

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 				A_					(						snake_case__   ):


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Dict:
      __lowerCAmelCase:  str        = 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     :						str     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						int=1_3     ,    UpperCAmelCase     :						Any=3_2     ,    UpperCAmelCase     :						List[str]=2     ,    UpperCAmelCase     :						Any=3     ,    UpperCAmelCase     :						List[str]=6_4_0     ,    UpperCAmelCase     :						str=4     ,    UpperCAmelCase     :						int="silu"     ,    UpperCAmelCase     :						Optional[int]=3     ,    UpperCAmelCase     :						int=3_2     ,    UpperCAmelCase     :						Union[str, Any]=0.1     ,    UpperCAmelCase     :						Any=0.1     ,    UpperCAmelCase     :						Any=0.1     ,    UpperCAmelCase     :						List[Any]=0.02     ,    UpperCAmelCase     :						int=True     ,    UpperCAmelCase     :						Any=True     ,    UpperCAmelCase     :						Union[str, Any]=1_0     ,    UpperCAmelCase     :						List[str]=None     ,    )				->      int:
      __lowerCAmelCase:  str        = parent
      __lowerCAmelCase:  Dict        = batch_size
      __lowerCAmelCase:  Union[str, Any]        = image_size
      __lowerCAmelCase:  List[str]        = patch_size
      __lowerCAmelCase:  int        = num_channels
      __lowerCAmelCase:  Optional[Any]        = last_hidden_size
      __lowerCAmelCase:  Union[str, Any]        = num_attention_heads
      __lowerCAmelCase:  int        = hidden_act
      __lowerCAmelCase:  str        = conv_kernel_size
      __lowerCAmelCase:  Optional[int]        = output_stride
      __lowerCAmelCase:  Optional[Any]        = hidden_dropout_prob
      __lowerCAmelCase:  Dict        = attention_probs_dropout_prob
      __lowerCAmelCase:  Dict        = classifier_dropout_prob
      __lowerCAmelCase:  Optional[int]        = use_labels
      __lowerCAmelCase:  Tuple        = is_training
      __lowerCAmelCase:  List[str]        = num_labels
      __lowerCAmelCase:  str        = initializer_range
      __lowerCAmelCase:  Optional[Any]        = scope


     def     UpperCAmelCase    (				self     :						Optional[Any]   )				->      List[str]:
      __lowerCAmelCase:  Any        = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      __lowerCAmelCase:  Tuple        = None
      __lowerCAmelCase:  int        = None
      if self.use_labels:
       __lowerCAmelCase:  int        = ids_tensor([self.batch_size]     ,    self.num_labels   )
       __lowerCAmelCase:  List[str]        = 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     UpperCAmelCase    (				self     :						Optional[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     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						Any     ,    UpperCAmelCase     :						Union[str, Any]   )				->      Tuple:
      __lowerCAmelCase:  str        = TFMobileViTModel(config=UpperCAmelCase   )
      __lowerCAmelCase:  List[str]        = model(UpperCAmelCase     ,    training=UpperCAmelCase   )
      __lowerCAmelCase:  Optional[int]        = 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     UpperCAmelCase    (				self     :						Any     ,    UpperCAmelCase     :						Union[str, Any]     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						Tuple   )				->      List[str]:
      __lowerCAmelCase:  Optional[Any]        = self.num_labels
      __lowerCAmelCase:  Tuple        = TFMobileViTForImageClassification(UpperCAmelCase   )
      __lowerCAmelCase:  Union[str, Any]        = model(UpperCAmelCase     ,    labels=UpperCAmelCase     ,    training=UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.num_labels)   )


     def     UpperCAmelCase    (				self     :						int     ,    UpperCAmelCase     :						Any     ,    UpperCAmelCase     :						List[Any]     ,    UpperCAmelCase     :						Optional[Any]     ,    UpperCAmelCase     :						Optional[Any]   )				->      str:
      __lowerCAmelCase:  Optional[Any]        = self.num_labels
      __lowerCAmelCase:  Any        = TFMobileViTForSemanticSegmentation(UpperCAmelCase   )
      __lowerCAmelCase:  str        = self.image_size // self.output_stride

      __lowerCAmelCase:  Tuple        = model(UpperCAmelCase     ,    training=UpperCAmelCase   )
      self.parent.assertEqual(
          result.logits.shape     ,    (self.batch_size, self.num_labels, expected_height, expected_width)   )

