





"""simple docstring"""
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							(UniPCMultistepScheduler,)
					UpperCamelCase      =							(('''num_inference_steps''', 2_5),)


					def 	lowerCamelCase       (					self			:Dict     ,       **__UpperCamelCase			:Dict		):
												A		=  {
												    "num_train_timesteps": 10_00,
												    "beta_start": 0.0_001,
												    "beta_end": 0.02,
												    "beta_schedule": "linear",
												    "solver_order": 2,
												    "solver_type": "bh2",
												}

												config.update(**__UpperCamelCase		)
												return config


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:Optional[Any]=0     ,       **__UpperCamelCase			:int		):
												A		=  dict(self.forward_default_kwargs		)
												A		=  kwargs.pop("num_inference_steps"     ,       __UpperCamelCase		)
												A		=  self.dummy_sample
												A		=  0.1 * sample
												A		=  [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:List[str]=0     ,       **__UpperCamelCase			:Any		):
												A		=  dict(self.forward_default_kwargs		)
												A		=  kwargs.pop("num_inference_steps"     ,       __UpperCamelCase		)
												A		=  self.dummy_sample
												A		=  0.1 * sample
												A		=  [residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																			A		=  self.get_scheduler_config()
																			A		=  scheduler_class(**__UpperCamelCase		)
																			scheduler.set_timesteps(__UpperCamelCase		)

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

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

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

																			A		=  scheduler.step(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       **__UpperCamelCase		).prev_sample
																			A		=  new_scheduler.step(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       **__UpperCamelCase		).prev_sample

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


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Tuple=None     ,       **__UpperCamelCase			:Dict		):
												if scheduler is None:
																			A		=  self.scheduler_classes[0]
																			A		=  self.get_scheduler_config(**__UpperCamelCase		)
																			A		=  scheduler_class(**__UpperCamelCase		)

												A		=  self.scheduler_classes[0]
												A		=  self.get_scheduler_config(**__UpperCamelCase		)
												A		=  scheduler_class(**__UpperCamelCase		)

												A		=  10
												A		=  self.dummy_model()
												A		=  self.dummy_sample_deter
												scheduler.set_timesteps(__UpperCamelCase		)

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

												return sample


					def 	lowerCamelCase       (					self			:Any		):
												A		=  dict(self.forward_default_kwargs		)

												A		=  kwargs.pop("num_inference_steps"     ,       __UpperCamelCase		)

												for scheduler_class in self.scheduler_classes:
																			A		=  self.get_scheduler_config()
																			A		=  scheduler_class(**__UpperCamelCase		)

																			A		=  self.dummy_sample
																			A		=  0.1 * sample

																			if num_inference_steps is not None and hasattr(__UpperCamelCase     ,       "set_timesteps"		):
																										scheduler.set_timesteps(__UpperCamelCase		)
																			elif num_inference_steps is not None and not hasattr(__UpperCamelCase     ,       "set_timesteps"		):
																										A		=  num_inference_steps

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

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

																			A		=  scheduler.step(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       **__UpperCamelCase		).prev_sample
																			A		=  scheduler.step(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       **__UpperCamelCase		).prev_sample

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


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

												assert abs(result_mean.item() - 0.2_464		) < 1e-3

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

												A		=  self.full_loop(scheduler=__UpperCamelCase		)
												A		=  torch.mean(torch.abs(__UpperCamelCase		)		)

												assert abs(result_mean.item() - 0.2_464		) < 1e-3


					def 	lowerCamelCase       (					self			:List[Any]		):
												for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																			self.check_over_configs(num_train_timesteps=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict		):
												self.check_over_configs(thresholding=__UpperCamelCase		)
												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=__UpperCamelCase     ,       prediction_type=__UpperCamelCase     ,       sample_max_value=__UpperCamelCase     ,       solver_order=__UpperCamelCase     ,       solver_type=__UpperCamelCase     ,       )


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


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


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												self.check_over_configs(lower_order_final=__UpperCamelCase		)
												self.check_over_configs(lower_order_final=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[int]		):
												for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																			self.check_over_forward(num_inference_steps=__UpperCamelCase     ,       time_step=0		)


					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  self.full_loop()
												A		=  torch.mean(torch.abs(__UpperCamelCase		)		)

												assert abs(result_mean.item() - 0.2_464		) < 1e-3


					def 	lowerCamelCase       (					self			:Tuple		):
												A		=  self.full_loop(prediction_type="v_prediction"		)
												A		=  torch.mean(torch.abs(__UpperCamelCase		)		)

												assert abs(result_mean.item() - 0.1_014		) < 1e-3


					def 	lowerCamelCase       (					self			:str		):
												A		=  self.scheduler_classes[0]
												A		=  self.get_scheduler_config(thresholding=__UpperCamelCase     ,       dynamic_thresholding_ratio=0		)
												A		=  scheduler_class(**__UpperCamelCase		)

												A		=  10
												A		=  self.dummy_model()
												A		=  self.dummy_sample_deter.half()
												scheduler.set_timesteps(__UpperCamelCase		)

