



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)




from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def  _lowercase			(       self      :     List[Any]):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]







					def  _lowercase			(       self      :     Tuple):
											A__	:							Any						    = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
											return Dataset.from_dict(_A)







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							str						    = self._create_example_records()
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertListEqual(dset.column_names			,		["col_1", "col_2"])
											for i, r in enumerate(_A):
																	self.assertDictEqual(_A			,		example_records[i])







					def  _lowercase			(       self      :     List[str]):
											A__	:							List[str]						    = self._create_example_records()
											A__	:							Any						    = Dataset.from_list(_A)
											A__	:							Optional[int]						    = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
											self.assertEqual(dset.info			,		dset_from_dict.info)







					def  _lowercase			(       self      :     Any):  # checks what happens with missing columns
											A__	:							Dict						    = [{"col_1": 1}, {"col_2": "x"}]
											A__	:							Any						    = Dataset.from_list(_A)
											self.assertDictEqual(dset[0]			,		{"col_1": 1})
											self.assertDictEqual(dset[1]			,		{"col_1": None})  # NB: first record is used for columns







					def  _lowercase			(       self      :     str):  # checks if the type can be inferred from the second record
											A__	:							Dict						    = [{"col_1": []}, {"col_1": [1, 2]}]
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertEqual(dset.info.features["col_1"]			,		Sequence(Value("int64")))







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Dict						    = Dataset.from_list([])
											self.assertEqual(len(_A)			,		0)
											self.assertListEqual(dset.column_names			,		[])