




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						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.config_class(**self.inputs_dict				)
          self.parent.assertTrue(hasattr(snake_case ,       '''hidden_sizes'''				)				)
          self.parent.assertTrue(hasattr(snake_case ,       '''neck_hidden_sizes'''				)				)
          self.parent.assertTrue(hasattr(snake_case ,       '''num_attention_heads'''				)				)







class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: List[Any] ,       snake_case					: Union[str, Any] ,       snake_case					: int=13 ,       snake_case					: Any=32 ,       snake_case					: Optional[int]=2 ,       snake_case					: str=3 ,       snake_case					: Dict=640 ,       snake_case					: Optional[Any]=4 ,       snake_case					: Dict="silu" ,       snake_case					: List[Any]=3 ,       snake_case					: Optional[int]=32 ,       snake_case					: Dict=0.1 ,       snake_case					: Optional[Any]=0.1 ,       snake_case					: Union[str, Any]=0.1 ,       snake_case					: str=0.02 ,       snake_case					: List[Any]=True ,       snake_case					: str=True ,       snake_case					: Optional[int]=10 ,       snake_case					: List[Any]=None ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						image_size
          __UpperCamelCase  		=						patch_size
          __UpperCamelCase  		=						num_channels
          __UpperCamelCase  		=						last_hidden_size
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						conv_kernel_size
          __UpperCamelCase  		=						output_stride
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						classifier_dropout_prob
          __UpperCamelCase  		=						use_labels
          __UpperCamelCase  		=						is_training
          __UpperCamelCase  		=						num_labels
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						scope







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

          __UpperCamelCase  		=						self.get_config()

          return config, pixel_values, labels, pixel_labels







      def       snake_case	(     self					: Optional[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					: List[str] ,       snake_case					: Any ,       snake_case					: Tuple ,       snake_case					: List[Any] ,       snake_case					: Dict				):
          __UpperCamelCase  		=						MobileViTModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)
          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					: Union[str, Any] ,       snake_case					: Optional[Any] ,       snake_case					: Optional[int] ,       snake_case					: str ,       snake_case					: str				):
          __UpperCamelCase  		=						self.num_labels
          __UpperCamelCase  		=						MobileViTForImageClassification(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels)				)







      def       snake_case	(     self					: Any ,       snake_case					: Union[str, Any] ,       snake_case					: Optional[int] ,       snake_case					: Dict ,       snake_case					: Optional[Any]				):
          __UpperCamelCase  		=						self.num_labels
          __UpperCamelCase  		=						MobileViTForSemanticSegmentation(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case				)
          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  		=						model(snake_case ,       labels=snake_case				)
          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[int]				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						config_and_inputs
          __UpperCamelCase  		=						{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[Any]    =					(
          (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__			: Optional[int]    =					False
      lowerCAmelCase__			: str    =					False
      lowerCAmelCase__			: str    =					False
      lowerCAmelCase__			: str    =					False







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						MobileViTModelTester(self				)
          __UpperCamelCase  		=						MobileViTConfigTester(self ,       config_class=snake_case ,       has_text_modality=snake_case				)







      def       snake_case	(     self					: List[Any]				):
          self.config_tester.run_common_tests()







      @unittest.skip(reason='''MobileViT does not use inputs_embeds'''				)
      def       snake_case	(     self					: str				):
          pass







      @unittest.skip(reason='''MobileViT does not support input and output embeddings'''				)
      def       snake_case	(     self					: Optional[Any]				):
          pass







      @unittest.skip(reason='''MobileViT does not output attentions'''				)
      def       snake_case	(     self					: List[str]				):
          pass







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs_for_common()

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

              __UpperCamelCase  		=						['''pixel_values''']
              self.assertListEqual(arg_names[:1] ,       snake_case				)







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







      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case				)







      def       snake_case	(     self					: Any				):
          def check_hidden_states_output(snake_case					: Union[str, Any] ,       snake_case					: Dict ,       snake_case					: List[Any]				):
              __UpperCamelCase  		=						model_class(snake_case				)
              model.to(snake_case				)
              model.eval()

              with torch.no_grad():
                  __UpperCamelCase  		=						model(**self._prepare_for_class(snake_case ,       snake_case				)				)

              __UpperCamelCase  		=						outputs.hidden_states

              __UpperCamelCase  		=						5
              self.assertEqual(len(snake_case				) ,       snake_case				)

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

          for model_class in self.all_model_classes:
              __UpperCamelCase  		=						True
              check_hidden_states_output(snake_case ,       snake_case ,       snake_case				)

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

              check_hidden_states_output(snake_case ,       snake_case ,       snake_case				)







      def       snake_case	(     self					: List[str]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case				)







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*snake_case				)







      @slow
      def       snake_case	(     self					: Any				):
          for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						MobileViTModel.from_pretrained(snake_case				)
              self.assertIsNotNone(snake_case				)







def 					__SCREAMING_SNAKE_CASE   (  )       ->	str:

    """simple docstring"""


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


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

      """simple docstring"""

      @cached_property
      def       snake_case	(     self					: Optional[int]				):
          return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''				) if is_vision_available() else None







      @slow
      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''				).to(snake_case				)

          __UpperCamelCase  		=						self.default_image_processor
          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				).to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(**snake_case				)

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

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

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







      @slow
      def       snake_case	(     self					: str				):
          __UpperCamelCase  		=						MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''				)
          __UpperCamelCase  		=						model.to(snake_case				)

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

          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				).to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(**snake_case				)
          __UpperCamelCase  		=						outputs.logits

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

          __UpperCamelCase  		=						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]],
                  [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
                  [[-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=snake_case ,       )

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







      @slow
      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''				)
          __UpperCamelCase  		=						model.to(snake_case				)

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

          __UpperCamelCase  		=						prepare_img()
          __UpperCamelCase  		=						image_processor(images=snake_case ,       return_tensors='''pt'''				).to(snake_case				)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase  		=						model(**snake_case				)

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

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

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












def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_ = False      )       ->	str:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ):
        __UpperCamelCase  		=						F"Expected string as input, found {type(lowercase_      )}"
        raise ValueError(lowercase_      )
    if not isinstance(lowercase_					,     lowercase_      ):
        __UpperCamelCase  		=						F"Expected boolean as use_pascal parameter, found {type(lowercase_      )}"
        raise ValueError(lowercase_      )

    __UpperCamelCase  		=						input_str.split('''_'''      )

    __UpperCamelCase  		=						0 if use_pascal else 1

    __UpperCamelCase  		=						words[start_index:]

    __UpperCamelCase  		=						[word[0].upper() + word[1:] for word in words_to_capitalize]

    __UpperCamelCase  		=						'''''' if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]      )


if __name__ == "__main__":
 from doctest import testmod

 testmod()