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

												assert sample.dtype == torch.floataa


					def 	lowerCamelCase       (					self			:str     ,       **__UpperCamelCase			:Optional[int]		):
												for scheduler_class in self.scheduler_classes:
																			A		=  self.get_scheduler_config(**__UpperCamelCase		)
																			A		=  scheduler_class(**__UpperCamelCase		)

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









"""simple docstring"""
import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class 					_UpperCAmelCase   :


					def __init__(					self			:List[Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[str]=13     ,       __UpperCamelCase			:Any=30     ,       __UpperCamelCase			:int=2     ,       __UpperCamelCase			:Union[str, Any]=3     ,       __UpperCamelCase			:Union[str, Any]=True     ,       __UpperCamelCase			:Optional[int]=True     ,       __UpperCamelCase			:List[str]=32     ,       __UpperCamelCase			:List[Any]=5     ,       __UpperCamelCase			:Dict=4     ,       __UpperCamelCase			:List[str]=37     ,       __UpperCamelCase			:str="gelu"     ,       __UpperCamelCase			:Union[str, Any]=0.1     ,       __UpperCamelCase			:List[Any]=0.1     ,       __UpperCamelCase			:Tuple=10     ,       __UpperCamelCase			:Tuple=0.02     ,       __UpperCamelCase			:int=None     ,       ):
												A		=  parent
												A		=  batch_size
												A		=  image_size
												A		=  patch_size
												A		=  num_channels
												A		=  is_training
												A		=  use_labels
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  type_sequence_label_size
												A		=  initializer_range
												A		=  scope

												# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
												A		=  (image_size // patch_size) ** 2
												A		=  num_patches + 1


					def 	lowerCamelCase       (					self			:Any		):
												A		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

												A		=  None
												if self.use_labels:
																			A		=  ids_tensor([self.batch_size]     ,       self.type_sequence_label_size		)

												A		=  self.get_config()

												return config, pixel_values, labels


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return ViTMSNConfig(
												    image_size=self.image_size     ,       patch_size=self.patch_size     ,       num_channels=self.num_channels     ,       hidden_size=self.hidden_size     ,       num_hidden_layers=self.num_hidden_layers     ,       num_attention_heads=self.num_attention_heads     ,       intermediate_size=self.intermediate_size     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       initializer_range=self.initializer_range     ,       )


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Any		):
												A		=  ViTMSNModel(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  self.type_sequence_label_size
												A		=  ViTMSNForImageClassification(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase     ,       labels=__UpperCamelCase		)
												print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"		)
												print("Labels: {labels}"		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)

												# test greyscale images
												A		=  1
												A		=  ViTMSNForImageClassification(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()

												A		=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  self.prepare_config_and_inputs()
												A,	A,	A		=  config_and_inputs
												A		=  {"pixel_values": pixel_values}
												return config, inputs_dict







@require_torch
class 					_UpperCAmelCase   (	lowercase_     ,							lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
					UpperCamelCase      =							(
					    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False


					def 	lowerCamelCase       (					self			:Optional[int]		):
												A		=  ViTMSNModelTester(self		)
												A		=  ConfigTester(self     ,       config_class=__UpperCamelCase     ,       has_text_modality=__UpperCamelCase     ,       hidden_size=37		)


					def 	lowerCamelCase       (					self			:Any		):
												self.config_tester.run_common_tests()


					@unittest.skip(reason="ViTMSN does not use inputs_embeds"		)
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												pass


					def 	lowerCamelCase       (					self			:int		):
												A,	A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			self.assertIsInstance(model.get_input_embeddings()     ,       (nn.Module)		)
																			A		=  model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__UpperCamelCase     ,       nn.Linear		)		)


					def 	lowerCamelCase       (					self			:Tuple		):
												A,	A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			A		=  inspect.signature(model.forward		)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			A		=  [*signature.parameters.keys()]

																			A		=  ["pixel_values"]
																			self.assertListEqual(arg_names[:1]     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase		)


					@slow
					def 	lowerCamelCase       (					self			:List[Any]		):
												for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A		=  ViTMSNModel.from_pretrained(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







def 					A__				(			):
							A		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							return image






@require_torch
@require_vision
class 					_UpperCAmelCase   (	unittest.TestCase ):


					@cached_property
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"		) if is_vision_available() else None


					@slow
					def 	lowerCamelCase       (					self			:Any		):
												torch.manual_seed(2		)
												A		=  ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).to(__UpperCamelCase		)

												A		=  self.default_image_processor
												A		=  prepare_img()
												A		=  image_processor(images=__UpperCamelCase     ,       return_tensors="pt"		).to(__UpperCamelCase		)

												# forward pass
												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)

												# verify the logits
												A		=  torch.Size((1, 10_00)		)
												self.assertEqual(outputs.logits.shape     ,       __UpperCamelCase		)

												A		=  torch.tensor([-0.0_803, -0.4_454, -0.2_375]		).to(__UpperCamelCase		)

												self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,       __UpperCamelCase     ,       atol=1e-4		)		)


