



"""simple docstring"""
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Any		=	(EulerAncestralDiscreteScheduler,)
					_A						:     Union[str, Any]		=	1_0






					def   lowerCAmelCase_							(			self:				Dict		,	**snake_case:				Union[str, Any]		)  ->      Optional[int]:
											snake_case_					:Any		=     {
											    """num_train_timesteps""": 1_100,
											    """beta_start""": 0.0_0_0_1,
											    """beta_end""": 0.0_2,
											    """beta_schedule""": """linear""",
											}

											config.update(**snake_case		)
											return config






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Union[str, Any]:
											for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=snake_case		)






					def   lowerCAmelCase_							(			self:				Any		)  ->      Optional[int]:
											for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,	[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
																	self.check_over_configs(beta_start=snake_case		,	beta_end=snake_case		)






					def   lowerCAmelCase_							(			self:				int		)  ->      int:
											for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=snake_case		)






					def   lowerCAmelCase_							(			self:				str		)  ->      int:
											for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=snake_case		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      List[Any]:
											snake_case_					:str		=     self.scheduler_classes[0]
											snake_case_					:Optional[Any]		=     self.get_scheduler_config()
											snake_case_					:List[str]		=     scheduler_class(**snake_case		)

											scheduler.set_timesteps(self.num_inference_steps		)

											snake_case_					:str		=     torch.manual_seed(0		)

											snake_case_					:Optional[Any]		=     self.dummy_model()
											snake_case_					:Optional[int]		=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											snake_case_					:Optional[int]		=     sample.to(snake_case		)

											for i, t in enumerate(scheduler.timesteps		):
																	snake_case_					:Optional[int]		=     scheduler.scale_model_input(snake_case		,	snake_case		)

																	snake_case_					:List[str]		=     model(snake_case		,	snake_case		)

																	snake_case_					:Optional[int]		=     scheduler.step(snake_case		,	snake_case		,	snake_case		,	generator=snake_case		)
																	snake_case_					:Optional[Any]		=     output.prev_sample

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

											assert abs(result_sum.item() - 1_5_2.3_1_9_2		) < 1E-2
											assert abs(result_mean.item() - 0.1_9_8_3		) < 1E-3






					def   lowerCAmelCase_							(			self:				Dict		)  ->      Tuple:
											snake_case_					:Any		=     self.scheduler_classes[0]
											snake_case_					:Any		=     self.get_scheduler_config(prediction_type="""v_prediction"""		)
											snake_case_					:str		=     scheduler_class(**snake_case		)

											scheduler.set_timesteps(self.num_inference_steps		)

											snake_case_					:str		=     torch.manual_seed(0		)

											snake_case_					:List[Any]		=     self.dummy_model()
											snake_case_					:int		=     self.dummy_sample_deter * scheduler.init_noise_sigma
											snake_case_					:Dict		=     sample.to(snake_case		)

											for i, t in enumerate(scheduler.timesteps		):
																	snake_case_					:Tuple		=     scheduler.scale_model_input(snake_case		,	snake_case		)

																	snake_case_					:int		=     model(snake_case		,	snake_case		)

																	snake_case_					:List[str]		=     scheduler.step(snake_case		,	snake_case		,	snake_case		,	generator=snake_case		)
																	snake_case_					:Dict		=     output.prev_sample

											snake_case_					:Any		=     torch.sum(torch.abs(snake_case		)		)
											snake_case_					:Union[str, Any]		=     torch.mean(torch.abs(snake_case		)		)

											assert abs(result_sum.item() - 1_0_8.4_4_3_9		) < 1E-2
											assert abs(result_mean.item() - 0.1_4_1_2		) < 1E-3






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Optional[int]:
											snake_case_					:Any		=     self.scheduler_classes[0]
											snake_case_					:Any		=     self.get_scheduler_config()
											snake_case_					:str		=     scheduler_class(**snake_case		)

											scheduler.set_timesteps(self.num_inference_steps		,	device=snake_case		)
											snake_case_					:Any		=     torch.manual_seed(0		)

											snake_case_					:Dict		=     self.dummy_model()
											snake_case_					:List[Any]		=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											snake_case_					:Any		=     sample.to(snake_case		)

											for t in scheduler.timesteps:
																	snake_case_					:Tuple		=     scheduler.scale_model_input(snake_case		,	snake_case		)

																	snake_case_					:Optional[Any]		=     model(snake_case		,	snake_case		)

																	snake_case_					:List[str]		=     scheduler.step(snake_case		,	snake_case		,	snake_case		,	generator=snake_case		)
																	snake_case_					:int		=     output.prev_sample

											snake_case_					:List[Any]		=     torch.sum(torch.abs(snake_case		)		)
											snake_case_					:Dict		=     torch.mean(torch.abs(snake_case		)		)

											assert abs(result_sum.item() - 1_5_2.3_1_9_2		) < 1E-2
											assert abs(result_mean.item() - 0.1_9_8_3		) < 1E-3








import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor





class        a__      (	unittest.TestCase		):






		def __init__(      self							, lowercase__							, lowercase__=7							, lowercase__=3							, lowercase__=30							, lowercase__=400							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=False							, )				->				Tuple:
				__A    =							size if size is not None else {"height": 30, "width": 30}
				__A    =							parent
				__A    =							batch_size
				__A    =							num_channels
				__A    =							min_resolution
				__A    =							max_resolution
				__A    =							do_resize
				__A    =							size
				__A    =							do_normalize
				__A    =							image_mean
				__A    =							image_std
				__A    =							do_reduce_labels






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_reduce_labels": self.do_reduce_labels,
				}



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)

		return image, map



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)
		__A    =							Image.open(dataset[2]["file"]	)
		__A    =							Image.open(dataset[3]["file"]	)

		return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Dict 					= SegformerImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							SegformerImageProcessingTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_normalize"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_mean"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_std"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_reduce_labels"						)						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class.from_dict(self.image_processor_dict						)
				self.assertEqual(image_processor.size							, {"height": 30, "width": 30}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)

				__A    =							self.image_processing_class.from_dict(self.image_processor_dict							, size=42							, reduce_labels=lowercase__						)
				self.assertEqual(image_processor.size							, {"height": 42, "width": 42}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				pass






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, Image.Image						)

				# Test not batched input
				__A    =							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    =							image_processing(lowercase__							, 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"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, numpify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, np.ndarray						)

				# Test not batched input
				__A    =							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    =							image_processing(lowercase__							, 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"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)

				# Test not batched input
				__A    =							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    =							image_processing(lowercase__							, 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"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Dict:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				__A    =							[]
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)
						maps.append(torch.zeros(image.shape[-2:]						).long()						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, maps[0]							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].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"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test not batched input (PIL images)
				__A ,   __A    =							prepare_semantic_single_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched input (PIL images)
				__A ,   __A    =							prepare_semantic_batch_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        2,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        2,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)

				# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
				__A ,   __A    =							prepare_semantic_single_inputs()
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 150						)

				__A    =							True
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

