




from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
   from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       Union[str, Any]     )->			int:
     _lowercase: int						     =    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      UpperCAmelCase						:






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






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

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

     _lowercase: Union[str, Any]						     =    self.get_config()

     return config, pixel_values, labels, pixel_labels






   def  _a						(	self   :       Union[str, Any]     )->			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  _a						(	self   :       Tuple						,					UpperCamelCase_   :       Optional[int]						,					UpperCamelCase_   :       Dict						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       List[Any]     )->			Tuple:
     _lowercase: Tuple						     =    TFMobileViTModel(config=UpperCamelCase_     )
     _lowercase: List[Any]						     =    model(UpperCamelCase_						,					training=UpperCamelCase_     )
     _lowercase: Dict						     =    self.image_size // self.output_stride
     self.parent.assertEqual(
         result.last_hidden_state.shape						,					(self.batch_size, self.last_hidden_size, expected_height, expected_width)     )






   def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       Dict     )->			Union[str, Any]:
     _lowercase: Dict						     =    self.num_labels
     _lowercase: Optional[Any]						     =    TFMobileViTForImageClassification(UpperCamelCase_     )
     _lowercase: Dict						     =    model(UpperCamelCase_						,					labels=UpperCamelCase_						,					training=UpperCamelCase_     )
     self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)     )






   def  _a						(	self   :       int						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       Dict						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Union[str, Any]     )->			Dict:
     _lowercase: Optional[Any]						     =    self.num_labels
     _lowercase: int						     =    TFMobileViTForSemanticSegmentation(UpperCamelCase_     )
     _lowercase: Union[str, Any]						     =    self.image_size // self.output_stride

     _lowercase: List[str]						     =    model(UpperCamelCase_						,					training=UpperCamelCase_     )
     self.parent.assertEqual(
         result.logits.shape						,					(self.batch_size, self.num_labels, expected_height, expected_width)     )

     _lowercase: List[Any]						     =    model(UpperCamelCase_						,					labels=UpperCamelCase_						,					training=UpperCamelCase_     )
     self.parent.assertEqual(
         result.logits.shape						,					(self.batch_size, self.num_labels, expected_height, expected_width)     )






   def  _a						(	self   :       Tuple     )->			Tuple:
     _lowercase: int						     =    self.prepare_config_and_inputs()
     _lowercase     ,					_lowercase     ,					_lowercase     ,					_lowercase: List[str]						     =    config_and_inputs
     _lowercase: int						     =    {'pixel_values': pixel_values}
     return config, inputs_dict

@require_tf
class      UpperCAmelCase						(			A   ,					A   ,					unittest.TestCase   ):
   __SCREAMING_SNAKE_CASE		:							Any							=					(
       (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
       if is_tf_available()
       else ()
   )
   __SCREAMING_SNAKE_CASE		:							str							=					(
       {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
       if is_tf_available()
       else {}
   )

   __SCREAMING_SNAKE_CASE		:							List[str]							=					False
   __SCREAMING_SNAKE_CASE		:							int							=					False
   __SCREAMING_SNAKE_CASE		:							int							=					False
   __SCREAMING_SNAKE_CASE		:							List[str]							=					False
   __SCREAMING_SNAKE_CASE		:							List[Any]							=					False






   def  _a						(	self   :       Optional[Any]     )->			List[str]:
     _lowercase: Union[str, Any]						     =    TFMobileViTModelTester(self     )
     _lowercase: Dict						     =    TFMobileViTConfigTester(self						,					config_class=UpperCamelCase_						,					has_text_modality=UpperCamelCase_     )






   def  _a						(	self   :       Union[str, Any]     )->			Optional[Any]:
     self.config_tester.run_common_tests()






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






   @unittest.skip(reason='MobileViT does not support input and output embeddings'     )
   def  _a						(	self   :       int     )->			Dict:
     pass






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






   def  _a						(	self   :       Union[str, Any]     )->			int:
     _lowercase     ,					_lowercase: Union[str, Any]						     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _lowercase: str						     =    model_class(UpperCamelCase_     )
       _lowercase: Any						     =    inspect.signature(model.call     )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       _lowercase: int						     =    [*signature.parameters.keys()]

       _lowercase: List[Any]						     =    ['pixel_values']
       self.assertListEqual(arg_names[:1]						,					UpperCamelCase_     )






   def  _a						(	self   :       Tuple     )->			str:
     _lowercase: str						     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCamelCase_     )






   def  _a						(	self   :       str     )->			List[Any]:
     def check_hidden_states_output(UpperCamelCase_   :       Tuple						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Optional[Any]     ):
       _lowercase: List[str]						     =    model_class(UpperCamelCase_     )

