




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

      if is_vision_available():
            from transformers import MaskFormerImageProcessor

if is_vision_available():
      from PIL import Image





class     SCREAMING_SNAKE_CASE__		:
     def __init__(						self	:		Any       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Union[str, Any]=2       ,						__lowerCamelCase	:		int=True       ,						__lowerCamelCase	:		str=False       ,						__lowerCamelCase	:		Any=10       ,						__lowerCamelCase	:		Optional[int]=3       ,						__lowerCamelCase	:		Any=32 * 4       ,						__lowerCamelCase	:		str=32 * 6       ,						__lowerCamelCase	:		Dict=4       ,						__lowerCamelCase	:		Any=32       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      parent
       lowerCAmelCase__										=      batch_size
       lowerCAmelCase__										=      is_training
       lowerCAmelCase__										=      use_auxiliary_loss
       lowerCAmelCase__										=      num_queries
       lowerCAmelCase__										=      num_channels
       lowerCAmelCase__										=      min_size
       lowerCAmelCase__										=      max_size
       lowerCAmelCase__										=      num_labels
       lowerCAmelCase__										=      mask_feature_size
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
           __lowerCamelCase						)

       lowerCAmelCase__										=      torch.ones([self.batch_size, self.min_size, self.max_size]       ,						device=__lowerCamelCase						)

       lowerCAmelCase__										=      (
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,						device=__lowerCamelCase						) > 0.5
       ).float()
       lowerCAmelCase__										=      (torch.rand((self.batch_size, self.num_labels)       ,						device=__lowerCamelCase						) > 0.5).long()

       lowerCAmelCase__										=      self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       return MaskFormerConfig.from_backbone_and_decoder_configs(
           backbone_config=SwinConfig(
               depths=[1, 1, 1, 1]       ,						)       ,						decoder_config=DetrConfig(
               decoder_ffn_dim=1_28       ,						num_queries=self.num_queries       ,						decoder_attention_heads=2       ,						d_model=self.mask_feature_size       ,						)       ,						mask_feature_size=self.mask_feature_size       ,						fpn_feature_size=self.mask_feature_size       ,						num_channels=self.num_channels       ,						num_labels=self.num_labels       ,						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.prepare_config_and_inputs()
       lowerCAmelCase__										=      {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
       return config, inputs_dict
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		List[Any]       ,						__lowerCamelCase	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      output.encoder_hidden_states
       lowerCAmelCase__										=      output.pixel_decoder_hidden_states
       lowerCAmelCase__										=      output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(__lowerCamelCase						)       ,						len(config.backbone_config.depths						)						)
       self.parent.assertTrue(len(__lowerCamelCase						)       ,						len(config.backbone_config.depths						)						)
       self.parent.assertTrue(len(__lowerCamelCase						)       ,						config.decoder_config.decoder_layers						)
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Tuple=False						):



       """simple docstring"""
       with torch.no_grad():
         lowerCAmelCase__										=      MaskFormerModel(config=__lowerCamelCase						)
         model.to(__lowerCamelCase						)
         model.eval()

         lowerCAmelCase__										=      model(pixel_values=__lowerCamelCase       ,						pixel_mask=__lowerCamelCase						)
         lowerCAmelCase__										=      model(__lowerCamelCase       ,						output_hidden_states=__lowerCamelCase						)
       # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
       # encoder and pixel decoder
       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape       ,						(self.batch_size, self.num_queries, self.mask_feature_size)       ,						)
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None						)
       self.parent.assertTrue(output.encoder_last_hidden_state is not None						)

       if output_hidden_states:
         self.check_output_hidden_state(__lowerCamelCase       ,						__lowerCamelCase						)



     def        A__      (						self	:		str       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      MaskFormerForInstanceSegmentation(config=__lowerCamelCase						)
       model.to(__lowerCamelCase						)
       model.eval()

