
"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


SCREAMING_SNAKE_CASE_ 			=     logging.get_logger(__name__)  # pylint: disable=invalid-name
class 	lowerCAmelCase_						(							UpperCAmelCase__      ):



							'''simple docstring'''





							def __init__(      self  ,							snake_case_  ,							snake_case_							)    ->   List[str]:
													super().__init__()
													self.register_modules(unet=UpperCAmelCase__  ,							scheduler=UpperCAmelCase__							)




							@torch.no_grad()
							def __call__(      self  ,							snake_case_ = 1  ,							snake_case_ = 100  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = True  ,							)    ->   Union[AudioPipelineOutput, Tuple]:

													if audio_length_in_s is None:
																			__lowerCAmelCase   					=   self.unet.config.sample_size / self.unet.config.sample_rate

													__lowerCAmelCase   					=   audio_length_in_s * self.unet.config.sample_rate

													__lowerCAmelCase   					=   2 ** len(self.unet.up_blocks							)
													if sample_size < 3 * down_scale_factor:
																			raise ValueError(
																			    f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
																			    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""							)

													__lowerCAmelCase   					=   int(UpperCAmelCase__							)
													if sample_size % down_scale_factor != 0:
																			__lowerCAmelCase   					=   (
																			    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
																			) * down_scale_factor
																			logger.info(
																			    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
																			    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
																			    """ process."""							)
													__lowerCAmelCase   					=   int(UpperCAmelCase__							)

													__lowerCAmelCase   					=   next(iter(self.unet.parameters()							)							).dtype
													__lowerCAmelCase   					=   (batch_size, self.unet.config.in_channels, sample_size)
													if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__							) and len(UpperCAmelCase__							) != batch_size:
																			raise ValueError(
																			    f"""You have passed a list of generators of length {len(UpperCAmelCase__							)}, but requested an effective batch"""
																			    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

													__lowerCAmelCase   					=   randn_tensor(UpperCAmelCase__  ,							generator=UpperCAmelCase__  ,							device=self.device  ,							dtype=UpperCAmelCase__							)

													# set step values
													self.scheduler.set_timesteps(UpperCAmelCase__  ,							device=audio.device							)
													__lowerCAmelCase   					=   self.scheduler.timesteps.to(UpperCAmelCase__							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# 1. predict noise model_output
																			__lowerCAmelCase   					=   self.unet(UpperCAmelCase__  ,							UpperCAmelCase__							).sample

																			# 2. compute previous image: x_t -> t_t-1
																			__lowerCAmelCase   					=   self.scheduler.step(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__							).prev_sample

													__lowerCAmelCase   					=   audio.clamp(-1  ,							1							).float().cpu().numpy()

													__lowerCAmelCase   					=   audio[:, :, :original_sample_size]

													if not return_dict:
																			return (audio,)

													return AudioPipelineOutput(audios=UpperCAmelCase__							)






import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


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





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





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





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