       _lowercase: str						     =    model(**self._prepare_for_class(UpperCamelCase_						,					UpperCamelCase_     )     )

       _lowercase: List[Any]						     =    outputs.hidden_states

       _lowercase: List[str]						     =    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.
       _lowercase: Any						     =    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     )

     _lowercase     ,					_lowercase: List[Any]						     =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

       check_hidden_states_output(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )






   def  _a						(	self   :       List[str]     )->			Optional[Any]:
     _lowercase: Optional[Any]						     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_     )






   def  _a						(	self   :       Optional[Any]     )->			str:
     _lowercase: Any						     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_     )






   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('GPU'     )     ) == 0						,					reason='TF does not support backprop for grouped convolutions on CPU.'						,					)
   def  _a						(	self   :       int     )->			List[Any]:
     super().test_dataset_conversion()






   def  _a						(	self   :       Dict						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       List[str]=2E-1						,					UpperCamelCase_   :       Union[str, Any]=2E-1     )->			Dict:
     self.assertTrue(np.allclose(UpperCamelCase_						,					UpperCamelCase_						,					atol=UpperCamelCase_						,					rtol=UpperCamelCase_     )     )






   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('GPU'     )     ) == 0						,					reason='TF does not support backprop for grouped convolutions on CPU.'						,					)
   @slow
   def  _a						(	self   :       Any     )->			List[Any]:
     _lowercase     ,					_lowercase: Any						     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
       if model_class.__name__ != "TFMobileViTModel":
         _lowercase: Dict						     =    model_class(UpperCamelCase_     )
         if getattr(UpperCamelCase_						,					'hf_compute_loss'						,					UpperCamelCase_     ):
           super().test_keras_fit()






   def  _a						(	self   :       Optional[int]     )->			List[Any]:
     _lowercase     ,					_lowercase: List[Any]						     =    self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
       # set an ignore index to correctly test the masked loss used in
       # `TFMobileViTForSemanticSegmentation`.
       if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
         _lowercase: List[str]						     =    5

       _lowercase: Tuple						     =    model_class(UpperCamelCase_     )
       if getattr(UpperCamelCase_						,					'hf_compute_loss'						,					UpperCamelCase_     ):
         # The number of elements in the loss should be the same as the number of elements in the label
         _lowercase: List[str]						     =    self._prepare_for_class(inputs_dict.copy()						,					UpperCamelCase_						,					return_labels=UpperCamelCase_     )
         _lowercase: Union[str, Any]						     =    prepared_for_class[
             sorted(prepared_for_class.keys() - inputs_dict.keys()						,					reverse=UpperCamelCase_     )[0]
         ]
         _lowercase: str						     =    added_label.shape.as_list()[:1]

         # Test that model correctly compute the loss with kwargs
         _lowercase: Dict						     =    self._prepare_for_class(inputs_dict.copy()						,					UpperCamelCase_						,					return_labels=UpperCamelCase_     )
         _lowercase: Optional[Any]						     =    {'input_ids', 'pixel_values', 'input_features'}
         _lowercase: List[Any]						     =    possible_input_names.intersection(set(UpperCamelCase_     )     ).pop()
         _lowercase: str						     =    prepared_for_class.pop(UpperCamelCase_     )

         _lowercase: List[str]						     =    model(UpperCamelCase_						,					**UpperCamelCase_     )[0]
         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]     )

         # Test that model correctly compute the loss when we mask some positions
         _lowercase: Union[str, Any]						     =    self._prepare_for_class(inputs_dict.copy()						,					UpperCamelCase_						,					return_labels=UpperCamelCase_     )
         _lowercase: Any						     =    {'input_ids', 'pixel_values', 'input_features'}
         _lowercase: Dict						     =    possible_input_names.intersection(set(UpperCamelCase_     )     ).pop()
         _lowercase: str						     =    prepared_for_class.pop(UpperCamelCase_     )
         if "labels" in prepared_for_class:
           _lowercase: List[Any]						     =    prepared_for_class['labels'].numpy()
           if len(labels.shape     ) > 1 and labels.shape[1] != 1:
             # labels[0] = -100
             _lowercase: Optional[int]						     =    tf.convert_to_tensor(UpperCamelCase_     )
             _lowercase: Optional[Any]						     =    model(UpperCamelCase_						,					**UpperCamelCase_     )[0]
             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]     )
             self.assertTrue(not np.any(np.isnan(loss.numpy()     )     )     )

                # Test that model correctly compute the loss with a dict
         _lowercase: Dict						     =    self._prepare_for_class(inputs_dict.copy()						,					UpperCamelCase_						,					return_labels=UpperCamelCase_     )
         _lowercase: Any						     =    model(UpperCamelCase_     )[0]
         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]     )

