:max_length, :]

					_snake_case					:							Any					     =   fbank.numpy()

					return fbank







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					return (input_values - (self.mean)) / (self.std * 2)







				def __call__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
							raise ValueError(
							    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
							    f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
							    f''' {self.sampling_rate} and not {sampling_rate}.'''      )
					else:
						logger.warning(
						    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
						    """Failing to do so can result in silent errors that might be hard to debug."""      )

					_snake_case					:							Optional[int]					     =   isinstance(UpperCamelCase__						,	np.ndarray      ) and len(raw_speech.shape      ) > 1
					if is_batched_numpy and len(raw_speech.shape      ) > 2:
						raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''      )
					_snake_case					:							int					     =   is_batched_numpy or (
					    isinstance(UpperCamelCase__						,	(list, tuple)      ) and (isinstance(raw_speech[0]						,	(np.ndarray, tuple, list)      ))
					)

					if is_batched:
						_snake_case					:							List[Any]					     =   [np.asarray(UpperCamelCase__						,	dtype=np.floataa      ) for speech in raw_speech]
					elif not is_batched and not isinstance(UpperCamelCase__						,	np.ndarray      ):
						_snake_case					:							str					     =   np.asarray(UpperCamelCase__						,	dtype=np.floataa      )
					elif isinstance(UpperCamelCase__						,	np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
						_snake_case					:							Dict					     =   raw_speech.astype(np.floataa      )

					# always return batch
					if not is_batched:
						_snake_case					:							Union[str, Any]					     =   [raw_speech]

					# extract fbank features and pad/truncate to max_length
					_snake_case					:							Optional[Any]					     =   [self._extract_fbank_features(UpperCamelCase__						,	max_length=self.max_length      ) for waveform in raw_speech]

					# convert into BatchFeature
					_snake_case					:							str					     =   BatchFeature({"""input_values""": features}      )

					# make sure list is in array format
					_snake_case					:							List[Any]					     =   padded_inputs.get("""input_values"""      )
					if isinstance(input_values[0]						,	UpperCamelCase__      ):
						_snake_case					:							str					     =   [np.asarray(UpperCamelCase__						,	dtype=np.floataa      ) for feature in input_values]

					# normalization
					if self.do_normalize:
						_snake_case					:							List[str]					     =   [self.normalize(UpperCamelCase__      ) for feature in input_values]

					if return_tensors is not None:
						_snake_case					:							Any					     =   padded_inputs.convert_to_tensors(UpperCamelCase__      )

					return padded_inputs







import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Any=2				,    __UpperCamelCase			:    str=True				,    __UpperCamelCase			:    Optional[Any]=False							)			->							Union[str, Any]:

				"""simple docstring"""







				A__	:						List[str]								=				Accelerator(step_scheduler_with_optimizer=__UpperCamelCase				,    split_batches=__UpperCamelCase							)
				A__	:						str								=				torch.nn.Linear(2				,    4							)
				A__	:						Any								=				torch.optim.AdamW(model.parameters()				,    lr=1.0							)
				A__	:						Tuple								=				torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase				,    max_lr=0.0_1				,    steps_per_epoch=2				,    epochs=1							)
				A__							,				A__							,				A__	:						str								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

				# Optimizer has stepped
				scheduler.step()
				if step_scheduler_with_optimizer or (num_processes == 1):
								assert (
								    scheduler.scheduler.last_epoch == num_processes
								), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
				else:
								assert (
								    scheduler.scheduler.last_epoch != num_processes
								), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Optional[Any]=2				,    __UpperCamelCase			:    Tuple=True				,    __UpperCamelCase			:    List[Any]=False							)			->							str:

				"""simple docstring"""







				A__	:						Union[str, Any]								=				Accelerator(step_scheduler_with_optimizer=__UpperCamelCase				,    split_batches=__UpperCamelCase							)
				A__	:						List[str]								=				torch.nn.Linear(2				,    4							)
				A__	:						int								=				torch.optim.AdamW(model.parameters()				,    lr=1.0							)
				A__	:						List[Any]								=				torch.optim.lr_scheduler.LambdaLR(__UpperCamelCase				,    lr_lambda=lambda __UpperCamelCase							: 1 - n / 10							)
				A__							,				A__							,				A__	:						int								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

				# Optimizer has stepped
				A__	:						List[Any]								=				False
				scheduler.step()
				A__	:						str								=				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
				assert (
				    scheduler.get_last_lr()[0] == expected_lr
				), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

				# Optimizer has not stepped
				A__	:						str								=				True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								A__	:						Dict								=				1 - 2 / 10
				assert (
				    scheduler.get_last_lr()[0] == expected_lr
				), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int = 2							)			->							List[Any]:

				"""simple docstring"""







				from transformers import get_linear_schedule_with_warmup

				A__	:						str								=				[1, 2, 4]
				for num_steps in steps:
								A__	:						Any								=				GradientAccumulationPlugin(num_steps=__UpperCamelCase				,    adjust_scheduler=num_steps > 1							)
								A__	:						Tuple								=				Accelerator(gradient_accumulation_plugin=__UpperCamelCase							)
								A__	:						Optional[int]								=				torch.nn.Linear(2				,    4							)
								A__	:						Optional[int]								=				torch.optim.AdamW(model.parameters()				,    lr=1_0.0							)
								A__	:						Optional[int]								=				get_linear_schedule_with_warmup(optimizer=__UpperCamelCase				,    num_warmup_steps=0				,    num_training_steps=20							)

								A__							,				A__							,				A__	:						List[Any]								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

								for i in range(10 * num_steps							):
												with accelerator.accumulate(__UpperCamelCase							):
																optimizer.step()
																scheduler.step()

												if i == (10 * num_steps - 2):
																assert (
																    scheduler.get_last_lr()[0] != 0
																), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
								assert (
								    scheduler.get_last_lr()[0] == 0
								), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
								GradientState._reset_state()







@require_cpu
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)					,							num_processes=1		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)					,							num_processes=1		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							step_scheduler_with_optimizer=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							step_scheduler_with_optimizer=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							step_scheduler_with_optimizer=UpperCamelCase__		)		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							step_scheduler_with_optimizer=UpperCamelCase__		)		)





	@require_huggingface_suite
	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)		)
					debug_launcher(UpperCamelCase__		)