       def comm_check_on_output(__lowerCamelCase	:		List[Any]						):
         # let's still check that all the required stuff is there
         self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None						)
         self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None						)
         self.parent.assertTrue(result.encoder_last_hidden_state is not None						)
         # okay, now we need to check the logits shape
         # due to the encoder compression, masks have a //4 spatial size
         self.parent.assertEqual(
             result.masks_queries_logits.shape       ,						(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)       ,						)
         # + 1 for null class
         self.parent.assertEqual(
             result.class_queries_logits.shape       ,						(self.batch_size, self.num_queries, self.num_labels + 1)						)

       with torch.no_grad():
         lowerCAmelCase__										=      model(pixel_values=__lowerCamelCase       ,						pixel_mask=__lowerCamelCase						)
         lowerCAmelCase__										=      model(__lowerCamelCase						)

         comm_check_on_output(__lowerCamelCase						)

         lowerCAmelCase__										=      model(
             pixel_values=__lowerCamelCase       ,						pixel_mask=__lowerCamelCase       ,						mask_labels=__lowerCamelCase       ,						class_labels=__lowerCamelCase						)

       comm_check_on_output(__lowerCamelCase						)

       self.parent.assertTrue(result.loss is not None						)
       self.parent.assertEqual(result.loss.shape       ,						torch.Size([1]						)						)





@require_torch
class     SCREAMING_SNAKE_CASE__		(_UpperCamelCase    ,					_UpperCamelCase    ,					unittest.TestCase			):
     lowercase_ :      Tuple				= (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
     lowercase_ :      Optional[int]				= (
         {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
         if is_torch_available()
         else {}
     )

     lowercase_ :      Optional[int]				= False
     lowercase_ :      Any				= False
     lowercase_ :      List[str]				= False
     lowercase_ :      List[Any]				= False
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      MaskFormerModelTester(self						)
       lowerCAmelCase__										=      ConfigTester(self       ,						config_class=__lowerCamelCase       ,						has_text_modality=__lowerCamelCase						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       self.config_tester.run_common_tests()
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(__lowerCamelCase       ,						**__lowerCamelCase       ,						output_hidden_states=__lowerCamelCase						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCamelCase						)
     @unittest.skip(reason='''MaskFormer does not use inputs_embeds'''						)
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       pass
     @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''						)
     def        A__      (						self	:		int						):



       """simple docstring"""
       pass
     @unittest.skip(reason='''MaskFormer is not a generative model'''						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       pass
     @unittest.skip(reason='''MaskFormer does not use token embeddings'''						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       pass
     @require_torch_multi_gpu
     @unittest.skip(
         reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       pass
     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       pass
     def        A__      (						self	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         lowerCAmelCase__										=      model_class(__lowerCamelCase						)
         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]       ,						__lowerCamelCase						)
     @slow
     def        A__      (						self	:		int						):



       """simple docstring"""
       for model_name in ["facebook/maskformer-swin-small-coco"]:
         lowerCAmelCase__										=      MaskFormerModel.from_pretrained(__lowerCamelCase						)
         self.assertIsNotNone(__lowerCamelCase						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      (self.model_tester.min_size,) * 2
       lowerCAmelCase__										=      {
           "pixel_values": torch.randn((2, 3, *size)       ,						device=__lowerCamelCase						),
           "mask_labels": torch.randn((2, 10, *size)       ,						device=__lowerCamelCase						),
           "class_labels": torch.zeros(2       ,						10       ,						device=__lowerCamelCase						).long(),
       }

       lowerCAmelCase__										=      MaskFormerForInstanceSegmentation(MaskFormerConfig()						).to(__lowerCamelCase						)
       lowerCAmelCase__										=      model(**__lowerCamelCase						)
       self.assertTrue(outputs.loss is not None						)
     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(__lowerCamelCase       ,						**__lowerCamelCase       ,						output_hidden_states=__lowerCamelCase						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         lowerCAmelCase__										=      model_class(__lowerCamelCase						).to(__lowerCamelCase						)
         lowerCAmelCase__										=      model(**__lowerCamelCase       ,						output_attentions=__lowerCamelCase						)
         self.assertTrue(outputs.attentions is not None						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       if not self.model_tester.is_training:
         return
       # only MaskFormerForInstanceSegmentation has the loss
       lowerCAmelCase__										=      self.all_model_classes[1]
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()

       lowerCAmelCase__										=      model_class(__lowerCamelCase						)
       model.to(__lowerCamelCase						)
       model.train()

       lowerCAmelCase__										=      model(__lowerCamelCase       ,						mask_labels=__lowerCamelCase       ,						class_labels=__lowerCamelCase						).loss
       loss.backward()



