
'''simple docstring'''



import inspect
import unittest

from transformers import MobileViTConfig
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 transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
						from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor




class   UpperCAmelCase_					(		snake_case__			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.config_class(**self.inputs_dict				)
												self.parent.assertTrue(hasattr(_A   ,       """hidden_sizes"""				)				)
												self.parent.assertTrue(hasattr(_A   ,       """neck_hidden_sizes"""				)				)
												self.parent.assertTrue(hasattr(_A   ,       """num_attention_heads"""				)				)






class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=13   ,       _lowercase=32   ,       _lowercase=2   ,       _lowercase=3   ,       _lowercase=640   ,       _lowercase=4   ,       _lowercase="silu"   ,       _lowercase=3   ,       _lowercase=32   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=0.02   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase=10   ,       _lowercase=None   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			image_size
												_lowerCAmelCase  						=			patch_size
												_lowerCAmelCase  						=			num_channels
												_lowerCAmelCase  						=			last_hidden_size
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			conv_kernel_size
												_lowerCAmelCase  						=			output_stride
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			classifier_dropout_prob
												_lowerCAmelCase  						=			use_labels
												_lowerCAmelCase  						=			is_training
												_lowerCAmelCase  						=			num_labels
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			scope




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			None
												if self.use_labels:
																		_lowerCAmelCase  						=			ids_tensor([self.batch_size]   ,       self.num_labels				)
																		_lowerCAmelCase  						=			ids_tensor([self.batch_size, self.image_size, self.image_size]   ,       self.num_labels				)

												_lowerCAmelCase  						=			self.get_config()

												return config, pixel_values, labels, pixel_labels




						def  _lowercase     ( self				):

												"""simple docstring"""



												return MobileViTConfig(
												    image_size=self.image_size   ,       patch_size=self.patch_size   ,       num_channels=self.num_channels   ,       num_attention_heads=self.num_attention_heads   ,       hidden_act=self.hidden_act   ,       conv_kernel_size=self.conv_kernel_size   ,       output_stride=self.output_stride   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       classifier_dropout_prob=self.classifier_dropout_prob   ,       initializer_range=self.initializer_range   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			MobileViTModel(config=_A				)
												model.to(_A				)
												model.eval()
												_lowerCAmelCase  						=			model(_A				)
												self.parent.assertEqual(
												    result.last_hidden_state.shape   ,       (
												        self.batch_size,
												        self.last_hidden_size,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.num_labels
												_lowerCAmelCase  						=			MobileViTForImageClassification(_A				)
												model.to(_A				)
												model.eval()
												_lowerCAmelCase  						=			model(_A   ,       labels=_A				)
												self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.num_labels
												_lowerCAmelCase  						=			MobileViTForSemanticSegmentation(_A				)
												model.to(_A				)
												model.eval()
												_lowerCAmelCase  						=			model(_A				)
												self.parent.assertEqual(
												    result.logits.shape   ,       (
												        self.batch_size,
												        self.num_labels,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )   ,       )
												_lowerCAmelCase  						=			model(_A   ,       labels=_A				)
												self.parent.assertEqual(
												    result.logits.shape   ,       (
												        self.batch_size,
												        self.num_labels,
												        self.image_size // self.output_stride,
												        self.image_size // self.output_stride,
												    )   ,       )




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.prepare_config_and_inputs()
												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			config_and_inputs
												_lowerCAmelCase  						=			{"""pixel_values""": pixel_values}
												return config, inputs_dict






@require_torch
class   UpperCAmelCase_					(		snake_case__	,							snake_case__	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Any									=       (
						    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						_lowercase		:				Union[str, Any]									=       (
						    {
						        """feature-extraction""": MobileViTModel,
						        """image-classification""": MobileViTForImageClassification,
						        """image-segmentation""": MobileViTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowercase		:				int									=       False
						_lowercase		:				Union[str, Any]									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				Dict									=       False




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			MobileViTModelTester(self				)
												_lowerCAmelCase  						=			MobileViTConfigTester(self   ,       config_class=_A   ,       has_text_modality=_A				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												self.config_tester.run_common_tests()




						@unittest.skip(reason="""MobileViT does not use inputs_embeds"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@unittest.skip(reason="""MobileViT does not support input and output embeddings"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@unittest.skip(reason="""MobileViT does not output attentions"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																		_lowerCAmelCase  						=			["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]   ,       _A				)




						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_A				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												def check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			model_class(_A				)
																		model.to(_A				)
																		model.eval()

																		with torch.no_grad():
																								_lowerCAmelCase  						=			model(**self._prepare_for_class(_A   ,       _A				)				)