      __lowerCAmelCase:  Dict        = model(UpperCAmelCase     ,    labels=UpperCAmelCase     ,    training=UpperCAmelCase   )
      self.parent.assertEqual(
          result.logits.shape     ,    (self.batch_size, self.num_labels, expected_height, expected_width)   )


     def     UpperCAmelCase    (				self     :						Any   )				->      Union[str, Any]:
      __lowerCAmelCase:  Optional[int]        = self.prepare_config_and_inputs()
      __lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase:  int        = config_and_inputs
      __lowerCAmelCase:  Dict        = {'pixel_values': pixel_values}
      return config, inputs_dict







@require_tf
class 				A_					(						snake_case__				,      snake_case__				,      unittest.TestCase   ):
     _lowercase     :	List[str]        =      (
         (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
         if is_tf_available()
         else ()
     )
     _lowercase     :	Dict        =      (
         {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
         if is_tf_available()
         else {}
     )

     _lowercase     :	Optional[Any]        =      False
     _lowercase     :	Any        =      False
     _lowercase     :	Any        =      False
     _lowercase     :	Optional[int]        =      False
     _lowercase     :	Dict        =      False


     def     UpperCAmelCase    (				self     :						Optional[int]   )				->      Optional[int]:
      __lowerCAmelCase:  Dict        = TFMobileViTModelTester(self   )
      __lowerCAmelCase:  int        = TFMobileViTConfigTester(self     ,    config_class=UpperCAmelCase     ,    has_text_modality=UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						int   )				->      Optional[Any]:
      self.config_tester.run_common_tests()


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


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


     @unittest.skip(reason='MobileViT does not output attentions'   )
     def     UpperCAmelCase    (				self     :						int   )				->      str:
      pass


     def     UpperCAmelCase    (				self     :						str   )				->      Tuple:
      __lowerCAmelCase      ,				__lowerCAmelCase:  Union[str, Any]        = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:  Tuple        = model_class(UpperCAmelCase   )
       __lowerCAmelCase:  Tuple        = inspect.signature(model.call   )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowerCAmelCase:  List[Any]        = [*signature.parameters.keys()]

       __lowerCAmelCase:  Union[str, Any]        = ['pixel_values']
       self.assertListEqual(arg_names[:1]     ,    UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Any   )				->      Dict:
      __lowerCAmelCase:  Optional[int]        = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						str   )				->      int:
      def check_hidden_states_output(UpperCAmelCase     :						Optional[int]     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						Tuple   ):
       __lowerCAmelCase:  List[str]        = model_class(UpperCAmelCase   )

       __lowerCAmelCase:  int        = model(**self._prepare_for_class(UpperCAmelCase     ,    UpperCAmelCase   )   )

       __lowerCAmelCase:  Union[str, Any]        = outputs.hidden_states

       __lowerCAmelCase:  Dict        = 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.
       __lowerCAmelCase:  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   )

      __lowerCAmelCase      ,				__lowerCAmelCase:  int        = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:  Optional[Any]        = True
       check_hidden_states_output(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )

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

       check_hidden_states_output(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Any:
      __lowerCAmelCase:  Union[str, Any]        = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      Dict:
      __lowerCAmelCase:  str        = 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     UpperCAmelCase    (				self     :						List[str]   )				->      Dict:
      super().test_dataset_conversion()


     def     UpperCAmelCase    (				self     :						int     ,    UpperCAmelCase     :						Tuple     ,    UpperCAmelCase     :						Optional[Any]     ,    UpperCAmelCase     :						Union[str, Any]=2E-1     ,    UpperCAmelCase     :						str=2E-1   )				->      Any:
      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     UpperCAmelCase    (				self     :						Optional[int]   )				->      Optional[int]:
      __lowerCAmelCase      ,				__lowerCAmelCase:  Tuple        = 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":
        __lowerCAmelCase:  Union[str, Any]        = model_class(UpperCAmelCase   )
        if getattr(UpperCAmelCase     ,    'hf_compute_loss'     ,    UpperCAmelCase   ):
         super().test_keras_fit()


     def     UpperCAmelCase    (				self     :						Any   )				->      List[Any]:
      __lowerCAmelCase      ,				__lowerCAmelCase:  Tuple        = 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":
        __lowerCAmelCase:  str        = 5

       __lowerCAmelCase:  Any        = 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
        __lowerCAmelCase:  List[str]        = self._prepare_for_class(inputs_dict.copy()     ,    UpperCAmelCase     ,    return_labels=UpperCAmelCase   )
        __lowerCAmelCase:  List[Any]        = prepared_for_class[
            sorted(prepared_for_class.keys() - inputs_dict.keys()     ,    reverse=UpperCAmelCase   )[0]
        ]
        __lowerCAmelCase:  List[str]        = added_label.shape.as_list()[:1]

