



'''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 __snake_case(				__lowercase ):


						'''simple docstring'''



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


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=32     ,	A_=2     ,	A_=3     ,	A_=640     ,	A_=4     ,	A_="silu"     ,	A_=3     ,	A_=32     ,	A_=0.1     ,	A_=0.1     ,	A_=0.1     ,	A_=0.0_2     ,	A_=True     ,	A_=True     ,	A_=10     ,	A_=None     ,	)     ->     Optional[int]:
							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     __snake_case   ( self				)     ->     Tuple:
							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     __snake_case   ( self				)     ->     int:
							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     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Union[str, Any]:
							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     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Optional[int]:
							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     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     str:
							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     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							lowerCAmelCase   =     config_and_inputs
							lowerCAmelCase   =     {"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_torch
class __snake_case(				__lowercase		,							__lowercase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Dict									=		(
						    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase							:   Optional[int]									=		(
						    {
						        '''feature-extraction''': MobileViTModel,
						        '''image-classification''': MobileViTForImageClassification,
						        '''image-segmentation''': MobileViTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCAmelCase							:   Union[str, Any]									=		False
						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   int									=		False
						UpperCAmelCase							:   int									=		False


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     MobileViTModelTester(self				)
							lowerCAmelCase   =     MobileViTConfigTester(self     ,	config_class=A_     ,	has_text_modality=A_				)


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							self.config_tester.run_common_tests()


						@unittest.skip(reason="""MobileViT does not use inputs_embeds"""				)
						def     __snake_case   ( self				)     ->     List[str]:
							pass


						@unittest.skip(reason="""MobileViT does not support input and output embeddings"""				)
						def     __snake_case   ( self				)     ->     Any:
							pass


						@unittest.skip(reason="""MobileViT does not output attentions"""				)
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							pass


						def     __snake_case   ( self				)     ->     Optional[Any]:
							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     __snake_case   ( self				)     ->     List[Any]:
							pass


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


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

							def check_hidden_states_output(A_     ,	A_     ,	A_				):
								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   =     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     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*A_				)




						@slow
						def     __snake_case   ( self				)     ->     List[Any]:

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







def 		_snake_case		( )      ->						Any:







	"""simple docstring"""
	lowerCAmelCase   =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
	return image







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


						'''simple docstring'''



						@cached_property
						def     __snake_case   ( self				)     ->     Any:
							return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""				) if is_vision_available() else None


						@slow
						def     __snake_case   ( self				)     ->     Any:
							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, 1000)				)
							self.assertEqual(outputs.logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]				).to(A_				)

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


						@slow
						def     __snake_case   ( self				)     ->     Dict:
							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(
							    [
							        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
							        [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
							        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
							    ]     ,	device=A_     ,	)

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




						@slow
						def     __snake_case   ( self				)     ->     Dict:
							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'''

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers.generation import (
				    MaxLengthCriteria,
				    MaxNewTokensCriteria,
				    MaxTimeCriteria,
				    StoppingCriteriaList,
				    validate_stopping_criteria,
				)

@require_torch
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     3
							lowerCAmelCase   =     250

							lowerCAmelCase   =     ids_tensor((batch_size, length)     ,	A_				)
							lowerCAmelCase   =     torch.ones((batch_size, length)     ,	device=A_     ,	dtype=torch.float				) / length
							return input_ids, scores


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     StoppingCriteriaList(
							    [
							        MaxLengthCriteria(max_length=10				),
							        MaxTimeCriteria(max_time=0.1				),
							    ]				)

							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     MaxLengthCriteria(max_length=10				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     MaxNewTokensCriteria(start_length=5     ,	max_new_tokens=5				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     StoppingCriteriaList([criteria]				)
							self.assertEqual(criteria_list.max_length     ,	10				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1     ,	initial_timestamp=time.time() - 0.2				)
							self.assertTrue(criteria(A_     ,	A_				)				)




						def     __snake_case   ( self				)     ->     Optional[int]:
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	10				)

							with self.assertWarns(A_				):
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	11				)

							lowerCAmelCase   =     validate_stopping_criteria(StoppingCriteriaList()     ,	11				)

							self.assertEqual(len(A_				)     ,	1				)