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 					A							(    UpperCAmelCase_					):


   def       lowercase_				(self					:						List[str]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.config_class(**self.inputs_dict		)
         self.parent.assertTrue(hasattr(__UpperCAmelCase	,  "hidden_sizes"		)		)
         self.parent.assertTrue(hasattr(__UpperCAmelCase	,  "neck_hidden_sizes"		)		)
         self.parent.assertTrue(hasattr(__UpperCAmelCase	,  "num_attention_heads"		)		)





class 					A							:


   def __init__(self					:						List[str]	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						List[Any]=1_3	,  __UpperCAmelCase					:						List[Any]=3_2	,  __UpperCAmelCase					:						Optional[Any]=2	,  __UpperCAmelCase					:						Dict=3	,  __UpperCAmelCase					:						List[Any]=6_4_0	,  __UpperCAmelCase					:						List[str]=4	,  __UpperCAmelCase					:						Dict="silu"	,  __UpperCAmelCase					:						List[Any]=3	,  __UpperCAmelCase					:						Optional[int]=3_2	,  __UpperCAmelCase					:						Union[str, Any]=0.1	,  __UpperCAmelCase					:						Optional[int]=0.1	,  __UpperCAmelCase					:						List[Any]=0.1	,  __UpperCAmelCase					:						Dict=0.02	,  __UpperCAmelCase					:						List[Any]=True	,  __UpperCAmelCase					:						Dict=True	,  __UpperCAmelCase					:						List[Any]=1_0	,  __UpperCAmelCase					:						str=None	,  )						->    Optional[int]:
         """simple docstring"""



         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       lowercase_				(self					:						str		)						->    int:
         """simple docstring"""



         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       lowercase_				(self					:						Tuple		)						->    Dict:
         """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       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Tuple		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  MobileViTModel(config=__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.eval()
         UpperCAmelCase__								=  model(__UpperCAmelCase		)
         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       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Optional[int]		)						->    Union[str, Any]:
         """simple docstring"""



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


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Any		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.num_labels
         UpperCAmelCase__								=  MobileViTForSemanticSegmentation(__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         model.eval()
         UpperCAmelCase__								=  model(__UpperCAmelCase		)
         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(__UpperCAmelCase	,  labels=__UpperCAmelCase		)
         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       lowercase_				(self					:						Optional[int]		)						->    List[str]:
         """simple docstring"""



         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 					A							(    UpperCAmelCase_		,    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			List[Any]              =      (
       (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase			:			str              =      (
       {
           'feature-extraction': MobileViTModel,
           'image-classification': MobileViTForImageClassification,
           'image-segmentation': MobileViTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __UpperCAmelCase			:			Optional[int]              =      False
   __UpperCAmelCase			:			Optional[Any]              =      False
   __UpperCAmelCase			:			Optional[int]              =      False
   __UpperCAmelCase			:			Tuple              =      False


   def       lowercase_				(self					:						List[str]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  MobileViTModelTester(self		)
         UpperCAmelCase__								=  MobileViTConfigTester(self	,  config_class=__UpperCAmelCase	,  has_text_modality=__UpperCAmelCase		)


   def       lowercase_				(self					:						Tuple		)						->    Optional[Any]:
         """simple docstring"""



         self.config_tester.run_common_tests()


   @unittest.skip(reason="MobileViT does not use inputs_embeds"		)
   def       lowercase_				(self					:						Dict		)						->    Optional[int]:
         """simple docstring"""



         pass


   @unittest.skip(reason="MobileViT does not support input and output embeddings"		)
   def       lowercase_				(self					:						Union[str, Any]		)						->    List[str]:
         """simple docstring"""



         pass


   @unittest.skip(reason="MobileViT does not output attentions"		)
   def       lowercase_				(self					:						List[str]		)						->    Any:
         """simple docstring"""



         pass


   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               UpperCAmelCase__								=  model_class(__UpperCAmelCase		)
               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]	,  __UpperCAmelCase		)


   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
   def       lowercase_				(self					:						str		)						->    int:
         """simple docstring"""



         pass


   def       lowercase_				(self					:						Any		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase		)


   def       lowercase_				(self					:						Union[str, Any]		)						->    Any:
         """simple docstring"""



         def check_hidden_states_output(__UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						int		):
               UpperCAmelCase__								=  model_class(__UpperCAmelCase		)
               model.to(__UpperCAmelCase		)
               model.eval()

               with torch.no_grad():
                     UpperCAmelCase__								=  model(**self._prepare_for_class(__UpperCAmelCase	,  __UpperCAmelCase		)		)

