




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import GLPNImageProcessor


class 							__UpperCamelCase			( unittest.TestCase  ):





			'''simple docstring'''



			def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=7       , UpperCAmelCase_=3       , UpperCAmelCase_=18       , UpperCAmelCase_=30       , UpperCAmelCase_=4_00       , UpperCAmelCase_=True       , UpperCAmelCase_=32       , UpperCAmelCase_=True       , ):
										lowerCAmelCase   =		parent
										lowerCAmelCase   =		batch_size
										lowerCAmelCase   =		num_channels
										lowerCAmelCase   =		image_size
										lowerCAmelCase   =		min_resolution
										lowerCAmelCase   =		max_resolution
										lowerCAmelCase   =		do_resize
										lowerCAmelCase   =		size_divisor
										lowerCAmelCase   =		do_rescale






			def       __snake_case		( self						):
										return {
										    "do_resize": self.do_resize,
										    "size_divisor": self.size_divisor,
										    "do_rescale": self.do_rescale,
										}




@require_torch
@require_vision
class 							__UpperCamelCase			( __UpperCAmelCase     ,					unittest.TestCase  ):





			'''simple docstring'''



			__a							: Dict										=GLPNImageProcessor if is_vision_available() else None



			def       __snake_case		( self						):
										lowerCAmelCase   =		GLPNImageProcessingTester(self						)



			@property
			def       __snake_case		( self						):
										return self.image_processor_tester.prepare_image_processor_dict()



			def       __snake_case		( self						):
										lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
										self.assertTrue(hasattr(UpperCAmelCase_       , '''do_resize'''						)						)
										self.assertTrue(hasattr(UpperCAmelCase_       , '''size_divisor'''						)						)
										self.assertTrue(hasattr(UpperCAmelCase_       , '''resample'''						)						)
										self.assertTrue(hasattr(UpperCAmelCase_       , '''do_rescale'''						)						)



			def       __snake_case		( self						):
										pass



			def       __snake_case		( self						):
										# Initialize image_processing
										lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
										# create random PIL images
										lowerCAmelCase   =		prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_						)
										for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase_       , Image.Image						)

										# Test not batched input (GLPNImageProcessor doesn't support batching)
										lowerCAmelCase   =		image_processing(image_inputs[0]       , return_tensors='''pt'''						).pixel_values
										self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
										self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)



			def       __snake_case		( self						):
										# Initialize image_processing
										lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
										# create random numpy tensors
										lowerCAmelCase   =		prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , numpify=UpperCAmelCase_						)
										for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase_       , np.ndarray						)

										# Test not batched input (GLPNImageProcessor doesn't support batching)
										lowerCAmelCase   =		image_processing(image_inputs[0]       , return_tensors='''pt'''						).pixel_values
										self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
										self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)






			def       __snake_case		( self						):
										# Initialize image_processing
										lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)
										# create random PyTorch tensors
										lowerCAmelCase   =		prepare_image_inputs(self.image_processor_tester       , equal_resolution=UpperCAmelCase_       , torchify=UpperCAmelCase_						)
										for image in image_inputs:
																	self.assertIsInstance(UpperCAmelCase_       , torch.Tensor						)

										# Test not batched input (GLPNImageProcessor doesn't support batching)
										lowerCAmelCase   =		image_processing(image_inputs[0]       , return_tensors='''pt'''						).pixel_values
										self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
										self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)









# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										=1



				@register_to_config
				def __init__( self       , UpperCAmelCase_=20_00       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=1E-3						):
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		torch.linspace(1       , self.config.sampling_eps       , UpperCAmelCase_       , device=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=None						):
											if self.timesteps is None:
																		raise ValueError(
																		    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											lowerCAmelCase   =		(
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											lowerCAmelCase   =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
											lowerCAmelCase   =		std.flatten()
											while len(std.shape						) < len(score.shape						):
																		lowerCAmelCase   =		std.unsqueeze(-1						)
											lowerCAmelCase   =		-score / std

											# compute
											lowerCAmelCase   =		-1.0 / len(self.timesteps						)

											lowerCAmelCase   =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											lowerCAmelCase   =		beta_t.flatten()
											while len(beta_t.shape						) < len(x.shape						):
																		lowerCAmelCase   =		beta_t.unsqueeze(-1						)
											lowerCAmelCase   =		-0.5 * beta_t * x

											lowerCAmelCase   =		torch.sqrt(UpperCAmelCase_						)
											lowerCAmelCase   =		drift - diffusion**2 * score
											lowerCAmelCase   =		x + drift * dt

											# add noise
											lowerCAmelCase   =		randn_tensor(x.shape       , layout=x.layout       , generator=UpperCAmelCase_       , device=x.device       , dtype=x.dtype						)
											lowerCAmelCase   =		x_mean + diffusion * math.sqrt(-dt						) * noise

											return x, x_mean






				def __len__( self						):
											return self.config.num_train_timesteps



