


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  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				Any     =	self.config_class(**self.inputs_dict							)
        self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,	"""hidden_sizes"""							)							)
        self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,	"""neck_hidden_sizes"""							)							)
        self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,	"""num_attention_heads"""							)							)



class        a					:





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Union[str, Any]     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

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

        return config, pixel_values, labels, pixel_labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        return MobileViTConfig(
            image_size=self.image_size    ,	patch_size=self.patch_size    ,	num_channels=self.num_channels    ,	num_attention_heads=self.num_attention_heads    ,	hidden_act=self.hidden_act    ,	conv_kernel_size=self.conv_kernel_size    ,	output_stride=self.output_stride    ,	hidden_dropout_prob=self.hidden_dropout_prob    ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,	classifier_dropout_prob=self.classifier_dropout_prob    ,	initializer_range=self.initializer_range    ,	)





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				List[Any]     =	self.num_labels
        __lowerCamelCase:				Optional[int]     =	MobileViTForSemanticSegmentation(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_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,
            )    ,	)
        __lowerCamelCase:				List[Any]     =	model(SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Optional[Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	config_and_inputs
        __lowerCamelCase:				Dict     =	{"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Tuple						      =	(
         (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__ :  Dict						      =	(
         {
             "feature-extraction": MobileViTModel,
             "image-classification": MobileViTForImageClassification,
             "image-segmentation": MobileViTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     UpperCAmelCase__ :  Optional[int]						      =	False
     UpperCAmelCase__ :  Any						      =	False
     UpperCAmelCase__ :  Optional[int]						      =	False
     UpperCAmelCase__ :  Optional[Any]						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				int     =	MobileViTModelTester(self							)
        __lowerCamelCase:				Optional[Any]     =	MobileViTConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	has_text_modality=SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        self.config_tester.run_common_tests()





     @unittest.skip(reason="""MobileViT does not use inputs_embeds"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        pass





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





     @unittest.skip(reason="""MobileViT does not output attentions"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase   ,     __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs_for_common()

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

           __lowerCamelCase:				List[Any]     =	["""pixel_values"""]
           self.assertListEqual(arg_names[:1]    ,	SCREAMING_SNAKE_CASE_							)





     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				str     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Any							):
           __lowerCamelCase:				List[Any]     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()

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

           __lowerCamelCase:				List[Any]     =	outputs.hidden_states

           __lowerCamelCase:				Optional[int]     =	5
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_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.
           __lowerCamelCase:				List[Any]     =	2
           for i in range(len(SCREAMING_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							)

        __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Optional[int]     =	True
           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

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

           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				Dict     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				Optional[Any]     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				Tuple     =	MobileViTModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)



def     __lowerCAmelCase						(							)       ->							str:
   __lowerCamelCase:				Any     =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







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





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





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				Any     =	MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	self.default_image_processor
        __lowerCamelCase:				List[str]     =	prepare_img()
        __lowerCamelCase:				Tuple     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

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

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

        __lowerCamelCase:				List[Any]     =	torch.tensor([-1.9364, -1.2327, -0.4653]							).to(SCREAMING_SNAKE_CASE_							)

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





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				List[Any]     =	MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""							)
        __lowerCamelCase:				int     =	model.to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[Any]     =	MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""							)

        __lowerCamelCase:				Any     =	prepare_img()
        __lowerCamelCase:				List[str]     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

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

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

        __lowerCamelCase:				str     =	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=SCREAMING_SNAKE_CASE_    ,	)

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







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				List[str]     =	MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""							)
        __lowerCamelCase:				int     =	model.to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""							)

        __lowerCamelCase:				Optional[int]     =	prepare_img()
        __lowerCamelCase:				Dict     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

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

        __lowerCamelCase:				List[str]     =	outputs.logits.detach().cpu()

        __lowerCamelCase:				Dict     =	image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_    ,	target_sizes=[(50, 60)]							)
        __lowerCamelCase:				Optional[Any]     =	torch.Size((50, 60)							)
        self.assertEqual(segmentation[0].shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	torch.Size((32, 32)							)
        self.assertEqual(segmentation[0].shape    ,	SCREAMING_SNAKE_CASE_							)






import math



def     __lowerCAmelCase						(							snake_case	:    int )       ->							bool:
   __lowerCamelCase:				Dict     =	math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )

   return exponent == int(snake_case )



def     __lowerCAmelCase						(							snake_case	:    float = 1 / 12345 )       ->							int:
   __lowerCamelCase:				str     =	0
   __lowerCamelCase:				Optional[Any]     =	0

   __lowerCamelCase:				str     =	3
   while True:
      __lowerCamelCase:				int     =	(integer**2 - 1) / 4
      # if candidate is an integer, then there is a partition for k
      if partition_candidate == int(snake_case ):
         __lowerCamelCase:				Union[str, Any]     =	int(snake_case )
         total_partitions += 1
         if check_partition_perfect(snake_case ):
            perfect_partitions += 1
      if perfect_partitions > 0:
         if perfect_partitions / total_partitions < max_proportion:
            return int(snake_case )
      integer += 1


if __name__ == "__main__":
   print(F"""{solution() = }""")


