






'''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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Dict									=self.config_class(**self.inputs_dict      )
       self.parent.assertTrue(hasattr(__lowercase					,      '''hidden_sizes'''      )      )
       self.parent.assertTrue(hasattr(__lowercase					,      '''neck_hidden_sizes'''      )      )
       self.parent.assertTrue(hasattr(__lowercase					,      '''num_attention_heads'''      )      )






class      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							int					,      __lowercase	:							List[Any]					,      __lowercase	:							Optional[Any]=13					,      __lowercase	:							Any=32					,      __lowercase	:							Union[str, Any]=2					,      __lowercase	:							Any=3					,      __lowercase	:							Tuple=6_40					,      __lowercase	:							Optional[Any]=4					,      __lowercase	:							Optional[int]="silu"					,      __lowercase	:							List[str]=3					,      __lowercase	:							int=32					,      __lowercase	:							Tuple=0.1					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							Union[str, Any]=0.1					,      __lowercase	:							Optional[Any]=0.02					,      __lowercase	:							int=True					,      __lowercase	:							Optional[Any]=True					,      __lowercase	:							Optional[int]=10					,      __lowercase	:							Optional[Any]=None					,      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				str									=parent
       SCREAMING_SNAKE_CASE__     :				List[Any]									=batch_size
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=image_size
       SCREAMING_SNAKE_CASE__     :				List[Any]									=patch_size
       SCREAMING_SNAKE_CASE__     :				Tuple									=num_channels
       SCREAMING_SNAKE_CASE__     :				Tuple									=last_hidden_size
       SCREAMING_SNAKE_CASE__     :				Tuple									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_act
       SCREAMING_SNAKE_CASE__     :				Tuple									=conv_kernel_size
       SCREAMING_SNAKE_CASE__     :				Any									=output_stride
       SCREAMING_SNAKE_CASE__     :				str									=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__     :				List[str]									=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__     :				Any									=classifier_dropout_prob
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=use_labels
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=is_training
       SCREAMING_SNAKE_CASE__     :				List[str]									=num_labels
       SCREAMING_SNAKE_CASE__     :				Tuple									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Dict									=scope




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				str									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

       SCREAMING_SNAKE_CASE__     :				List[str]									=self.get_config()

       return config, pixel_values, labels, pixel_labels




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




 def      __magic_name__	(      self	:							Dict					,      __lowercase	:							List[Any]					,      __lowercase	:							str					,      __lowercase	:							Optional[Any]					,      __lowercase	:							str      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.num_labels
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=MobileViTForSemanticSegmentation(__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(__lowercase      )
       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__     :				str									=model(__lowercase					,      labels=__lowercase      )
       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      __magic_name__	(      self	:							List[Any]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__     :				List[Any]									=config_and_inputs
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							(
     (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 snake_case_  							=							(
     {
         """feature-extraction""": MobileViTModel,
         """image-classification""": MobileViTForImageClassification,
         """image-segmentation""": MobileViTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 snake_case_  							=							False
 snake_case_  							=							False
 snake_case_  							=							False
 snake_case_  							=							False




 def      __magic_name__	(      self	:							Tuple      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Tuple									=MobileViTModelTester(self      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=MobileViTConfigTester(self					,      config_class=__lowercase					,      has_text_modality=__lowercase      )




 def      __magic_name__	(      self	:							Any      )	->						Any:
       self.config_tester.run_common_tests()




 @unittest.skip(reason='''MobileViT does not use inputs_embeds'''      )
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Any:
       pass




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




 @unittest.skip(reason='''MobileViT does not output attentions'''      )
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
       pass




 def      __magic_name__	(      self	:							Any      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE__     :				Optional[int]									=['''pixel_values''']
             self.assertListEqual(arg_names[:1]					,      __lowercase      )




 @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
 def      __magic_name__	(      self	:							List[str]      )	->						int:
       pass




 def      __magic_name__	(      self	:							Dict      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowercase      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						List[Any]:
       def check_hidden_states_output(__lowercase	:							Optional[Any]					,      __lowercase	:							str					,      __lowercase	:							str      ):
             SCREAMING_SNAKE_CASE__     :				List[str]									=model_class(__lowercase      )
             model.to(__lowercase      )
             model.eval()

             with torch.no_grad():
                   SCREAMING_SNAKE_CASE__     :				Dict									=model(**self._prepare_for_class(__lowercase					,      __lowercase      )      )

