


'''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   __lowerCAmelCase						(lowercase__     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= self.config_class(**self.inputs_dict )
             self.parent.assertTrue(hasattr(__lowerCamelCase   ,						'''hidden_sizes''' ) )
             self.parent.assertTrue(hasattr(__lowerCamelCase   ,						'''neck_hidden_sizes''' ) )
             self.parent.assertTrue(hasattr(__lowerCamelCase   ,						'''num_attention_heads''' ) )






class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    List[Any]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    List[Any]=13   ,						UpperCamelCase    :    List[str]=32   ,						UpperCamelCase    :    int=2   ,						UpperCamelCase    :    Union[str, Any]=3   ,						UpperCamelCase    :    Tuple=640   ,						UpperCamelCase    :    List[str]=4   ,						UpperCamelCase    :    int="silu"   ,						UpperCamelCase    :    Optional[Any]=3   ,						UpperCamelCase    :    str=32   ,						UpperCamelCase    :    Optional[int]=0.1   ,						UpperCamelCase    :    Optional[int]=0.1   ,						UpperCamelCase    :    str=0.1   ,						UpperCamelCase    :    List[str]=0.02   ,						UpperCamelCase    :    Dict=True   ,						UpperCamelCase    :    Any=True   ,						UpperCamelCase    :    Any=10   ,						UpperCamelCase    :    Any=None   ,						):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







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

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

             lowercase__      			= self.get_config()

             return config, pixel_values, labels, pixel_labels



      def       UpperCamelCase__		(self    :    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       UpperCamelCase__		(self    :    Union[str, Any]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Dict ):

             '''simple docstring'''







             lowercase__      			= MobileViTModel(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowercase__      			= model(__lowerCamelCase )
             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       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= self.num_labels
             lowercase__      			= MobileViTForSemanticSegmentation(__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowercase__      			= model(__lowerCamelCase )
             self.parent.assertEqual(
                 result.logits.shape   ,						(
                     self.batch_size,
                     self.num_labels,
                     self.image_size // self.output_stride,
                     self.image_size // self.output_stride,
                 )   ,						)
             lowercase__      			= model(__lowerCamelCase   ,						labels=__lowerCamelCase )
             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       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







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






@require_torch
class   __lowerCAmelCase						(lowercase__  ,     lowercase__  ,     unittest.TestCase     ):





      '''simple docstring'''






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

      lowerCAmelCase__					:     Union[str, Any]  					=							False
      lowerCAmelCase__					:     List[Any]  					=							False
      lowerCAmelCase__					:     Tuple  					=							False
      lowerCAmelCase__					:     Optional[int]  					=							False



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= MobileViTModelTester(self )
             lowercase__      			= MobileViTConfigTester(self   ,						config_class=__lowerCamelCase   ,						has_text_modality=__lowerCamelCase )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      @unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''MobileViT does not output attentions''' )
      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__      			= ["pixel_values"]
                    self.assertListEqual(arg_names[:1]   ,						__lowerCamelCase )



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

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase )



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             def check_hidden_states_output(UpperCamelCase    :    Dict   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Any ):
                    lowercase__      			= model_class(__lowerCamelCase )
                    model.to(__lowerCamelCase )
                    model.eval()

                    with torch.no_grad():
                           lowercase__      			= model(**self._prepare_for_class(__lowerCamelCase   ,						__lowerCamelCase ) )

                    lowercase__      			= outputs.hidden_states

                    lowercase__      			= 5
                    self.assertEqual(len(__lowerCamelCase )   ,						__lowerCamelCase )

                    # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
                    # with the width and height being successively divided by 2.
                    lowercase__      			= 2
                    for i in range(len(__lowerCamelCase ) ):
                           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 )

             lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__      			= True
                    check_hidden_states_output(__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase )

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

                    check_hidden_states_output(__lowerCamelCase   ,						__lowerCamelCase   ,						__lowerCamelCase )



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase )






      @slow
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







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






def _SCREAMING_SNAKE_CASE      ()       ->       int:






       """simple docstring"""







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

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





      '''simple docstring'''






      @cached_property
      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







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



      @slow
      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' ).to(__lowerCamelCase )

             lowercase__      			= self.default_image_processor
             lowercase__      			= prepare_img()
             lowercase__      			= image_processor(images=__lowerCamelCase   ,						return_tensors='''pt''' ).to(__lowerCamelCase )

