

'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor



class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def __init__(				self	:						List[Any]		,      lowerCAmelCase_	:						Optional[Any]		,      lowerCAmelCase_	:						Tuple=7		,      lowerCAmelCase_	:						Dict=3		,      lowerCAmelCase_	:						Union[str, Any]=18		,      lowerCAmelCase_	:						Optional[int]=30		,      lowerCAmelCase_	:						Any=4_00		,      lowerCAmelCase_	:						int=True		,      lowerCAmelCase_	:						Optional[Any]=None		,      lowerCAmelCase_	:						str=True		,      lowerCAmelCase_	:						str=False		,      lowerCAmelCase_	:						List[Any]=True		,      lowerCAmelCase_	:						str=True		,      lowerCAmelCase_	:						List[Any]=[0.5, 0.5, 0.5]		,      lowerCAmelCase_	:						List[Any]=[0.5, 0.5, 0.5]		,      )      ->		Dict:





									'''simple docstring'''
									A__					:       str     =parent
									A__					:       Any     =batch_size
									A__					:       Dict     =num_channels
									A__					:       List[Any]     =image_size
									A__					:       int     =min_resolution
									A__					:       str     =max_resolution
									A__					:       List[Any]     =do_resize
									A__					:       str     =size if size is not None else {"""height""": 18, """width""": 20}
									A__					:       Optional[int]     =do_thumbnail
									A__					:       Union[str, Any]     =do_align_axis
									A__					:       Dict     =do_pad
									A__					:       Tuple     =do_normalize
									A__					:       int     =image_mean
									A__					:       Dict     =image_std



			def lowercase__						(				self	:						str			)      ->		Optional[Any]:





									'''simple docstring'''
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_thumbnail": self.do_thumbnail,
									    "do_align_long_axis": self.do_align_axis,
									    "do_pad": self.do_pad,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}



@require_torch
@require_vision
class lowerCamelCase			(  lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	DonutImageProcessor if is_vision_available() else None


			def lowercase__						(				self	:						Any			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       int     =DonutImageProcessingTester(self			)


			@property
			def lowercase__						(				self	:						str			)      ->		Optional[int]:





									'''simple docstring'''
									return self.image_processor_tester.prepare_image_processor_dict()


			def lowercase__						(				self	:						Dict			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       str     =self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(__UpperCAmelCase		,      """do_resize"""			)			)
									self.assertTrue(hasattr(__UpperCAmelCase		,      """size"""			)			)
									self.assertTrue(hasattr(__UpperCAmelCase		,      """do_thumbnail"""			)			)
									self.assertTrue(hasattr(__UpperCAmelCase		,      """do_align_long_axis"""			)			)
									self.assertTrue(hasattr(__UpperCAmelCase		,      """do_pad"""			)			)
									self.assertTrue(hasattr(__UpperCAmelCase		,      """do_normalize"""			)			)
									self.assertTrue(hasattr(__UpperCAmelCase		,      """image_mean"""			)			)
									self.assertTrue(hasattr(__UpperCAmelCase		,      """image_std"""			)			)


			def lowercase__						(				self	:						Tuple			)      ->		str:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size		,      {"""height""": 18, """width""": 20}			)

									A__					:       str     =self.image_processing_class.from_dict(self.image_processor_dict		,      size=42			)
									self.assertEqual(image_processor.size		,      {"""height""": 42, """width""": 42}			)

									# Previous config had dimensions in (width, height) order
									A__					:       Union[str, Any]     =self.image_processing_class.from_dict(self.image_processor_dict		,      size=(42, 84)			)
									self.assertEqual(image_processor.size		,      {"""height""": 84, """width""": 42}			)


			def lowercase__						(				self	:						List[str]			)      ->		Union[str, Any]:





									'''simple docstring'''
									pass


			@is_flaky()
			def lowercase__						(				self	:						Optional[int]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Dict     =self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									A__					:       int     =prepare_image_inputs(self.image_processor_tester		,      equal_resolution=__UpperCAmelCase			)
									for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase		,      Image.Image			)

