




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_					(				UpperCamelCase__  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           A__   				=					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   :			List[str]      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[Any]=13      ,				UpperCAmelCase__   :			Tuple=32      ,				UpperCAmelCase__   :			Optional[Any]=2      ,				UpperCAmelCase__   :			Optional[Any]=3      ,				UpperCAmelCase__   :			Tuple=640      ,				UpperCAmelCase__   :			Optional[int]=4      ,				UpperCAmelCase__   :			Optional[int]="silu"      ,				UpperCAmelCase__   :			Any=3      ,				UpperCAmelCase__   :			List[str]=32      ,				UpperCAmelCase__   :			List[Any]=0.1      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			Union[str, Any]=0.1      ,				UpperCAmelCase__   :			str=0.02      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Tuple=True      ,				UpperCAmelCase__   :			Optional[int]=10      ,				UpperCAmelCase__   :			Dict=None      ,				)		->str:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->int:





           '''simple docstring'''

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

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

           A__   				=					self.get_config()

           return config, pixel_values, labels, pixel_labels


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->int:





           '''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  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[Any]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Dict)		->Optional[int]:





           '''simple docstring'''

           A__   				=					MobileViTModel(config=__A)
           model.to(__A)
           model.eval()
           A__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Any)		->str:





           '''simple docstring'''

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


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





           '''simple docstring'''

           A__   				=					self.num_labels
           A__   				=					MobileViTForSemanticSegmentation(__A)
           model.to(__A)
           model.eval()
           A__   				=					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,
               )      ,				)
           A__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->List[Any]:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()
           A__				,							A__				,							A__				,							A__   				=					config_and_inputs
           A__   				=					{'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_torch
class        UpperCamelCase_					(				UpperCamelCase__	,      UpperCamelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (
        (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__													=   (
        {
            '''feature-extraction''': MobileViTModel,
            '''image-classification''': MobileViTForImageClassification,
            '''image-segmentation''': MobileViTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


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





           '''simple docstring'''

           A__   				=					MobileViTModelTester(self)
           A__   				=					MobileViTConfigTester(self      ,				config_class=__A      ,				has_text_modality=__A)


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





           '''simple docstring'''

           self.config_tester.run_common_tests()


    @unittest.skip(reason='''MobileViT does not use inputs_embeds''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->str:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''MobileViT does not support input and output embeddings''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Dict:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''MobileViT does not output attentions''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Optional[int]:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=					model_class(__A)
                  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]      ,				__A)


    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           pass


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





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__A)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->str:





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			Any):
                  A__   				=					model_class(__A)
                  model.to(__A)
                  model.eval()

                  with torch.no_grad():
                         A__   				=					model(**self._prepare_for_class(__A      ,				__A))

                  A__   				=					outputs.hidden_states

                  A__   				=					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.
                  A__   				=					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)

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=					True
                  check_hidden_states_output(__A      ,				__A      ,				__A)

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

                  check_hidden_states_output(__A      ,				__A      ,				__A)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Dict:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__A)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__A)





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Union[str, Any]:





           '''simple docstring'''

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







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



       """simple docstring"""

       A__   				=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
       return image


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

    '''simple docstring'''






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





           '''simple docstring'''

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


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''').to(__A)

           A__   				=					self.default_image_processor
           A__   				=					prepare_img()
           A__   				=					image_processor(images=__A      ,				return_tensors='''pt''').to(__A)

           # forward pass
           with torch.no_grad():
                  A__   				=					model(**__A)

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

           A__   				=					torch.tensor([-1.9364, -1.2327, -0.4653]).to(__A)

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


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->str:





           '''simple docstring'''

           A__   				=					MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')
           A__   				=					model.to(__A)

           A__   				=					MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')

           A__   				=					prepare_img()
           A__   				=					image_processor(images=__A      ,				return_tensors='''pt''').to(__A)

           # forward pass
           with torch.no_grad():
                  A__   				=					model(**__A)
           A__   				=					outputs.logits

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

           A__   				=					torch.tensor(
               [
                   [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                   [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9868, -9.7132], [-11.0_405, -11.0_221, -10.7_318]],
                   [[-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  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[Any]:





           '''simple docstring'''

           A__   				=					MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')
           A__   				=					model.to(__A)

           A__   				=					MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')

           A__   				=					prepare_img()
           A__   				=					image_processor(images=__A      ,				return_tensors='''pt''').to(__A)

           # forward pass
           with torch.no_grad():
                  A__   				=					model(**__A)

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

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

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








from __future__ import annotations

import numpy as np







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



       """simple docstring"""

       return np.maximum(0				,       lowercase_		)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