             # forward pass
             with torch.no_grad():
                    lowercase__      			= model(**__lowerCamelCase )

             # verify the logits
             lowercase__      			= torch.Size((1, 1000) )
             self.assertEqual(outputs.logits.shape   ,						__lowerCamelCase )

             lowercase__      			= torch.tensor([-1.93_64, -1.23_27, -0.46_53] ).to(__lowerCamelCase )

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



      @slow
      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







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

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

             lowercase__      			= prepare_img()
             lowercase__      			= image_processor(images=__lowerCamelCase   ,						return_tensors='''pt''' ).to(__lowerCamelCase )

             # forward pass
             with torch.no_grad():
                    lowercase__      			= model(**__lowerCamelCase )
             lowercase__      			= outputs.logits

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

             lowercase__      			= torch.tensor(
                 [
                     [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
                     [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.98_68, -9.71_32], [-11.0405, -11.0221, -10.7318]],
                     [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
                 ]   ,						device=__lowerCamelCase   ,						)

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






      @slow
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







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

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

             lowercase__      			= prepare_img()
             lowercase__      			= image_processor(images=__lowerCamelCase   ,						return_tensors='''pt''' ).to(__lowerCamelCase )

             # forward pass
             with torch.no_grad():
                    lowercase__      			= model(**__lowerCamelCase )

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

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

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








def       lowercase_		(		_A			:    int    ,					_A			:    list						):

      """simple docstring"""




      _enforce_args(_A    ,					_A						)
      if n == 0:
            return 0
      lowerCamelCase__		:				Union[str, Any]    =  float("-inf"						)
      for i in range(1    ,					n + 1						):
            lowerCamelCase__		:				int    =  max(
                _A    ,					prices[i - 1] + naive_cut_rod_recursive(n - i    ,					_A						)						)

      return max_revue

def       lowercase_		(		_A			:    int    ,					_A			:    list						):

      """simple docstring"""




      _enforce_args(_A    ,					_A						)
      lowerCamelCase__		:				int    =  [float("-inf"						) for _ in range(n + 1						)]
      return _top_down_cut_rod_recursive(_A    ,					_A    ,					_A						)

def       lowercase_		(		_A			:    int    ,					_A			:    list    ,					_A			:    list						):

      """simple docstring"""




      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            lowerCamelCase__		:				Dict    =  float("-inf"						)
            for i in range(1    ,					n + 1						):
                  lowerCamelCase__		:				int    =  max(
                      _A    ,					prices[i - 1] + _top_down_cut_rod_recursive(n - i    ,					_A    ,					_A						)    ,					)

            lowerCamelCase__		:				List[Any]    =  max_revenue

      return max_rev[n]

def       lowercase_		(		_A			:    int    ,					_A			:    list						):

      """simple docstring"""




      _enforce_args(_A    ,					_A						)

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      lowerCamelCase__		:				int    =  [float("-inf"						) for _ in range(n + 1						)]
      lowerCamelCase__		:				Optional[int]    =  0

      for i in range(1    ,					n + 1						):
            lowerCamelCase__		:				Union[str, Any]    =  max_rev[i]
            for j in range(1    ,					i + 1						):
                  lowerCamelCase__		:				Any    =  max(_A    ,					prices[j - 1] + max_rev[i - j]						)

            lowerCamelCase__		:				Any    =  max_revenue_i

      return max_rev[n]

def       lowercase_		(		_A			:    int    ,					_A			:    list						):

      """simple docstring"""




      if n < 0:
            lowerCamelCase__		:				Optional[int]    =  F"n must be greater than or equal to 0. Got n = {n}"
            raise ValueError(_A						)

      if n > len(_A						):
            lowerCamelCase__		:				Optional[int]    =  (
                "Each integral piece of rod must have a corresponding price. "
                F"Got n = {n} but length of prices = {len(_A						)}"
            )
            raise ValueError(_A						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  [6, 10, 12, 15, 20, 23]
      lowerCamelCase__		:				Dict    =  len(_A						)

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      lowerCamelCase__		:				int    =  36

      lowerCamelCase__		:				List[Any]    =  top_down_cut_rod(_A    ,					_A						)
      lowerCamelCase__		:				List[Any]    =  bottom_up_cut_rod(_A    ,					_A						)
      lowerCamelCase__		:				List[Any]    =  naive_cut_rod_recursive(_A    ,					_A						)

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
    main()


