
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: List[Any]	=		["pixel_values"]

						def __init__(			self       ,							a = True       ,							a = None       ,							a = None       ,							a = PILImageResampling.BILINEAR       ,							a = True       ,							a = 1 / 2_55       ,							a = True       ,							a = None       ,							a = None       ,							**a       ,							)					->      None:


								'''simple docstring'''

								super().__init__(**a				)
								_UpperCamelCase						    =	size if size is not None else {"""shortest_edge""": 3_84}
								_UpperCamelCase						    =	get_size_dict(a       ,							default_to_square=a				)

								_UpperCamelCase						    =	do_resize
								_UpperCamelCase						    =	size
								# Default value set here for backwards compatibility where the value in config is None
								_UpperCamelCase						    =	crop_pct if crop_pct is not None else 2_24 / 2_56
								_UpperCamelCase						    =	resample
								_UpperCamelCase						    =	do_rescale
								_UpperCamelCase						    =	rescale_factor
								_UpperCamelCase						    =	do_normalize
								_UpperCamelCase						    =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								_UpperCamelCase						    =	image_std if image_std is not None else IMAGENET_STANDARD_STD

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a = PILImageResampling.BICUBIC       ,							a = None       ,							**a       ,							)					->      np.ndarray:


								'''simple docstring'''

								_UpperCamelCase						    =	get_size_dict(a       ,							default_to_square=a				)
								if "shortest_edge" not in size:
										raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'				)
								_UpperCamelCase						    =	size["""shortest_edge"""]

								if shortest_edge < 3_84:
										# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
										_UpperCamelCase						    =	int(shortest_edge / crop_pct				)
										_UpperCamelCase						    =	get_resize_output_image_size(a       ,							size=a       ,							default_to_square=a				)
										_UpperCamelCase						    =	resize(image=a       ,							size=a       ,							resample=a       ,							data_format=a       ,							**a				)
										# then crop to (shortest_edge, shortest_edge)
										return center_crop(image=a       ,							size=(shortest_edge, shortest_edge)       ,							data_format=a       ,							**a				)
								else:
										# warping (no cropping) when evaluated at 384 or larger
										return resize(
										    a       ,							size=(shortest_edge, shortest_edge)       ,							resample=a       ,							data_format=a       ,							**a				)

						def  A_				(			self       ,							a       ,							a       ,							a = None       ,							**a       ,							)					->      List[Any]:


								'''simple docstring'''

								return rescale(a       ,							scale=a       ,							data_format=a       ,							**a				)

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a = None       ,							**a       ,							)					->      np.ndarray:


								'''simple docstring'''

								return normalize(a       ,							mean=a       ,							std=a       ,							data_format=a       ,							**a				)







						def  A_				(			self       ,							a       ,							a = None       ,							a = None       ,							a = None       ,							a = None       ,							a = None       ,							a = None       ,							a = None       ,							a = None       ,							a = None       ,							a = None       ,							a = ChannelDimension.FIRST       ,							**a       ,							)					->      PIL.Image.Image:


								'''simple docstring'''

								_UpperCamelCase						    =	do_resize if do_resize is not None else self.do_resize
								_UpperCamelCase						    =	crop_pct if crop_pct is not None else self.crop_pct
								_UpperCamelCase						    =	resample if resample is not None else self.resample
								_UpperCamelCase						    =	do_rescale if do_rescale is not None else self.do_rescale
								_UpperCamelCase						    =	rescale_factor if rescale_factor is not None else self.rescale_factor
								_UpperCamelCase						    =	do_normalize if do_normalize is not None else self.do_normalize
								_UpperCamelCase						    =	image_mean if image_mean is not None else self.image_mean
								_UpperCamelCase						    =	image_std if image_std is not None else self.image_std

								_UpperCamelCase						    =	size if size is not None else self.size
								_UpperCamelCase						    =	get_size_dict(a       ,							default_to_square=a				)

								_UpperCamelCase						    =	make_list_of_images(a				)

								if not valid_images(a				):
										raise ValueError(
										    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
										    """torch.Tensor, tf.Tensor or jax.ndarray."""				)

								if do_resize and size is None or resample is None:
										raise ValueError("""Size and resample must be specified if do_resize is True."""				)

								if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
										raise ValueError("""crop_pct must be specified if size < 384."""				)

								if do_rescale and rescale_factor is None:
										raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

								if do_normalize and (image_mean is None or image_std is None):
										raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

								# All transformations expect numpy arrays.
								_UpperCamelCase						    =	[to_numpy_array(a				) for image in images]

								if do_resize:
										_UpperCamelCase						    =	[self.resize(image=a       ,							size=a       ,							crop_pct=a       ,							resample=a				) for image in images]

								if do_rescale:
										_UpperCamelCase						    =	[self.rescale(image=a       ,							scale=a				) for image in images]

								if do_normalize:
										_UpperCamelCase						    =	[self.normalize(image=a       ,							mean=a       ,							std=a				) for image in images]

								_UpperCamelCase						    =	[to_channel_dimension_format(a       ,							a				) for image in images]

								_UpperCamelCase						    =	{"""pixel_values""": images}
								return BatchFeature(data=a       ,							tensor_type=a				)





from typing import List, Optional, Tuple, Union

import torch

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


lowerCamelCase__	=							logging.get_logger(__name__)  # pylint: disable=invalid-name







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

								super().__init__()
								self.register_modules(unet=a       ,							scheduler=a				)







						@torch.no_grad()
						def __call__(			self       ,							a = 1       ,							a = 1_00       ,							a = None       ,							a = None       ,							a = True       ,							)					->      Union[AudioPipelineOutput, Tuple]:


								'''simple docstring'''

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

								_UpperCamelCase						    =	audio_length_in_s * self.unet.config.sample_rate

								_UpperCamelCase						    =	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}.'				)

								_UpperCamelCase						    =	int(a				)
								if sample_size % down_scale_factor != 0:
										_UpperCamelCase						    =	(
										    (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."""				)
								_UpperCamelCase						    =	int(a				)

								_UpperCamelCase						    =	next(iter(self.unet.parameters()				)				).dtype
								_UpperCamelCase						    =	(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.'				)

								_UpperCamelCase						    =	randn_tensor(a       ,							generator=a       ,							device=self.device       ,							dtype=a				)

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

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

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

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

								_UpperCamelCase						    =	audio[:, :, :original_sample_size]

								if not return_dict:
										return (audio,)

								return AudioPipelineOutput(audios=a				)