         # Test that model correctly compute the loss with a tuple
         _lowercase: Any						     =    self._prepare_for_class(inputs_dict.copy()						,					UpperCamelCase_						,					return_labels=UpperCamelCase_     )

         # Get keys that were added with the _prepare_for_class function
         _lowercase: Dict						     =    prepared_for_class.keys() - inputs_dict.keys()
         _lowercase: str						     =    inspect.signature(model.call     ).parameters
         _lowercase: int						     =    list(signature.keys()     )

         # Create a dictionary holding the location of the tensors in the tuple
         _lowercase: List[str]						     =    {0: input_name}
         for label_key in label_keys:
           _lowercase: List[Any]						     =    signature_names.index(UpperCamelCase_     )
           _lowercase: Optional[Any]						     =    label_key
         _lowercase: Tuple						     =    sorted(tuple_index_mapping.items()     )
         # Initialize a list with their default values, update the values and convert to a tuple
         _lowercase: int						     =    []

         for name in signature_names:
           if name != "kwargs":
             list_input.append(signature[name].default     )

         for index, value in sorted_tuple_index_mapping:
           _lowercase: Dict						     =    prepared_for_class[value]

         _lowercase: int						     =    tuple(UpperCamelCase_     )

         # Send to model
         _lowercase: Optional[int]						     =    model(tuple_input[:-1]     )[0]

         self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]     )






   @slow
   def  _a						(	self   :       List[Any]     )->			Optional[int]:
     for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase: List[str]						     =    TFMobileViTModel.from_pretrained(UpperCamelCase_     )
       self.assertIsNotNone(UpperCamelCase_     )


def 		_lowerCamelCase				(					)			->		str:





  """simple docstring"""





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





@require_tf
class      UpperCAmelCase						(			unittest.TestCase   ):






   @slow
   def  _a						(	self   :       str     )->			str:
     _lowercase: Dict						     =    TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'     )

     _lowercase: List[Any]						     =    MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'     )
     _lowercase: str						     =    prepare_img()
     _lowercase: Optional[int]						     =    image_processor(images=UpperCamelCase_						,					return_tensors='tf'     )

     # forward pass
     _lowercase: List[str]						     =    model(**UpperCamelCase_						,					training=UpperCamelCase_     )

     # verify the logits
     _lowercase: Tuple						     =    tf.TensorShape((1, 10_00)     )
     self.assertEqual(outputs.logits.shape						,					UpperCamelCase_     )

     _lowercase: Optional[Any]						     =    tf.constant([-1.9_364, -1.2_327, -0.4_653]     )

     tf.debugging.assert_near(outputs.logits[0, :3]						,					UpperCamelCase_						,					atol=1E-4						,					rtol=1E-04     )






   @slow
   def  _a						(	self   :       Union[str, Any]     )->			int:
     # `from_pt` will be removed
     _lowercase: int						     =    TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'     )

     _lowercase: List[str]						     =    MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'     )

     _lowercase: Dict						     =    prepare_img()
     _lowercase: str						     =    image_processor(images=UpperCamelCase_						,					return_tensors='tf'     )

     # forward pass
     _lowercase: Optional[int]						     =    model(inputs.pixel_values						,					training=UpperCamelCase_     )
     _lowercase: List[str]						     =    outputs.logits

     # verify the logits
     _lowercase: Dict						     =    tf.TensorShape((1, 21, 32, 32)     )
     self.assertEqual(logits.shape						,					UpperCamelCase_     )

     _lowercase: Union[str, Any]						     =    tf.constant(
         [
             [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
             [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
             [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
         ]     )

     tf.debugging.assert_near(logits[0, :3, :3, :3]						,					UpperCamelCase_						,					rtol=1E-4						,					atol=1E-4     )






from cva import destroyAllWindows, imread, imshow, waitKey


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase     ,					_lowercase: Tuple						     =    img.shape[0], img.shape[1]

  def to_grayscale(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							):
    return 0.2_126 * red + 0.587 * green + 0.114 * blue

  def normalize(UpperCamelCase							):
    return min(UpperCamelCase					,     255							)

  for i in range(UpperCamelCase							):
    for j in range(UpperCamelCase							):
      _lowercase: Any						     =    int(to_grayscale(*img[i][j]							)							)
      _lowercase: str						     =    [
          normalize(UpperCamelCase							),
          normalize(greyscale + factor							),
          normalize(greyscale + 2 * factor							),
      ]

  return img


if __name__ == "__main__":
   # read original image
   UpperCAmelCase__						:       Optional[Any]							=    {
       percentage: imread('image_data/lena.jpg', 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()