     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       # only MaskFormerForInstanceSegmentation has the loss
       lowerCAmelCase__										=      self.all_model_classes[1]
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       lowerCAmelCase__										=      True
       lowerCAmelCase__										=      True

       lowerCAmelCase__										=      model_class(__lowerCamelCase						)
       model.to(__lowerCamelCase						)
       model.train()

       lowerCAmelCase__										=      model(__lowerCamelCase       ,						mask_labels=__lowerCamelCase       ,						class_labels=__lowerCamelCase						)

       lowerCAmelCase__										=      outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       lowerCAmelCase__										=      outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()
       # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
       lowerCAmelCase__										=      outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       lowerCAmelCase__										=      outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=__lowerCamelCase						)

       self.assertIsNotNone(encoder_hidden_states.grad						)
       self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
       self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
       self.assertIsNotNone(attentions.grad						)



__magic_name__  :  int					=       1E-4
def 		a_							( ):
  lowerCAmelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
  return image





@require_vision
@slow
class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     @cached_property
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       return (
           MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''						)
           if is_vision_available()
           else None
       )
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''						).to(__lowerCamelCase						)
       lowerCAmelCase__										=      self.default_image_processor
       lowerCAmelCase__										=      prepare_img()
       lowerCAmelCase__										=      image_processor(__lowerCamelCase       ,						return_tensors='''pt'''						).to(__lowerCamelCase						)
       lowerCAmelCase__										=      inputs["pixel_values"].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
       # check size
       self.assertEqual(__lowerCamelCase       ,						(1, 3, 8_00, 10_88)						)

       with torch.no_grad():
         lowerCAmelCase__										=      model(**__lowerCamelCase						)

       lowerCAmelCase__										=      torch.tensor(
           [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]						).to(__lowerCamelCase						)
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,						__lowerCamelCase       ,						atol=__lowerCamelCase						)						)

       lowerCAmelCase__										=      torch.tensor(
           [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]						).to(__lowerCamelCase						)
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,						__lowerCamelCase       ,						atol=__lowerCamelCase						)						)

       lowerCAmelCase__										=      torch.tensor(
           [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]						).to(__lowerCamelCase						)
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,						__lowerCamelCase       ,						atol=__lowerCamelCase						)						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      (
           MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''						)
           .to(__lowerCamelCase						)
           .eval()
       )
       lowerCAmelCase__										=      self.default_image_processor
       lowerCAmelCase__										=      prepare_img()
       lowerCAmelCase__										=      image_processor(__lowerCamelCase       ,						return_tensors='''pt'''						).to(__lowerCamelCase						)
       lowerCAmelCase__										=      inputs["pixel_values"].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
       # check size
       self.assertEqual(__lowerCamelCase       ,						(1, 3, 8_00, 10_88)						)

       with torch.no_grad():
         lowerCAmelCase__										=      model(**__lowerCamelCase						)
       # masks_queries_logits
       lowerCAmelCase__										=      outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape       ,						(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       ,						)
       lowerCAmelCase__										=      [
           [-1.373_7124, -1.772_4937, -1.936_4233],
           [-1.597_7281, -1.986_7939, -2.152_3695],
           [-1.579_5398, -1.926_9832, -2.09_3942],
       ]
       lowerCAmelCase__										=      torch.tensor(__lowerCamelCase						).to(__lowerCamelCase						)
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,						__lowerCamelCase       ,						atol=__lowerCamelCase						)						)
       # class_queries_logits
       lowerCAmelCase__										=      outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape       ,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
       lowerCAmelCase__										=      torch.tensor(
           [
               [1.6512e00, -5.2572e00, -3.3519e00],
               [3.6169e-02, -5.9025e00, -2.9313e00],
               [1.0766e-04, -7.7630e00, -5.1263e00],
           ]						).to(__lowerCamelCase						)
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,						__lowerCamelCase       ,						atol=__lowerCamelCase						)						)
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      (
           MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''						)
           .to(__lowerCamelCase						)
           .eval()
       )
       lowerCAmelCase__										=      self.default_image_processor
       lowerCAmelCase__										=      prepare_img()
       lowerCAmelCase__										=      image_processor(__lowerCamelCase       ,						return_tensors='''pt'''						).to(__lowerCamelCase						)
       lowerCAmelCase__										=      inputs["pixel_values"].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
       # check size
       self.assertEqual(__lowerCamelCase       ,						(1, 3, 8_00, 10_88)						)

