






"""simple docstring"""





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 							A(    snake_case_=2		,  snake_case_=True		,  snake_case_=False						):
       """simple docstring"""





       lowercase__:							List[str]         =     Accelerator(step_scheduler_with_optimizer=__UpperCamelCase		,  split_batches=__UpperCamelCase						)
       lowercase__:							str         =     torch.nn.Linear(2		,  4						)
       lowercase__:							Any         =     torch.optim.AdamW(model.parameters()		,  lr=1.0						)
       lowercase__:							Tuple         =     torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase		,  max_lr=0.01		,  steps_per_epoch=2		,  epochs=1						)
       lowercase__:							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 							A(    snake_case_=2		,  snake_case_=True		,  snake_case_=False						):
       """simple docstring"""





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

       # Optimizer has stepped
       lowercase__:							List[Any]         =     False
       scheduler.step()
       lowercase__:							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
       lowercase__:							str         =     True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              lowercase__:							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 							A(    snake_case_ = 2						):
       """simple docstring"""





       from transformers import get_linear_schedule_with_warmup

       lowercase__:							str         =     [1, 2, 4]
       for num_steps in steps:
              lowercase__:							Any         =     GradientAccumulationPlugin(num_steps=__UpperCamelCase		,  adjust_scheduler=num_steps > 1						)
              lowercase__:							Tuple         =     Accelerator(gradient_accumulation_plugin=__UpperCamelCase						)
              lowercase__:							Optional[int]         =     torch.nn.Linear(2		,  4						)
              lowercase__:							Optional[int]         =     torch.optim.AdamW(model.parameters()		,  lr=10.0						)
              lowercase__:							Optional[int]         =     get_linear_schedule_with_warmup(optimizer=__UpperCamelCase		,  num_warmup_steps=0		,  num_training_steps=20						)

              lowercase__:							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 						_a  (		unittest.TestCase       ):





   '''simple docstring'''






   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          debug_launcher(partial(UpperCamelCase__					, num_processes=1)					, num_processes=1)
          debug_launcher(partial(UpperCamelCase__					, num_processes=1					, split_batches=UpperCamelCase__)					, num_processes=1)





   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          debug_launcher(partial(UpperCamelCase__					, num_processes=1)					, num_processes=1)
          debug_launcher(partial(UpperCamelCase__					, num_processes=1					, split_batches=UpperCamelCase__)					, num_processes=1)





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




          debug_launcher(partial(UpperCamelCase__					, num_processes=1					, step_scheduler_with_optimizer=UpperCamelCase__)					, num_processes=1)





   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          debug_launcher(partial(UpperCamelCase__					, num_processes=1					, step_scheduler_with_optimizer=UpperCamelCase__)					, num_processes=1)





   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          AcceleratorState._reset_state(UpperCamelCase__)
          debug_launcher(UpperCamelCase__)
          debug_launcher(partial(UpperCamelCase__					, num_processes=1					, split_batches=UpperCamelCase__)					, num_processes=1)





   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          AcceleratorState._reset_state(UpperCamelCase__)
          debug_launcher(UpperCamelCase__)
          debug_launcher(partial(UpperCamelCase__					, num_processes=1					, split_batches=UpperCamelCase__)					, num_processes=1)





   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          AcceleratorState._reset_state(UpperCamelCase__)
          debug_launcher(partial(UpperCamelCase__					, step_scheduler_with_optimizer=UpperCamelCase__))





   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          AcceleratorState._reset_state(UpperCamelCase__)
          debug_launcher(partial(UpperCamelCase__					, step_scheduler_with_optimizer=UpperCamelCase__))





   @require_huggingface_suite
   def __lowercase							(       self)			->   Optional[int]:




          '''simple docstring'''




          AcceleratorState._reset_state(UpperCamelCase__)
          debug_launcher(partial(UpperCamelCase__					, num_processes=1))
          debug_launcher(UpperCamelCase__)








import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
					import librosa  # noqa: E402

					_SCREAMING_SNAKE_CASE     :   Tuple							 =      True
					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      ''
except Exception as e:
					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      False
					_SCREAMING_SNAKE_CASE     :   Any							 =      (
					    f"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
					)


from PIL import Image  # noqa: E402







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					"mel_config.json"





	@register_to_config
	def __init__(      self					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 2_2050					,							UpperCamelCase__ = 2048					,							UpperCamelCase__ = 512					,							UpperCamelCase__ = 80					,							UpperCamelCase__ = 32					,							):
					A__	:						Any								=				hop_length
					A__	:						List[Any]								=				sample_rate
					A__	:						Union[str, Any]								=				n_fft
					A__	:						Optional[int]								=				top_db
					A__	:						Optional[Any]								=				n_iter
					self.set_resolution(UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						str								=				None

					if not _librosa_can_be_imported:
									raise ValueError(_import_error		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						int								=				x_res
					A__	:						List[Any]								=				y_res
					A__	:						List[str]								=				self.y_res
					A__	:						int								=				self.x_res * self.hop_length - 1





	def 	__snake_case					(      self					,							UpperCamelCase__ = None					,							UpperCamelCase__ = None		):
					if audio_file is not None:
									A__							,				A__	:						Union[str, Any]								=				librosa.load(UpperCamelCase__					,							mono=UpperCamelCase__					,							sr=self.sr		)
					else:
									A__	:						Tuple								=				raw_audio

					# Pad with silence if necessary.
					if len(self.audio		) < self.x_res * self.hop_length:
									A__	:						List[Any]								=				np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio		),)		)]		)





	def 	__snake_case					(      self		):
					return len(self.audio		) // self.slice_size





	def 	__snake_case					(      self					,							UpperCamelCase__ = 0		):
					return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]





	def 	__snake_case					(      self		):
					return self.sr





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						Optional[Any]								=				librosa.feature.melspectrogram(
					    y=self.get_audio_slice(UpperCamelCase__		)					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_mels=self.n_mels		)
					A__	:						str								=				librosa.power_to_db(UpperCamelCase__					,							ref=np.max					,							top_db=self.top_db		)
					A__	:						str								=				(((log_S + self.top_db) * 255 / self.top_db).clip(0					,							255		) + 0.5).astype(np.uinta		)
					A__	:						Dict								=				Image.fromarray(UpperCamelCase__		)
					return image





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						List[Any]								=				np.frombuffer(image.tobytes()					,							dtype='''uint8'''		).reshape((image.height, image.width)		)
					A__	:						Dict								=				bytedata.astype('''float'''		) * self.top_db / 255 - self.top_db
					A__	:						Union[str, Any]								=				librosa.db_to_power(UpperCamelCase__		)
					A__	:						Dict								=				librosa.feature.inverse.mel_to_audio(
					    UpperCamelCase__					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_iter=self.n_iter		)
					return audio