






"""simple docstring"""



import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter

class 			a	(  TensorFormatter[Mapping, np.ndarray, Mapping]      ):







    def __init__(     self				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      super().__init__(features=_lowerCamelCase			)
      lowercase				       =			np_array_kwargs







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if isinstance(_lowerCamelCase				,						_lowerCamelCase			):
        if column and all(
            isinstance(_lowerCamelCase				,						np.ndarray			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
          return np.stack(_lowerCamelCase			)
        else:
          # don't use np.array(column, dtype=object)
          # since it fails in certain cases
          # see https://stackoverflow.com/q/51005699
          lowercase				       =			np.empty(len(_lowerCamelCase			)				,						dtype=_lowerCamelCase			)
          lowercase				       =			column
          return out
      return column







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if isinstance(_lowerCamelCase				,						(str, bytes, type(_lowerCamelCase			))			):
        return value
      elif isinstance(_lowerCamelCase				,						(np.character, np.ndarray)			) and np.issubdtype(value.dtype				,						np.character			):
        return value
      elif isinstance(_lowerCamelCase				,						np.number			):
        return value

      lowercase				       =			{}

      if isinstance(_lowerCamelCase				,						np.ndarray			) and np.issubdtype(value.dtype				,						np.integer			):
        lowercase				       =			{'dtype': np.intaa}
      elif isinstance(_lowerCamelCase				,						np.ndarray			) and np.issubdtype(value.dtype				,						np.floating			):
        lowercase				       =			{'dtype': np.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(_lowerCamelCase				,						PIL.Image.Image			):
          return np.asarray(_lowerCamelCase				,						**self.np_array_kwargs			)

      return np.asarray(_lowerCamelCase				,						**{**default_dtype, **self.np_array_kwargs}			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
        import torch

        if isinstance(_lowerCamelCase				,						torch.Tensor			):
          return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
      if hasattr(_lowerCamelCase				,						'__array__'			) and not isinstance(_lowerCamelCase				,						(np.ndarray, np.character, np.number)			):
        lowercase				       =			data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_lowerCamelCase				,						np.ndarray			):
        if data_struct.dtype == object:
          return self._consolidate([self.recursive_tensorize(_lowerCamelCase			) for substruct in data_struct]			)
      if isinstance(_lowerCamelCase				,						(list, tuple)			):
        return self._consolidate([self.recursive_tensorize(_lowerCamelCase			) for substruct in data_struct]			)
      return self._tensorize(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      return map_nested(self._recursive_tensorize				,						_lowerCamelCase				,						map_list=_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.numpy_arrow_extractor().extract_row(_lowerCamelCase			)
      lowercase				       =			self.python_features_decoder.decode_row(_lowerCamelCase			)
      return self.recursive_tensorize(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.numpy_arrow_extractor().extract_column(_lowerCamelCase			)
      lowercase				       =			self.python_features_decoder.decode_column(_lowerCamelCase				,						pa_table.column_names[0]			)
      lowercase				       =			self.recursive_tensorize(_lowerCamelCase			)
      lowercase				       =			self._consolidate(_lowerCamelCase			)
      return column







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.numpy_arrow_extractor().extract_batch(_lowerCamelCase			)
      lowercase				       =			self.python_features_decoder.decode_batch(_lowerCamelCase			)
      lowercase				       =			self.recursive_tensorize(_lowerCamelCase			)
      for column_name in batch:
        lowercase				       =			self._consolidate(batch[column_name]			)
      return batch








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							):




							'''simple docstring'''



							def  _UpperCAmelCase  (       self							:						Tuple      )      ->       int:
										SCREAMING_SNAKE_CASE     =       self.config_class(**self.inputs_dict      )
										self.parent.assertTrue(hasattr(a							,  """hidden_sizes"""      )      )
										self.parent.assertTrue(hasattr(a							,  """neck_hidden_sizes"""      )      )
										self.parent.assertTrue(hasattr(a							,  """num_attention_heads"""      )      )





class       UpperCAmelCase_   :




							'''simple docstring'''



							def __init__(       self							:						Optional[Any]							,  a							:						Dict							,  a							:						Optional[int]=13							,  a							:						Optional[int]=32							,  a							:						List[str]=2							,  a							:						Optional[int]=3							,  a							:						str=640							,  a							:						str=4							,  a							:						Optional[int]="silu"							,  a							:						Union[str, Any]=3							,  a							:						Optional[int]=32							,  a							:						Optional[Any]=0.1							,  a							:						Optional[int]=0.1							,  a							:						int=0.1							,  a							:						List[str]=0.02							,  a							:						Tuple=True							,  a							:						Optional[Any]=True							,  a							:						List[Any]=10							,  a							:						List[Any]=None							,  )      ->       List[str]:
										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							:						Tuple      )      ->       List[Any]:
										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							:						List[Any]      )      ->       Union[str, 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							:						List[str]							,  a							:						Union[str, Any]							,  a							:						Tuple							,  a							:						Optional[int]							,  a							:						str      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       TFMobileViTModel(config=a      )
										SCREAMING_SNAKE_CASE     =       model(a							,  training=a      )
										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							:						Any							,  a							:						Dict							,  a							:						Optional[Any]							,  a							:						List[Any]							,  a							:						List[str]      )      ->       Any:
										SCREAMING_SNAKE_CASE     =       self.num_labels
										SCREAMING_SNAKE_CASE     =       TFMobileViTForImageClassification(a      )
										SCREAMING_SNAKE_CASE     =       model(a							,  labels=a							,  training=a      )
										self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)      )