       with torch.no_grad():
         lowerCAmelCase__										=      model(**__lowerCamelCase						)
       # masks_queries_logits
       lowerCAmelCase__										=      outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape       ,						(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       ,						)
       lowerCAmelCase__										=      [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
       lowerCAmelCase__										=      torch.tensor(__lowerCamelCase						).to(__lowerCamelCase						)
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,						__lowerCamelCase       ,						atol=__lowerCamelCase						)						)
       # class_queries_logits
       lowerCAmelCase__										=      outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape       ,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
       lowerCAmelCase__										=      torch.tensor(
           [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]						).to(__lowerCamelCase						)
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,						__lowerCamelCase       ,						atol=__lowerCamelCase						)						)



     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      (
           MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''						)
           .to(__lowerCamelCase						)
           .eval()
       )
       lowerCAmelCase__										=      self.default_image_processor

       lowerCAmelCase__										=      image_processor(
           [np.zeros((3, 8_00, 13_33)						), np.zeros((3, 8_00, 13_33)						)]       ,						segmentation_maps=[np.zeros((3_84, 3_84)						).astype(np.floataa						), np.zeros((3_84, 3_84)						).astype(np.floataa						)]       ,						return_tensors='''pt'''       ,						)

       lowerCAmelCase__										=      inputs["pixel_values"].to(__lowerCamelCase						)
       lowerCAmelCase__										=      [el.to(__lowerCamelCase						) for el in inputs["mask_labels"]]
       lowerCAmelCase__										=      [el.to(__lowerCamelCase						) for el in inputs["class_labels"]]

       with torch.no_grad():
         lowerCAmelCase__										=      model(**__lowerCamelCase						)

       self.assertTrue(outputs.loss is not None						)






"""simple docstring"""




import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
 from transformers.models.mobilevitva.modeling_mobilevitva import (
     MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
     make_divisible,
 )


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Dict														=       self.config_class(**self.inputs_dict		)
           self.parent.assertTrue(hasattr(A	,      "width_multiplier"		)		)







class       UpperCAmelCase_    :



      def __init__(	self	:      Dict	,      A	:      Union[str, Any]	,      A	:      Dict=1_3	,      A	:      Union[str, Any]=6_4	,      A	:      int=2	,      A	:      Tuple=3	,      A	:      int="swish"	,      A	:      Optional[int]=3	,      A	:      Tuple=3_2	,      A	:      Tuple=0.1	,      A	:      List[Any]=0.02	,      A	:      Dict=True	,      A	:      str=True	,      A	:      Any=1_0	,      A	:      Optional[int]=None	,      A	:      int=0.25	,      A	:      int=0.0	,      A	:      Dict=0.0	,      ):
           _UpperCAmelCase     :      List[Any]														=       parent
           _UpperCAmelCase     :      Optional[Any]														=       batch_size
           _UpperCAmelCase     :      Dict														=       image_size
           _UpperCAmelCase     :      int														=       patch_size
           _UpperCAmelCase     :      List[str]														=       num_channels
           _UpperCAmelCase     :      Optional[int]														=       make_divisible(5_1_2 * width_multiplier	,      divisor=8		)
           _UpperCAmelCase     :      Dict														=       hidden_act
           _UpperCAmelCase     :      List[str]														=       conv_kernel_size
           _UpperCAmelCase     :      Optional[int]														=       output_stride
           _UpperCAmelCase     :      Union[str, Any]														=       classifier_dropout_prob
           _UpperCAmelCase     :      str														=       use_labels
           _UpperCAmelCase     :      Dict														=       is_training
           _UpperCAmelCase     :      Optional[int]														=       num_labels
           _UpperCAmelCase     :      Tuple														=       initializer_range
           _UpperCAmelCase     :      Tuple														=       scope
           _UpperCAmelCase     :      List[Any]														=       width_multiplier
           _UpperCAmelCase     :      List[str]														=       ffn_dropout
           _UpperCAmelCase     :      List[Any]														=       attn_dropout