               UpperCAmelCase__								=  outputs.hidden_states

               UpperCAmelCase__								=  5
               self.assertEqual(len(__UpperCAmelCase		)	,  __UpperCAmelCase		)

               # 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(__UpperCAmelCase		)		):
                     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(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)

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

               check_hidden_states_output(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Union[str, Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase		)


   def       lowercase_				(self					:						Tuple		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase		)





   @slow
   def       lowercase_				(self					:						Dict		)						->    List[str]:
         """simple docstring"""



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




def        lowerCAmelCase_			(					)					->    List[str]:







      '''simple docstring'''


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







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


   @cached_property
   def       lowercase_				(self					:						Optional[int]		)						->    str:
         """simple docstring"""



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


   @slow
   def       lowercase_				(self					:						int		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"		).to(__UpperCAmelCase		)

         UpperCAmelCase__								=  self.default_image_processor
         UpperCAmelCase__								=  prepare_img()
         UpperCAmelCase__								=  image_processor(images=__UpperCAmelCase	,  return_tensors="pt"		).to(__UpperCAmelCase		)

         # forward pass
         with torch.no_grad():
               UpperCAmelCase__								=  model(**__UpperCAmelCase		)

         # verify the logits
         UpperCAmelCase__								=  torch.Size((1, 1_0_0_0)		)
         self.assertEqual(outputs.logits.shape	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  torch.tensor([-1.9364, -1.2327, -0.4653]		).to(__UpperCAmelCase		)

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


   @slow
   def       lowercase_				(self					:						Union[str, Any]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)
         UpperCAmelCase__								=  model.to(__UpperCAmelCase		)

         UpperCAmelCase__								=  MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)

         UpperCAmelCase__								=  prepare_img()
         UpperCAmelCase__								=  image_processor(images=__UpperCAmelCase	,  return_tensors="pt"		).to(__UpperCAmelCase		)

         # forward pass
         with torch.no_grad():
               UpperCAmelCase__								=  model(**__UpperCAmelCase		)
         UpperCAmelCase__								=  outputs.logits

         # verify the logits
         UpperCAmelCase__								=  torch.Size((1, 2_1, 3_2, 3_2)		)
         self.assertEqual(logits.shape	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  torch.tensor(
             [
                 [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                 [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                 [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
             ]	,  device=__UpperCAmelCase	,  )

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





   @slow
   def       lowercase_				(self					:						List[Any]		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)
         UpperCAmelCase__								=  model.to(__UpperCAmelCase		)

         UpperCAmelCase__								=  MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)

         UpperCAmelCase__								=  prepare_img()
         UpperCAmelCase__								=  image_processor(images=__UpperCAmelCase	,  return_tensors="pt"		).to(__UpperCAmelCase		)

         # forward pass
         with torch.no_grad():
               UpperCAmelCase__								=  model(**__UpperCAmelCase		)

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

         UpperCAmelCase__								=  image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase	,  target_sizes=[(5_0, 6_0)]		)
         UpperCAmelCase__								=  torch.Size((5_0, 6_0)		)
         self.assertEqual(segmentation[0].shape	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.Size((3_2, 3_2)		)
         self.assertEqual(segmentation[0].shape	,  __UpperCAmelCase		)




from __future__ import annotations




def        lowerCAmelCase_			(					__A							)					->    list[int]:







      '''simple docstring'''


      if len(__A							) == 0:
            return array

      UpperCAmelCase__		,				UpperCAmelCase__								=  min(__A							), max(__A							)

      # Compute the variables
      UpperCAmelCase__								=  _max - _min + 1
      UpperCAmelCase__		,				UpperCAmelCase__								=  [0] * holes_range, [0] * holes_range

      # Make the sorting.
      for i in array:
            UpperCAmelCase__								=  i - _min
            UpperCAmelCase__								=  i
            holes_repeat[index] += 1

      # Makes the array back by replacing the numbers.
      UpperCAmelCase__								=  0
      for i in range(__A							):
            while holes_repeat[i] > 0:
                  UpperCAmelCase__								=  holes[i]
                  index += 1
                  holes_repeat[i] -= 1

    # Returns the sorted array.
      return array


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      UpperCamelCase__    							= input('Enter numbers separated by comma:\n')
      UpperCamelCase__    							= [int(x) for x in user_input.split(',')]
      print(pigeon_sort(unsorted))