             SCREAMING_SNAKE_CASE__     :				Any									=outputs.hidden_states

             SCREAMING_SNAKE_CASE__     :				Dict									=5
             self.assertEqual(len(__lowercase      )					,      __lowercase      )

             # 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__     :				Tuple									=2
             for i in range(len(__lowercase      )      ):
                   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__     :				List[str]									=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__     :				str									=True
             check_hidden_states_output(__lowercase					,      __lowercase					,      __lowercase      )

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

             check_hidden_states_output(__lowercase					,      __lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							Any      )	->						str:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowercase      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Any									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase      )




 @slow
 def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
       for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE__     :				Dict									=MobileViTModel.from_pretrained(__lowercase      )
             self.assertIsNotNone(__lowercase      )





def  _a(  ):




      '''simple docstring'''






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






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




 @cached_property
 def      __magic_name__	(      self	:							int      )	->						Union[str, Any]:
       return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''      ) if is_vision_available() else None




 @slow
 def      __magic_name__	(      self	:							Dict      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				int									=MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''      ).to(__lowercase      )

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

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

       # verify the logits
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.Size((1, 10_00)      )
       self.assertEqual(outputs.logits.shape					,      __lowercase      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.tensor([-1.9364, -1.2327, -0.4653]      ).to(__lowercase      )

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




 @slow
 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Tuple									=MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model.to(__lowercase      )

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

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

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__     :				List[str]									=model(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__     :				Any									=torch.Size((1, 21, 32, 32)      )
       self.assertEqual(logits.shape					,      __lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									=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=__lowercase					,      )

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




 @slow
 def      __magic_name__	(      self	:							int      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model.to(__lowercase      )

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

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

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

       SCREAMING_SNAKE_CASE__     :				int									=outputs.logits.detach().cpu()

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

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







'''simple docstring'''
from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray





def  _a(  UpperCamelCase__				:						NDArray[floataa],	UpperCamelCase__				:						NDArray[floataa],	UpperCamelCase__				:						list[int],	UpperCamelCase__				:						int,	):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=coefficient_matrix.shape
      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=constant_matrix.shape

      if rowsa != colsa:
            SCREAMING_SNAKE_CASE__     :				Any									=f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
            raise ValueError(UpperCamelCase__							)

      if colsa != 1:
            SCREAMING_SNAKE_CASE__     :				str									=f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
            raise ValueError(UpperCamelCase__							)

      if rowsa != rowsa:
            SCREAMING_SNAKE_CASE__     :				str									=(
                '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
                f"received {rowsa}x{colsa} and {rowsa}x{colsa}"
            )
            raise ValueError(UpperCamelCase__							)

      if len(UpperCamelCase__							) != rowsa:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=(
                '''Number of initial values must be equal to number of rows in coefficient '''
                f"matrix but received {len(UpperCamelCase__							)} and {rowsa}"
            )
            raise ValueError(UpperCamelCase__							)

      if iterations <= 0:
            raise ValueError('''Iterations must be at least 1'''							)

      SCREAMING_SNAKE_CASE__     :				NDArray[floataa]									=np.concatenate(
          (coefficient_matrix, constant_matrix),	axis=1							)

      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Any									=table.shape

      strictly_diagonally_dominant(UpperCamelCase__							)

      # Iterates the whole matrix for given number of times
      for _ in range(UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				List[str]									=[]
            for row in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=0
                  for col in range(UpperCamelCase__							):
                        if col == row:
                              SCREAMING_SNAKE_CASE__     :				int									=table[row][col]
                        elif col == cols - 1:
                              SCREAMING_SNAKE_CASE__     :				Any									=table[row][col]
                        else:
                              temp += (-1) * table[row][col] * init_val[col]
                  SCREAMING_SNAKE_CASE__     :				int									=(temp + val) / denom
                  new_val.append(UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=new_val

      return [float(UpperCamelCase__							) for i in new_val]





def  _a(  UpperCamelCase__				:						NDArray[floataa]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[int]									=table.shape

      SCREAMING_SNAKE_CASE__     :				Any									=True

      for i in range(0,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				int									=0
            for j in range(0,	cols - 1							):
                  if i == j:
                        continue
                  else:
                        total += table[i][j]

            if table[i][i] <= total:
                  raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''							)

      return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
  import doctest

  doctest.testmod()