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

           _UpperCAmelCase     :      str														=       None
           _UpperCAmelCase     :      List[Any]														=       None
           if self.use_labels:
                _UpperCAmelCase     :      Dict														=       ids_tensor([self.batch_size]	,      self.num_labels		)
                _UpperCAmelCase     :      List[Any]														=       ids_tensor([self.batch_size, self.image_size, self.image_size]	,      self.num_labels		)

           _UpperCAmelCase     :      Optional[int]														=       self.get_config()

           return config, pixel_values, labels, pixel_labels



      def 		snake_case_   (	self	:      List[Any]		):
           return MobileViTVaConfig(
               image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      hidden_act=self.hidden_act	,      conv_kernel_size=self.conv_kernel_size	,      output_stride=self.output_stride	,      classifier_dropout_prob=self.classifier_dropout_prob	,      initializer_range=self.initializer_range	,      width_multiplier=self.width_multiplier	,      ffn_dropout=self.ffn_dropout_prob	,      attn_dropout=self.attn_dropout_prob	,      )



      def 		snake_case_   (	self	:      int	,      A	:      Optional[Any]	,      A	:      Optional[int]	,      A	:      Any	,      A	:      int		):
           _UpperCAmelCase     :      List[str]														=       MobileViTVaModel(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      str														=       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	:      Any	,      A	:      Optional[int]	,      A	:      Optional[Any]	,      A	:      str	,      A	:      List[str]		):
           _UpperCAmelCase     :      List[str]														=       self.num_labels
           _UpperCAmelCase     :      int														=       MobileViTVaForImageClassification(A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      str														=       model(A	,      labels=A		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      Tuple	,      A	:      Any	,      A	:      Any	,      A	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.num_labels
           _UpperCAmelCase     :      Optional[int]														=       MobileViTVaForSemanticSegmentation(A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      List[str]														=       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,
               )	,      )
           _UpperCAmelCase     :      Optional[int]														=       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	:      Any		):
           _UpperCAmelCase     :      Any														=       self.prepare_config_and_inputs()
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      str														=       config_and_inputs
           _UpperCAmelCase     :      str														=       {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Tuple						=					(
          (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
          if is_torch_available()
          else ()
      )

      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					(
          {
              'feature-extraction': MobileViTVaModel,
              'image-classification': MobileViTVaForImageClassification,
              'image-segmentation': MobileViTVaForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE		:  str						=					False
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					False
      __SCREAMING_SNAKE_CASE		:  Dict						=					False
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					False



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Dict														=       MobileViTVaModelTester(self		)
           _UpperCAmelCase     :      Optional[Any]														=       MobileViTVaConfigTester(self	,      config_class=A	,      has_text_modality=A		)



      def 		snake_case_   (	self	:      int		):
           self.config_tester.run_common_tests()



      @unittest.skip(reason="MobileViTV2 does not use inputs_embeds"		)
      def 		snake_case_   (	self	:      Optional[int]		):
           pass



      @unittest.skip(reason="MobileViTV2 does not support input and output embeddings"		)
      def 		snake_case_   (	self	:      Optional[Any]		):
           pass



      @unittest.skip(reason="MobileViTV2 does not output attentions"		)
      def 		snake_case_   (	self	:      int		):
           pass



      @require_torch_multi_gpu
      @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run."		)
      def 		snake_case_   (	self	:      Any		):
           pass