									# Test not batched input
									A__					:       Optional[int]     =image_processing(image_inputs[0]		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )

									# Test batched
									A__					:       Any     =image_processing(__UpperCAmelCase		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )


			@is_flaky()
			def lowercase__						(				self	:						Dict			)      ->		str:





									'''simple docstring'''
									A__					:       str     =self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									A__					:       Optional[Any]     =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
									A__					:       List[Any]     =image_processing(image_inputs[0]		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )

									# Test batched
									A__					:       Any     =image_processing(__UpperCAmelCase		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )



			@is_flaky()
			def lowercase__						(				self	:						Optional[int]			)      ->		str:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									A__					:       List[Any]     =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
									A__					:       Optional[Any]     =image_processing(image_inputs[0]		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )

									# Test batched
									A__					:       str     =image_processing(__UpperCAmelCase		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	(CMStochasticIterativeScheduler,)
			__snake_case        =	10


			def lowercase__						(				self	:						List[str]		,      **lowerCAmelCase_	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     ={
									    """num_train_timesteps""": 2_01,
									    """sigma_min""": 0.002,
									    """sigma_max""": 80.0,
									}

									config.update(**lowerCAmelCase_			)
									return config


			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Dict     =10

									A__					:       str     =self.get_scheduler_config()
									A__					:       Any     =self.scheduler_classes[0](**lowerCAmelCase_			)

									scheduler.set_timesteps(lowerCAmelCase_			)

									A__					:       List[Any]     =scheduler.timesteps[0]
									A__					:       Union[str, Any]     =scheduler.timesteps[1]

									A__					:       Optional[int]     =self.dummy_sample
									A__					:       Union[str, Any]     =0.1 * sample

									A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample
									A__					:       int     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample

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


			def lowercase__						(				self	:						Optional[int]			)      ->		Optional[int]:





									'''simple docstring'''
									for timesteps in [10, 50, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Tuple			)      ->		Optional[int]:





									'''simple docstring'''
									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Any     =scheduler_class(**lowerCAmelCase_			)

									A__					:       int     =1
									scheduler.set_timesteps(lowerCAmelCase_			)
									A__					:       Any     =scheduler.timesteps

									A__					:       Optional[int]     =torch.manual_seed(0			)

									A__					:       List[Any]     =self.dummy_model()
									A__					:       Optional[int]     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(lowerCAmelCase_			):
															# 1. scale model input
															A__					:       Optional[Any]     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       Union[str, Any]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Tuple     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Dict     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       Optional[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 192.7614			) < 1e-2
									assert abs(result_mean.item() - 0.2510			) < 1e-3


			def lowercase__						(				self	:						Dict			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Dict     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[1_06, 0]
									scheduler.set_timesteps(timesteps=lowerCAmelCase_			)
									A__					:       List[Any]     =scheduler.timesteps

									A__					:       Optional[Any]     =torch.manual_seed(0			)

									A__					:       int     =self.dummy_model()
									A__					:       int     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															A__					:       Any     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       List[str]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Union[str, Any]     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       List[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 347.6357			) < 1e-2
									assert abs(result_mean.item() - 0.4527			) < 1e-3


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Union[str, Any]     =self.get_scheduler_config()
									A__					:       List[Any]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[39, 30, 12, 15, 0]

									with self.assertRaises(lowerCAmelCase_		,      msg="""`timesteps` must be in descending order."""			):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       List[str]     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Dict     =[39, 30, 12, 1, 0]
									A__					:       int     =len(lowerCAmelCase_			)

									with self.assertRaises(lowerCAmelCase_		,      msg="""Can only pass one of `num_inference_steps` or `timesteps`."""			):
															scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_		,      timesteps=lowerCAmelCase_			)



			def lowercase__						(				self	:						Any			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Any     =self.get_scheduler_config()
									A__					:       Optional[int]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       List[str]     =[scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    lowerCAmelCase_		,      msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""		,      ):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)