							def  _UpperCAmelCase  (       self							:						Dict							,  a							:						List[str]							,  a							:						Union[str, Any]							,  a							:						List[Any]							,  a							:						Union[str, Any]      )      ->       Optional[int]:
										SCREAMING_SNAKE_CASE     =       self.num_labels
										SCREAMING_SNAKE_CASE     =       TFMobileViTForSemanticSegmentation(a      )
										SCREAMING_SNAKE_CASE     =       SCREAMING_SNAKE_CASE     =       self.image_size // self.output_stride

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

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







							def  _UpperCAmelCase  (       self							:						Any      )      ->       Optional[int]:
										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       UpperCAmelCase_   ( A		,   A		,   unittest.TestCase							):




							'''simple docstring'''



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

							a__    	=  False
							a__    	=  False
							a__    	=  False
							a__    	=  False
							a__    	=  False





							def  _UpperCAmelCase  (       self							:						str      )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       TFMobileViTModelTester(self      )
										SCREAMING_SNAKE_CASE     =       TFMobileViTConfigTester(self							,  config_class=a							,  has_text_modality=a      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       Dict:
										self.config_tester.run_common_tests()





							@unittest.skip(reason="""MobileViT does not use inputs_embeds"""      )
							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Tuple:
										pass





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





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





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       List[Any]:
										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(a      )
													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]							,  a      )





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a      )





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Union[str, Any]:
										def check_hidden_states_output(a							:						Dict							,  a							:						Union[str, Any]							,  a							:						Optional[Any]      ):
													SCREAMING_SNAKE_CASE     =       model_class(a      )

													SCREAMING_SNAKE_CASE     =       model(**self._prepare_for_class(a							,  a      )      )

													SCREAMING_SNAKE_CASE     =       outputs.hidden_states

													SCREAMING_SNAKE_CASE     =       5
													self.assertEqual(len(a      )							,  a      )

													# 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(a      )      ):
																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(a							,  a							,  a      )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE     =       True

													check_hidden_states_output(a							,  a							,  a      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*a      )





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*a      )





							@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							:						Optional[int]      )      ->       Any:
										super().test_dataset_conversion()





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





							@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]      )      ->       str:
										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(a      )
																if getattr(a							,  """hf_compute_loss"""							,  a      ):
																			super().test_keras_fit()





							def  _UpperCAmelCase  (       self							:						int      )      ->       Optional[Any]:
										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(a      )
													if getattr(a							,  """hf_compute_loss"""							,  a      ):
																# 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()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       prepared_for_class[
																    sorted(prepared_for_class.keys() - inputs_dict.keys()							,  reverse=a      )[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()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       {"""input_ids""", """pixel_values""", """input_features"""}
																SCREAMING_SNAKE_CASE     =       possible_input_names.intersection(set(a      )      ).pop()
																SCREAMING_SNAKE_CASE     =       prepared_for_class.pop(a      )

																SCREAMING_SNAKE_CASE     =       model(a							,  **a      )[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()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       {"""input_ids""", """pixel_values""", """input_features"""}
																SCREAMING_SNAKE_CASE     =       possible_input_names.intersection(set(a      )      ).pop()
																SCREAMING_SNAKE_CASE     =       prepared_for_class.pop(a      )
																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(a      )
																						SCREAMING_SNAKE_CASE     =       model(a							,  **a      )[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()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       model(a      )[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()							,  a							,  return_labels=a      )

																# 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(a      )
																			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(a      )

																# 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							:						str      )      ->       List[Any]:
										for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE     =       TFMobileViTModel.from_pretrained(a      )
													self.assertIsNotNone(a      )





def        lowerCamelCase_     (     ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
			return image




@require_tf
class       UpperCAmelCase_   ( unittest.TestCase							):




							'''simple docstring'''



							@slow
							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Dict:
										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=a							,  return_tensors="""tf"""      )

										# forward pass
										SCREAMING_SNAKE_CASE     =       model(**a							,  training=a      )

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

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

										tf.debugging.assert_near(outputs.logits[0, :3]							,  a							,  atol=1E-4							,  rtol=1E-0_4      )







							@slow
							def  _UpperCAmelCase  (       self							:						int      )      ->       Tuple:
										# `from_pt` will be removed
										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=a							,  return_tensors="""tf"""      )

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

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

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






