
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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


lowercase__	:Tuple										=					logging.get_logger(__name__)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if isinstance(lowerCAmelCase__				,    (list, tuple)	) and isinstance(videos[0]				,    (list, tuple)	) and is_valid_image(videos[0][0]	):
										return videos

					elif isinstance(lowerCAmelCase__				,    (list, tuple)	) and is_valid_image(videos[0]	):
										return [videos]

					elif is_valid_image(lowerCAmelCase__	):
										return [[videos]]

					raise ValueError(f'Could not make batched video from {videos}'	)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       =['''pixel_values''']




							def __init__(       self     ,A__ = True     ,A__ = None     ,A__ = PILImageResampling.BILINEAR     ,A__ = True     ,A__ = None     ,A__ = True     ,A__ = 1 / 2_5_5     ,A__ = True     ,A__ = None     ,A__ = None     ,**A__     ,):
												super().__init__(**A__)
												lowercase					=		size if size is not None else {'''shortest_edge''': 2_2_4}
												lowercase					=		get_size_dict(A__     ,default_to_square=A__)
												lowercase					=		crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
												lowercase					=		get_size_dict(A__     ,param_name='''crop_size''')

												lowercase					=		do_resize
												lowercase					=		size
												lowercase					=		do_center_crop
												lowercase					=		crop_size
												lowercase					=		resample
												lowercase					=		do_rescale
												lowercase					=		rescale_factor
												lowercase					=		do_normalize
												lowercase					=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowercase					=		image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 				A__   (       self     ,A__     ,A__     ,A__ = PILImageResampling.BILINEAR     ,A__ = None     ,**A__     ,):
												lowercase					=		get_size_dict(A__     ,default_to_square=A__)
												if "shortest_edge" in size:
																	lowercase					=		get_resize_output_image_size(A__     ,size['''shortest_edge''']     ,default_to_square=A__)
												elif "height" in size and "width" in size:
																	lowercase					=		(size['''height'''], size['''width'''])
												else:
																	raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}')
												return resize(A__     ,size=A__     ,resample=A__     ,data_format=A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												lowercase					=		get_size_dict(A__)
												if "height" not in size or "width" not in size:
																	raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}')
												return center_crop(A__     ,size=(size['''height'''], size['''width'''])     ,data_format=A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												return rescale(A__     ,scale=A__     ,data_format=A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__ = None     ,**A__     ,):
												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     ,):
												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_center_crop and crop_size is None:
																	raise ValueError('''Crop size must be specified if do_center_crop is True.''')

												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.
												lowercase					=		to_numpy_array(A__)

												if do_resize:
																	lowercase					=		self.resize(image=A__     ,size=A__     ,resample=A__)

												if do_center_crop:
																	lowercase					=		self.center_crop(A__     ,size=A__)

												if do_rescale:
																	lowercase					=		self.rescale(image=A__     ,scale=A__)

												if do_normalize:
																	lowercase					=		self.normalize(image=A__     ,mean=A__     ,std=A__)

												lowercase					=		to_channel_dimension_format(A__     ,A__)
												return image





							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__ = None     ,A__ = ChannelDimension.FIRST     ,**A__     ,):
												lowercase					=		do_resize if do_resize is not None else self.do_resize
												lowercase					=		resample if resample is not None else self.resample
												lowercase					=		do_center_crop if do_center_crop is not None else self.do_center_crop
												lowercase					=		do_rescale if do_rescale is not None else self.do_rescale
												lowercase					=		rescale_factor if rescale_factor is not None else self.rescale_factor
												lowercase					=		do_normalize if do_normalize is not None else self.do_normalize
												lowercase					=		image_mean if image_mean is not None else self.image_mean
												lowercase					=		image_std if image_std is not None else self.image_std

												lowercase					=		size if size is not None else self.size
												lowercase					=		get_size_dict(A__     ,default_to_square=A__)
												lowercase					=		crop_size if crop_size is not None else self.crop_size
												lowercase					=		get_size_dict(A__     ,param_name='''crop_size''')

												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.''')

