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    snake_case       (   a_    ):




  def 						__lowercase(   self  :						Tuple					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		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    snake_case       :




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		parent
       SCREAMING_SNAKE_CASE__					:					str      =		batch_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		image_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		patch_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_channels
       SCREAMING_SNAKE_CASE__					:					List[str]      =		last_hidden_size
       SCREAMING_SNAKE_CASE__					:					Dict      =		num_attention_heads
       SCREAMING_SNAKE_CASE__					:					Any      =		hidden_act
       SCREAMING_SNAKE_CASE__					:					Dict      =		conv_kernel_size
       SCREAMING_SNAKE_CASE__					:					List[str]      =		output_stride
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Dict      =		classifier_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Tuple      =		use_labels
       SCREAMING_SNAKE_CASE__					:					Tuple      =		is_training
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_labels
       SCREAMING_SNAKE_CASE__					:					List[str]      =		initializer_range
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		scope




  def 						__lowercase(   self  :						Union[str, Any]					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_config()

       return config, pixel_values, labels, pixel_labels




  def 						__lowercase(   self  :						List[Any]					)->							List[Any]:




       """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  :						Any  ,			a_  :						List[Any]  ,			a_  :						Any  ,			a_  :						Tuple  ,			a_  :						Optional[Any]					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		MobileViTModel(config=lowerCAmelCase_					)
       model.to(lowerCAmelCase_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		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 						__lowercase(   self  :						List[str]  ,			a_  :						List[str]  ,			a_  :						Optional[int]  ,			a_  :						List[Any]  ,			a_  :						Any					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		self.num_labels
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		MobileViTForImageClassification(lowerCAmelCase_					)
       model.to(lowerCAmelCase_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					Dict      =		model(lowerCAmelCase_  ,			labels=lowerCAmelCase_					)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_labels)					)




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						Optional[Any]  ,			a_  :						Optional[Any]  ,			a_  :						Optional[int]  ,			a_  :						int					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		self.num_labels
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		MobileViTForSemanticSegmentation(lowerCAmelCase_					)
       model.to(lowerCAmelCase_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					str      =		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,
           )  ,			)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		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 						__lowercase(   self  :						Optional[int]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		config_and_inputs
       SCREAMING_SNAKE_CASE__					:					Any      =		{'pixel_values': pixel_values}
       return config, inputs_dict




@require_torch
class    snake_case       (   a_ ,    a_ ,    unittest.TestCase    ):
  lowercase_								=  (
      (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  lowercase_								=  (
      {
          '''feature-extraction''': MobileViTModel,
          '''image-classification''': MobileViTForImageClassification,
          '''image-segmentation''': MobileViTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  lowercase_								=  False
  lowercase_								=  False
  lowercase_								=  False
  lowercase_								=  False




  def 						__lowercase(   self  :						Union[str, Any]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		MobileViTModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		MobileViTConfigTester(self  ,			config_class=lowerCAmelCase_  ,			has_text_modality=lowerCAmelCase_					)




  def 						__lowercase(   self  :						Any					)->							str:




       """simple docstring"""
       self.config_tester.run_common_tests()




  @unittest.skip(reason='MobileViT does not use inputs_embeds'					)
  def 						__lowercase(   self  :						int					)->							Any:




       """simple docstring"""
       pass




  @unittest.skip(reason='MobileViT does not support input and output embeddings'					)
  def 						__lowercase(   self  :						List[str]					)->							List[Any]:




       """simple docstring"""
       pass




  @unittest.skip(reason='MobileViT does not output attentions'					)
  def 						__lowercase(   self  :						List[str]					)->							Optional[int]:




       """simple docstring"""
       pass




  def 						__lowercase(   self  :						Optional[int]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		['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 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       pass




  def 						__lowercase(   self  :						str					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase_					)




  def 						__lowercase(   self  :						Optional[Any]					)->							str:




       """simple docstring"""
       def check_hidden_states_output(a_  :						Optional[int]  ,			a_  :						int  ,			a_  :						List[str]					):
            SCREAMING_SNAKE_CASE__					:					Any      =		model_class(lowerCAmelCase_					)
            model.to(lowerCAmelCase_					)
            model.eval()

            with torch.no_grad():
                 SCREAMING_SNAKE_CASE__					:					Any      =		model(**self._prepare_for_class(lowerCAmelCase_  ,			lowerCAmelCase_					)					)