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



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase     :      Optional[Any]														=       ["pixel_values"]
                self.assertListEqual(arg_names[:1]	,      A		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           def check_hidden_states_output(A	:      Union[str, Any]	,      A	:      Dict	,      A	:      str		):
                _UpperCAmelCase     :      int														=       model_class(A		)
                model.to(A		)
                model.eval()

                with torch.no_grad():
                     _UpperCAmelCase     :      Tuple														=       model(**self._prepare_for_class(A	,      A		)		)

                _UpperCAmelCase     :      List[str]														=       outputs.hidden_states

                _UpperCAmelCase     :      int														=       5
                self.assertEqual(len(A		)	,      A		)

                # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
                # with the width and height being successively divided by 2.
                _UpperCAmelCase     :      Optional[int]														=       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		)

           _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase     :      Optional[Any]														=       True
                check_hidden_states_output(A	,      A	,      A		)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                _UpperCAmelCase     :      str														=       True

                check_hidden_states_output(A	,      A	,      A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[str]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      int														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*A		)



      @slow
      def 		snake_case_   (	self	:      int		):
           for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      Union[str, Any]														=       MobileViTVaModel.from_pretrained(A		)
                self.assertIsNotNone(A		)







def 		__snake_case		( )    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image






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



      @cached_property
      def 		snake_case_   (	self	:      Any		):
           return (
               MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"		)
               if is_vision_available()
               else None
           )



      @slow
      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Dict														=       MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256"		).to(
               A		)

           _UpperCAmelCase     :      Optional[Any]														=       self.default_image_processor
           _UpperCAmelCase     :      Tuple														=       prepare_img()
           _UpperCAmelCase     :      Union[str, Any]														=       image_processor(images=A	,      return_tensors="pt"		).to(A		)

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

           # verify the logits
           _UpperCAmelCase     :      Tuple														=       torch.Size((1, 1_0_0_0)		)
           self.assertEqual(outputs.logits.shape	,      A		)

           _UpperCAmelCase     :      Optional[Any]														=       torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]		).to(A		)

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



      @slow
      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      List[Any]														=       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)
           _UpperCAmelCase     :      Any														=       model.to(A		)

           _UpperCAmelCase     :      Optional[Any]														=       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)

           _UpperCAmelCase     :      Dict														=       prepare_img()
           _UpperCAmelCase     :      Union[str, Any]														=       image_processor(images=A	,      return_tensors="pt"		).to(A		)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase     :      int														=       model(**A		)
           _UpperCAmelCase     :      Any														=       outputs.logits

           # verify the logits
           _UpperCAmelCase     :      Optional[int]														=       torch.Size((1, 2_1, 3_2, 3_2)		)
           self.assertEqual(logits.shape	,      A		)

           _UpperCAmelCase     :      Union[str, Any]														=       torch.tensor(
               [
                   [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
                   [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
                   [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
               ]	,      device=A	,      )

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



      @slow
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      List[str]														=       MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)
           _UpperCAmelCase     :      Tuple														=       model.to(A		)

           _UpperCAmelCase     :      Dict														=       MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3"		)

           _UpperCAmelCase     :      Optional[int]														=       prepare_img()
           _UpperCAmelCase     :      Optional[int]														=       image_processor(images=A	,      return_tensors="pt"		).to(A		)

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

           _UpperCAmelCase     :      Optional[int]														=       outputs.logits.detach().cpu()

           _UpperCAmelCase     :      List[Any]														=       image_processor.post_process_semantic_segmentation(outputs=A	,      target_sizes=[(5_0, 6_0)]		)
           _UpperCAmelCase     :      Dict														=       torch.Size((5_0, 6_0)		)
           self.assertEqual(segmentation[0].shape	,      A		)

           _UpperCAmelCase     :      Dict														=       image_processor.post_process_semantic_segmentation(outputs=A		)
           _UpperCAmelCase     :      Union[str, Any]														=       torch.Size((3_2, 3_2)		)
           self.assertEqual(segmentation[0].shape	,      A		)