												lowercase					=		make_batched(A__)

												lowercase					=		[
												    [
												        self._preprocess_image(
												            image=A__     ,do_resize=A__     ,size=A__     ,resample=A__     ,do_center_crop=A__     ,crop_size=A__     ,do_rescale=A__     ,rescale_factor=A__     ,do_normalize=A__     ,image_mean=A__     ,image_std=A__     ,data_format=A__     ,)
												        for img in video
												    ]
												    for video in videos
												]

												lowercase					=		{'''pixel_values''': videos}
												return BatchFeature(data=A__     ,tensor_type=A__)






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       =(UniPCMultistepScheduler,)
							lowercase_ : Tuple       =(('''num_inference_steps''', 25),)




							def 				A__   (       self     ,**A__):
												lowercase					=		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''solver_order''': 2,
												    '''solver_type''': '''bh2''',
												}

												config.update(**A__)
												return config




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)
																	# copy over dummy past residuals
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(A__)
																						lowercase					=		scheduler_class.from_pretrained(A__)
																						new_scheduler.set_timesteps(A__)
																						# copy over dummy past residuals
																						lowercase					=		dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowercase			,		lowercase					=		sample, sample
																	for t in range(A__     ,time_step + scheduler.config.solver_order + 1):
																						lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																						lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

																						assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)

																	# copy over dummy past residuals (must be after setting timesteps)
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(A__)
																						lowercase					=		scheduler_class.from_pretrained(A__)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(A__)

																						# copy over dummy past residual (must be after setting timesteps)
																						lowercase					=		dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

																	assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"




							def 				A__   (       self     ,A__=None     ,**A__):
												if scheduler is None:
																	lowercase					=		self.scheduler_classes[0]
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(**A__)
												lowercase					=		scheduler_class(**A__)

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter
												scheduler.set_timesteps(A__)

												for i, t in enumerate(scheduler.timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

												return sample




							def 				A__   (       self):
												lowercase					=		dict(self.forward_default_kwargs)

												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)

																	lowercase					=		self.dummy_sample
																	lowercase					=		0.1 * sample

																	if num_inference_steps is not None and hasattr(A__     ,'''set_timesteps'''):
																						scheduler.set_timesteps(A__)
																	elif num_inference_steps is not None and not hasattr(A__     ,'''set_timesteps'''):
																						lowercase					=		num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	lowercase					=		scheduler.timesteps[5]
																	lowercase					=		scheduler.timesteps[6]

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

																	self.assertEqual(output_a.shape     ,sample.shape)
																	self.assertEqual(output_a.shape     ,output_a.shape)




							def 				A__   (       self):
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												lowercase					=		UniPCMultistepScheduler(**self.get_scheduler_config())
												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3

												lowercase					=		DPMSolverSinglestepScheduler.from_config(scheduler.config)
												lowercase					=		DEISMultistepScheduler.from_config(scheduler.config)
												lowercase					=		DPMSolverMultistepScheduler.from_config(scheduler.config)
												lowercase					=		UniPCMultistepScheduler.from_config(scheduler.config)

												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=A__)




							def 				A__   (       self):
												self.check_over_configs(thresholding=A__)
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=A__     ,prediction_type=A__     ,sample_max_value=A__     ,solver_order=A__     ,solver_type=A__     ,)




							def 				A__   (       self):
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=A__)




							def 				A__   (       self):
												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											lowercase					=		self.full_loop(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											assert not torch.isnan(A__).any(), "Samples have nan numbers"




							def 				A__   (       self):
												self.check_over_configs(lower_order_final=A__)
												self.check_over_configs(lower_order_final=A__)




							def 				A__   (       self):
												for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=A__     ,time_step=0)




							def 				A__   (       self):
												lowercase					=		self.full_loop()
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.full_loop(prediction_type='''v_prediction''')
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.1014) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(thresholding=A__     ,dynamic_thresholding_ratio=0)
												lowercase					=		scheduler_class(**A__)

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter.half()
												scheduler.set_timesteps(A__)

												for i, t in enumerate(scheduler.timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

												assert sample.dtype == torch.floataa





							def 				A__   (       self     ,**A__):
												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps)
																	assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps




