

'''simple docstring'''



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 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   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 		lowerCAmelCase__     :



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   parent
      __SCREAMING_SNAKE_CASE						     =   batch_size
      __SCREAMING_SNAKE_CASE						     =   image_size
      __SCREAMING_SNAKE_CASE						     =   patch_size
      __SCREAMING_SNAKE_CASE						     =   num_channels
      __SCREAMING_SNAKE_CASE						     =   last_hidden_size
      __SCREAMING_SNAKE_CASE						     =   num_attention_heads
      __SCREAMING_SNAKE_CASE						     =   hidden_act
      __SCREAMING_SNAKE_CASE						     =   conv_kernel_size
      __SCREAMING_SNAKE_CASE						     =   output_stride
      __SCREAMING_SNAKE_CASE						     =   hidden_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   classifier_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   use_labels
      __SCREAMING_SNAKE_CASE						     =   is_training
      __SCREAMING_SNAKE_CASE						     =   num_labels
      __SCREAMING_SNAKE_CASE						     =   initializer_range
      __SCREAMING_SNAKE_CASE						     =   scope
 def    UpperCAmelCase__	(	self      : int    )						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

      __SCREAMING_SNAKE_CASE						     =   self.get_config()

      return config, pixel_values, labels, pixel_labels
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Union[str, 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    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : Any    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   TFMobileViTModel(config=__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							training=__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   __SCREAMING_SNAKE_CASE						     =   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      : Tuple      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Any    )						->				Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.num_labels
      __SCREAMING_SNAKE_CASE						     =   TFMobileViTForImageClassification(__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							labels=__SCREAMING_SNAKE_CASE      ,							training=__SCREAMING_SNAKE_CASE    )
      self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)    )
 def    UpperCAmelCase__	(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Any      ,							__SCREAMING_SNAKE_CASE      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.num_labels
      __SCREAMING_SNAKE_CASE						     =   TFMobileViTForSemanticSegmentation(__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   __SCREAMING_SNAKE_CASE						     =   self.image_size // self.output_stride

      __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							training=__SCREAMING_SNAKE_CASE    )
      self.parent.assertEqual(
          result.logits.shape      ,							(self.batch_size, self.num_labels, expected_height, expected_width)    )

      __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							labels=__SCREAMING_SNAKE_CASE      ,							training=__SCREAMING_SNAKE_CASE    )
      self.parent.assertEqual(
          result.logits.shape      ,							(self.batch_size, self.num_labels, expected_height, expected_width)    )


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   config_and_inputs
      __SCREAMING_SNAKE_CASE						     =   {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_tf
class 		lowerCAmelCase__     (   a				,      a				,      unittest.TestCase      ):



 """simple docstring"""



 lowerCAmelCase__						      =   (
     (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
     if is_tf_available()
     else ()
 )
 lowerCAmelCase__						      =   (
     {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
     if is_tf_available()
     else {}
 )

 lowerCAmelCase__						      =   False
 lowerCAmelCase__						      =   False
 lowerCAmelCase__						      =   False
 lowerCAmelCase__						      =   False
 lowerCAmelCase__						      =   False
 def    UpperCAmelCase__	(	self      : str    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   TFMobileViTModelTester(self    )
      __SCREAMING_SNAKE_CASE						     =   TFMobileViTConfigTester(self      ,							config_class=__SCREAMING_SNAKE_CASE      ,							has_text_modality=__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				Union[str, Any]:


      """simple docstring"""




      self.config_tester.run_common_tests()
 @unittest.skip(reason="""MobileViT does not use inputs_embeds"""    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Optional[Any]:


      """simple docstring"""




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


      """simple docstring"""




      pass
 @unittest.skip(reason="""MobileViT does not output attentions"""    )
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				Dict:


      """simple docstring"""




      pass
 def    UpperCAmelCase__	(	self      : Any    )						->				Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs_for_common()

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

           __SCREAMING_SNAKE_CASE						     =   ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]      ,							__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Tuple    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      def check_hidden_states_output(__SCREAMING_SNAKE_CASE      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Any    ):
           __SCREAMING_SNAKE_CASE						     =   model_class(__SCREAMING_SNAKE_CASE    )

           __SCREAMING_SNAKE_CASE						     =   model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )    )

           __SCREAMING_SNAKE_CASE						     =   outputs.hidden_states

           __SCREAMING_SNAKE_CASE						     =   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.
           __SCREAMING_SNAKE_CASE						     =   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    )

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           __SCREAMING_SNAKE_CASE						     =   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"]
           __SCREAMING_SNAKE_CASE						     =   True

           check_hidden_states_output(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : str    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE    )
 @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      : int    )						->				str:


      """simple docstring"""




      super().test_dataset_conversion()
 def    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=2E-1      ,							__SCREAMING_SNAKE_CASE      : Tuple=2E-1    )						->				List[Any]:


      """simple docstring"""




      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							atol=__SCREAMING_SNAKE_CASE      ,							rtol=__SCREAMING_SNAKE_CASE    )    )
 @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      : Dict    )						->				int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   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":
                __SCREAMING_SNAKE_CASE						     =   model_class(__SCREAMING_SNAKE_CASE    )
                if getattr(__SCREAMING_SNAKE_CASE      ,							"""hf_compute_loss"""      ,							__SCREAMING_SNAKE_CASE    ):
                     super().test_keras_fit()
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   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":
                __SCREAMING_SNAKE_CASE						     =   5