            SCREAMING_SNAKE_CASE__					:					List[str]      =		outputs.hidden_states

            SCREAMING_SNAKE_CASE__					:					str      =		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.
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		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					)

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		True
            check_hidden_states_output(lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_					)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		True

            check_hidden_states_output(lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_					)




  def 						__lowercase(   self  :						Tuple					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_					)




  @slow
  def 						__lowercase(   self  :						Union[str, Any]					)->							Optional[Any]:




       """simple docstring"""
       for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__					:					Any      =		MobileViTModel.from_pretrained(lowerCAmelCase_					)
            self.assertIsNotNone(lowerCAmelCase_					)







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[str]      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
     return image







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




  @cached_property
  def 						__lowercase(   self  :						Optional[Any]					)->							Tuple:




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




  @slow
  def 						__lowercase(   self  :						int					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'					).to(lowerCAmelCase_					)

       SCREAMING_SNAKE_CASE__					:					int      =		self.default_image_processor
       SCREAMING_SNAKE_CASE__					:					Dict      =		prepare_img()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		image_processor(images=lowerCAmelCase_  ,			return_tensors='pt'					).to(lowerCAmelCase_					)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					str      =		model(**lowerCAmelCase_					)

       # verify the logits
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.Size((1, 1000)					)
       self.assertEqual(outputs.logits.shape  ,			lowerCAmelCase_					)

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

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




  @slow
  def 						__lowercase(   self  :						Union[str, Any]					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		model.to(lowerCAmelCase_					)

       SCREAMING_SNAKE_CASE__					:					str      =		MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		prepare_img()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		image_processor(images=lowerCAmelCase_  ,			return_tensors='pt'					).to(lowerCAmelCase_					)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model(**lowerCAmelCase_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.Size((1, 21, 32, 32)					)
       self.assertEqual(logits.shape  ,			lowerCAmelCase_					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		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=lowerCAmelCase_  ,			)

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




  @slow
  def 						__lowercase(   self  :						Tuple					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'					)
       SCREAMING_SNAKE_CASE__					:					int      =		model.to(lowerCAmelCase_					)

       SCREAMING_SNAKE_CASE__					:					int      =		MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'					)

       SCREAMING_SNAKE_CASE__					:					str      =		prepare_img()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		image_processor(images=lowerCAmelCase_  ,			return_tensors='pt'					).to(lowerCAmelCase_					)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					List[str]      =		model(**lowerCAmelCase_					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		outputs.logits.detach().cpu()

       SCREAMING_SNAKE_CASE__					:					int      =		image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_  ,			target_sizes=[(50, 60)]					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.Size((50, 60)					)
       self.assertEqual(segmentation[0].shape  ,			lowerCAmelCase_					)

       SCREAMING_SNAKE_CASE__					:					str      =		image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.Size((32, 32)					)
       self.assertEqual(segmentation[0].shape  ,			lowerCAmelCase_					)







'''simple docstring'''





import numpy as np

from transformers import Pipeline






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




			__A=	np.max(_SCREAMING_SNAKE_CASE,axis=-1,keepdims=_SCREAMING_SNAKE_CASE )
			__A=	np.exp(outputs - maxes )
			return shifted_exp / shifted_exp.sum(axis=-1,keepdims=_SCREAMING_SNAKE_CASE )






class 						a__					(					a_   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    **lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						__A=	{}
						if "second_text" in kwargs:
									__A=	kwargs['second_text']
						return preprocess_kwargs, {}, {}
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]=None	)      ->		int:
						return self.tokenizer(lowerCAmelCase_			,    text_pair=lowerCAmelCase_			,    return_tensors=self.framework	)
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Tuple:
						return self.model(**lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict	)      ->		Optional[int]:
						__A=	model_outputs.logits[0].numpy()
						__A=	softmax(lowerCAmelCase_	)

						__A=	np.argmax(lowerCAmelCase_	)
						__A=	self.model.config.idalabel[best_class]
						__A=	probabilities[best_class].item()
						__A=	logits.tolist()
						return {"label": label, "score": score, "logits": logits}



