
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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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       :				str       =			logging.get_logger(__name__)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		List[str]       =					["pixel_values"]



	def __init__(  self	:    str			,  lowercase	:    bool = True			,  lowercase	:    Dict[str, int] = None			,  lowercase	:    int = 0.9			,  lowercase	:    PILImageResampling = PILImageResampling.BICUBIC			,  lowercase	:    bool = True			,  lowercase	:    Dict[str, int] = None			,  lowercase	:    Union[int, float] = 1 / 255			,  lowercase	:    bool = True			,  lowercase	:    bool = True			,  lowercase	:    Optional[Union[float, List[float]]] = None			,  lowercase	:    Optional[Union[float, List[float]]] = None			,  **lowercase	:    List[str]			,  ):


				'''simple docstring'''


				super().__init__(**lowercase	)
				_snake_case		=			size if size is not None else {'shortest_edge': 224}
				_snake_case		=			get_size_dict(lowercase			,  default_to_square=lowercase	)
				_snake_case		=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
				_snake_case		=			get_size_dict(lowercase			,  param_name='crop_size'	)

				_snake_case		=			do_resize
				_snake_case		=			size
				_snake_case		=			crop_pct
				_snake_case		=			resample
				_snake_case		=			do_center_crop
				_snake_case		=			crop_size
				_snake_case		=			do_rescale
				_snake_case		=			rescale_factor
				_snake_case		=			do_normalize
				_snake_case		=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				_snake_case		=			image_std if image_std is not None else IMAGENET_DEFAULT_STD



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    np.ndarray			,  lowercase	:    Dict[str, int]			,  lowercase	:    Optional[float] = None			,  lowercase	:    PILImageResampling = PILImageResampling.BICUBIC			,  lowercase	:    Optional[Union[str, ChannelDimension]] = None			,  **lowercase	:    Union[str, Any]			,  ):


				'''simple docstring'''


				_snake_case		=			get_size_dict(lowercase			,  default_to_square=lowercase	)
				if "shortest_edge" not in size and ("height" not in size or "width" not in size):
							raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''	)
				if crop_pct is not None:
							if "shortest_edge" in size:
										_snake_case		=			int(size['shortest_edge'] / crop_pct	)
							elif "height" in size and "width" in size:
										if size["height"] == size["width"]:
													_snake_case		=			int(size['height'] / crop_pct	)
										else:
													_snake_case		=			(int(size['height'] / crop_pct	), int(size['width'] / crop_pct	))
							else:
										raise ValueError('Invalid size for resize: {}'.format(lowercase	)	)

							_snake_case		=			get_resize_output_image_size(lowercase			,  size=lowercase			,  default_to_square=lowercase	)
				else:
							if "shortest_edge" in size:
										_snake_case		=			get_resize_output_image_size(lowercase			,  size=size['shortest_edge']			,  default_to_square=lowercase	)
							elif "height" in size and "width" in size:
										_snake_case		=			(size['height'], size['width'])
							else:
										raise ValueError('Invalid size for resize: {}'.format(lowercase	)	)

				return resize(lowercase			,  size=lowercase			,  resample=lowercase			,  data_format=lowercase			,  **lowercase	)



	def  A							(  self	:    int			,  lowercase	:    np.ndarray			,  lowercase	:    Dict[str, int]			,  lowercase	:    Optional[Union[str, ChannelDimension]] = None			,  **lowercase	:    Union[str, Any]			,  ):


				'''simple docstring'''


				_snake_case		=			get_size_dict(lowercase	)
				if "height" not in size or "width" not in size:
							raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''	)
				return center_crop(lowercase			,  size=(size['height'], size['width'])			,  data_format=lowercase			,  **lowercase	)



	def  A							(  self	:    str			,  lowercase	:    np.ndarray			,  lowercase	:    Union[int, float]			,  lowercase	:    Optional[Union[str, ChannelDimension]] = None			,  **lowercase	:    Optional[int]			,  ):


				'''simple docstring'''


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



	def  A							(  self	:    Optional[int]			,  lowercase	:    np.ndarray			,  lowercase	:    Union[float, List[float]]			,  lowercase	:    Union[float, List[float]]			,  lowercase	:    Optional[Union[str, ChannelDimension]] = None			,  **lowercase	:    Union[str, Any]			,  ):


				'''simple docstring'''


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





	def  A							(  self	:    List[str]			,  lowercase	:    ImageInput			,  lowercase	:    bool = None			,  lowercase	:    Dict[str, int] = None			,  lowercase	:    int = None			,  lowercase	:    PILImageResampling = None			,  lowercase	:    bool = None			,  lowercase	:    Dict[str, int] = None			,  lowercase	:    bool = None			,  lowercase	:    float = None			,  lowercase	:    bool = None			,  lowercase	:    Optional[Union[float, List[float]]] = None			,  lowercase	:    Optional[Union[float, List[float]]] = None			,  lowercase	:    Optional[Union[str, TensorType]] = None			,  lowercase	:    ChannelDimension = ChannelDimension.FIRST			,  **lowercase	:    Optional[int]			,  ):


				'''simple docstring'''


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

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

				_snake_case		=			make_list_of_images(lowercase	)

				if not valid_images(lowercase	):
							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_center_crop and crop_pct is None:
							raise ValueError('Crop_pct 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.
				_snake_case		=			[to_numpy_array(lowercase	) for image in images]

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

				if do_center_crop:
							_snake_case		=			[self.center_crop(image=lowercase			,  size=lowercase	) for image in images]

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

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

				_snake_case		=			[to_channel_dimension_format(lowercase			,  lowercase	) for image in images]

				_snake_case		=			{'pixel_values': images}
				return BatchFeature(data=lowercase			,  tensor_type=lowercase	)

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





	@slow
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

				# train with a DDPM scheduler
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddpm_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)