																		_lowerCAmelCase  						=			outputs.hidden_states

																		_lowerCAmelCase  						=			5
																		self.assertEqual(len(_A				)   ,       _A				)

																		# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																		# with the width and height being successively divided by 2.
																		_lowerCAmelCase  						=			2
																		for i in range(len(_A				)				):
																								self.assertListEqual(
																								    list(hidden_states[i].shape[-2:]				)   ,       [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]   ,       )
																								divisor *= 2

																		self.assertEqual(self.model_tester.output_stride   ,       divisor // 2				)

												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			True
																		check_hidden_states_output(_A   ,       _A   ,       _A				)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		_lowerCAmelCase  						=			True

																		check_hidden_states_output(_A   ,       _A   ,       _A				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_A				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*_A				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowerCAmelCase  						=			MobileViTModel.from_pretrained(_A				)
																		self.assertIsNotNone(_A				)






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





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






						'''simple docstring'''






						@cached_property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""				) if is_vision_available() else None




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""				).to(_A				)

												_lowerCAmelCase  						=			self.default_image_processor
												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			image_processor(images=_A   ,       return_tensors="""pt"""				).to(_A				)

												# forward pass
												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_A				)

												# verify the logits
												_lowerCAmelCase  						=			torch.Size((1, 1_000)				)
												self.assertEqual(outputs.logits.shape   ,       _A				)

												_lowerCAmelCase  						=			torch.tensor([-1.9364, -1.2327, -0.4653]				).to(_A				)

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




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""				)
												_lowerCAmelCase  						=			model.to(_A				)

												_lowerCAmelCase  						=			MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""				)

												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			image_processor(images=_A   ,       return_tensors="""pt"""				).to(_A				)

												# forward pass
												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_A				)
												_lowerCAmelCase  						=			outputs.logits

												# verify the logits
												_lowerCAmelCase  						=			torch.Size((1, 21, 32, 32)				)
												self.assertEqual(logits.shape   ,       _A				)

												_lowerCAmelCase  						=			torch.tensor(
												    [
												        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
												        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
												        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
												    ]   ,       device=_A   ,       )

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




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""				)
												_lowerCAmelCase  						=			model.to(_A				)

												_lowerCAmelCase  						=			MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""				)

												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			image_processor(images=_A   ,       return_tensors="""pt"""				).to(_A				)

												# forward pass
												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_A				)

												_lowerCAmelCase  						=			outputs.logits.detach().cpu()

												_lowerCAmelCase  						=			image_processor.post_process_semantic_segmentation(outputs=_A   ,       target_sizes=[(50, 60)]				)
												_lowerCAmelCase  						=			torch.Size((50, 60)				)
												self.assertEqual(segmentation[0].shape   ,       _A				)

												_lowerCAmelCase  						=			image_processor.post_process_semantic_segmentation(outputs=_A				)
												_lowerCAmelCase  						=			torch.Size((32, 32)				)
												self.assertEqual(segmentation[0].shape   ,       _A				)


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_lowerCAmelCase  						=			DDIMScheduler.from_config(scheduler.config				)

												self.register_modules(unet=_lowercase   ,       scheduler=_lowercase				)




						@torch.no_grad()
						def __call__( self   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = 0.0   ,       _lowercase = 50   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if isinstance(self.unet.config.sample_size   ,       _lowercase				):
																		_lowerCAmelCase  						=			(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		_lowerCAmelCase  						=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(_lowercase   ,       _lowercase				) and len(_lowercase				) != batch_size:
																		raise ValueError(
																		    F'You have passed a list of generators of length {len(_lowercase				)}, but requested an effective batch'
																		    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

												_lowerCAmelCase  						=			randn_tensor(_lowercase   ,       generator=_lowercase   ,       device=self.device   ,       dtype=self.unet.dtype				)

												# set step values
												self.scheduler.set_timesteps(_lowercase				)

												for t in self.progress_bar(self.scheduler.timesteps				):
																		# 1. predict noise model_output
																		_lowerCAmelCase  						=			self.unet(_lowercase   ,       _lowercase				).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		_lowerCAmelCase  						=			self.scheduler.step(
																		    _lowercase   ,       _lowercase   ,       _lowercase   ,       eta=_lowercase   ,       use_clipped_model_output=_lowercase   ,       generator=_lowercase				).prev_sample

												_lowerCAmelCase  						=			(image / 2 + 0.5).clamp(0   ,       1				)
												_lowerCAmelCase  						=			image.cpu().permute(0   ,       2   ,       3   ,       1				).numpy()
												if output_type == "pil":
																		_lowerCAmelCase  						=			self.numpy_to_pil(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)