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

                # Test that model correctly compute the loss with kwargs
                __SCREAMING_SNAKE_CASE						     =   self._prepare_for_class(inputs_dict.copy()      ,							__SCREAMING_SNAKE_CASE      ,							return_labels=__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   {"""input_ids""", """pixel_values""", """input_features"""}
                __SCREAMING_SNAKE_CASE						     =   possible_input_names.intersection(set(__SCREAMING_SNAKE_CASE    )    ).pop()
                __SCREAMING_SNAKE_CASE						     =   prepared_for_class.pop(__SCREAMING_SNAKE_CASE    )

                __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )[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
                __SCREAMING_SNAKE_CASE						     =   self._prepare_for_class(inputs_dict.copy()      ,							__SCREAMING_SNAKE_CASE      ,							return_labels=__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   {"""input_ids""", """pixel_values""", """input_features"""}
                __SCREAMING_SNAKE_CASE						     =   possible_input_names.intersection(set(__SCREAMING_SNAKE_CASE    )    ).pop()
                __SCREAMING_SNAKE_CASE						     =   prepared_for_class.pop(__SCREAMING_SNAKE_CASE    )
                if "labels" in prepared_for_class:
                     __SCREAMING_SNAKE_CASE						     =   prepared_for_class["""labels"""].numpy()
                     if len(labels.shape    ) > 1 and labels.shape[1] != 1:
                          # labels[0] = -100
                          __SCREAMING_SNAKE_CASE						     =   tf.convert_to_tensor(__SCREAMING_SNAKE_CASE    )
                          __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )[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
                __SCREAMING_SNAKE_CASE						     =   self._prepare_for_class(inputs_dict.copy()      ,							__SCREAMING_SNAKE_CASE      ,							return_labels=__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE    )[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
                __SCREAMING_SNAKE_CASE						     =   self._prepare_for_class(inputs_dict.copy()      ,							__SCREAMING_SNAKE_CASE      ,							return_labels=__SCREAMING_SNAKE_CASE    )

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

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

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

                for index, value in sorted_tuple_index_mapping:
                     __SCREAMING_SNAKE_CASE						     =   prepared_for_class[value]

                __SCREAMING_SNAKE_CASE						     =   tuple(__SCREAMING_SNAKE_CASE    )

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

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


 @slow
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				List[Any]:


      """simple docstring"""




      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE						     =   TFMobileViTModel.from_pretrained(__SCREAMING_SNAKE_CASE    )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )







def      a__ (						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     return image







@require_tf
class 		lowerCAmelCase__     (   unittest.TestCase      ):



 """simple docstring"""



 @slow
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""    )

      __SCREAMING_SNAKE_CASE						     =   MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""    )
      __SCREAMING_SNAKE_CASE						     =   prepare_img()
      __SCREAMING_SNAKE_CASE						     =   image_processor(images=__SCREAMING_SNAKE_CASE      ,							return_tensors="""tf"""    )

      # forward pass
      __SCREAMING_SNAKE_CASE						     =   model(**__SCREAMING_SNAKE_CASE      ,							training=__SCREAMING_SNAKE_CASE    )

      # verify the logits
      __SCREAMING_SNAKE_CASE						     =   tf.TensorShape((1, 1_000)    )
      self.assertEqual(outputs.logits.shape      ,							__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   tf.constant([-1.9364, -1.2327, -0.4653]    )

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


 @slow
 def    UpperCAmelCase__	(	self      : List[str]    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""    )

      __SCREAMING_SNAKE_CASE						     =   MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""    )

      __SCREAMING_SNAKE_CASE						     =   prepare_img()
      __SCREAMING_SNAKE_CASE						     =   image_processor(images=__SCREAMING_SNAKE_CASE      ,							return_tensors="""tf"""    )

      # forward pass
      __SCREAMING_SNAKE_CASE						     =   model(inputs.pixel_values      ,							training=__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   outputs.logits

      # verify the logits
      __SCREAMING_SNAKE_CASE						     =   tf.TensorShape((1, 21, 32, 32)    )
      self.assertEqual(logits.shape      ,							__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   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]      ,							__SCREAMING_SNAKE_CASE      ,							rtol=1E-4      ,							atol=1E-4    )









'''simple docstring'''



from __future__ import annotations

from collections.abc import Callable







def      a__ (						a__   ,					a__   ,					a__   ,					a__ = 1_00   ,					):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   x_start
     __SCREAMING_SNAKE_CASE						     =   fnc(a__		)
     __SCREAMING_SNAKE_CASE						     =   0.0

     for _ in range(a__		):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          __SCREAMING_SNAKE_CASE						     =   (x_end - x_start) / steps + xa
          __SCREAMING_SNAKE_CASE						     =   fnc(a__		)
          area += abs(fxa + fxa		) * (xa - xa) / 2

          # Increment step
          __SCREAMING_SNAKE_CASE						     =   xa
          __SCREAMING_SNAKE_CASE						     =   fxa
     return area


if __name__ == "__main__":







  def      a__ (						a__		):




       """simple docstring"""


       return x**3

  print('f(x) = x^3')
  print('The area between the curve, x = -10, x = 10 and the x axis is:')
  UpperCAmelCase				: Optional[Any]         =  1_0
  while i <= 1_0_0_0_0_0:
    UpperCAmelCase				: Union[str, Any]         =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 1_0