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

        __lowerCAmelCase:  int        = 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
        __lowerCAmelCase:  Any        = self._prepare_for_class(inputs_dict.copy()     ,    UpperCAmelCase     ,    return_labels=UpperCAmelCase   )
        __lowerCAmelCase:  Union[str, Any]        = {'input_ids', 'pixel_values', 'input_features'}
        __lowerCAmelCase:  List[Any]        = possible_input_names.intersection(set(UpperCAmelCase   )   ).pop()
        __lowerCAmelCase:  Dict        = prepared_for_class.pop(UpperCAmelCase   )
        if "labels" in prepared_for_class:
         __lowerCAmelCase:  Union[str, Any]        = prepared_for_class['labels'].numpy()
         if len(labels.shape   ) > 1 and labels.shape[1] != 1:
          # labels[0] = -100
          __lowerCAmelCase:  List[Any]        = tf.convert_to_tensor(UpperCAmelCase   )
          __lowerCAmelCase:  List[str]        = 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
        __lowerCAmelCase:  Union[str, Any]        = self._prepare_for_class(inputs_dict.copy()     ,    UpperCAmelCase     ,    return_labels=UpperCAmelCase   )
        __lowerCAmelCase:  int        = 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
        __lowerCAmelCase:  Union[str, Any]        = self._prepare_for_class(inputs_dict.copy()     ,    UpperCAmelCase     ,    return_labels=UpperCAmelCase   )

        # Get keys that were added with the _prepare_for_class function
        __lowerCAmelCase:  Tuple        = prepared_for_class.keys() - inputs_dict.keys()
        __lowerCAmelCase:  Any        = inspect.signature(model.call   ).parameters
        __lowerCAmelCase:  Tuple        = list(signature.keys()   )

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

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

        for index, value in sorted_tuple_index_mapping:
         __lowerCAmelCase:  Optional[int]        = prepared_for_class[value]

        __lowerCAmelCase:  List[str]        = tuple(UpperCAmelCase   )

        # Send to model
        __lowerCAmelCase:  Tuple        = model(tuple_input[:-1]   )[0]

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


     @slow
     def     UpperCAmelCase    (				self     :						Tuple   )				->      List[Any]:
      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCAmelCase:  Any        = TFMobileViTModel.from_pretrained(UpperCAmelCase   )
       self.assertIsNotNone(UpperCAmelCase   )







def _a					(			)       ->       List[str]:

 """simple docstring"""






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






@require_tf
class 				A_					(						unittest.TestCase   ):


     @slow
     def     UpperCAmelCase    (				self     :						Dict   )				->      str:
      __lowerCAmelCase:  Optional[int]        = TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'   )

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

      # forward pass
      __lowerCAmelCase:  Union[str, Any]        = model(**UpperCAmelCase     ,    training=UpperCAmelCase   )

      # verify the logits
      __lowerCAmelCase:  Tuple        = tf.TensorShape((1, 1_0_0_0)   )
      self.assertEqual(outputs.logits.shape     ,    UpperCAmelCase   )

      __lowerCAmelCase:  Optional[Any]        = tf.constant([-1.9364, -1.2327, -0.4653]   )

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


     @slow
     def     UpperCAmelCase    (				self     :						Dict   )				->      Tuple:
      # `from_pt` will be removed
      __lowerCAmelCase:  List[Any]        = TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'   )

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

      __lowerCAmelCase:  List[Any]        = prepare_img()
      __lowerCAmelCase:  Optional[int]        = image_processor(images=UpperCAmelCase     ,    return_tensors='tf'   )

      # forward pass
      __lowerCAmelCase:  List[str]        = model(inputs.pixel_values     ,    training=UpperCAmelCase   )
      __lowerCAmelCase:  Optional[Any]        = outputs.logits

      # verify the logits
      __lowerCAmelCase:  Tuple        = tf.TensorShape((1, 2_1, 3_2, 3_2)   )
      self.assertEqual(logits.shape     ,    UpperCAmelCase   )

      __lowerCAmelCase:  List[str]        = tf.constant(
          [
              [[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]],
          ]   )

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








import numpy as np







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]						)       ->       List[str]:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = np.exp(SCREAMING_SNAKE_CASE						)

 # Add up the all the exponentials
 __lowerCAmelCase:  Dict        = np.sum(SCREAMING_SNAKE_CASE						)

 # Divide every exponent by the sum of all exponents
 __lowerCAmelCase:  int        = exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
  print(softmax((0,)))





