






from typing import List, Optional, Tuple, Union

import torch

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


snake_case    :						List[Any]  =						logging.get_logger(__name__)  # pylint: disable=invalid-name




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Tuple			,		_A      :     Any			,		_A      :     Optional[int]):
											super().__init__()
											self.register_modules(unet=_A			,		scheduler=_A)







					@torch.no_grad()
					def __call__(       self      :     int			,		_A      :     int = 1			,		_A      :     int = 100			,		_A      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None			,		_A      :     Optional[float] = None			,		_A      :     bool = True			,		):

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

											A__	:							int						    = audio_length_in_s * self.unet.config.sample_rate

											A__	:							Union[str, Any]						    = 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}.')

											A__	:							List[Any]						    = int(_A)
											if sample_size % down_scale_factor != 0:
																	A__	:							List[str]						    = (
																	    (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.")
											A__	:							Dict						    = int(_A)

											A__	:							Union[str, Any]						    = next(iter(self.unet.parameters())).dtype
											A__	:							List[Any]						    = (batch_size, self.unet.config.in_channels, sample_size)
											if isinstance(_A			,		_A) and len(_A) != batch_size:
																	raise ValueError(
																	    F'You have passed a list of generators of length {len(_A)}, but requested an effective batch'
																	    F' size of {batch_size}. Make sure the batch size matches the length of the generators.')

											A__	:							Dict						    = randn_tensor(_A			,		generator=_A			,		device=self.device			,		dtype=_A)

											# set step values
											self.scheduler.set_timesteps(_A			,		device=audio.device)
											A__	:							int						    = self.scheduler.timesteps.to(_A)

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

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

											A__	:							Tuple						    = audio.clamp(-1			,		1).float().cpu().numpy()

											A__	:							Optional[Any]						    = audio[:, :, :original_sample_size]

											if not return_dict:
																	return (audio,)

											return AudioPipelineOutput(audios=_A)




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 							lowerCAmelCase__  (	unittest.TestCase						):







					@slow
					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"			,		return_dict=_A).to(_A)
											A__	:							Any						    = AutoTokenizer.from_pretrained("google/mt5-small")

											A__	:							int						    = tokenizer("Hello there"			,		return_tensors="pt").input_ids
											A__	:							List[str]						    = tokenizer("Hi I am"			,		return_tensors="pt").input_ids

											A__	:							int						    = model(input_ids.to(_A)			,		labels=labels.to(_A)).loss
											A__	:							Optional[int]						    = -(labels.shape[-1] * loss.item())

											A__	:							List[str]						    = -84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)