

'''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        lowercase    (	snake_case_			):


							def     _snake_case				(      self)				->					Optional[Any]:
								UpperCAmelCase_		:   int													=       self.config_class(**self.inputs_dict)
								self.parent.assertTrue(hasattr(lowerCamelCase_     ,      'hidden_sizes'))
								self.parent.assertTrue(hasattr(lowerCamelCase_     ,      'neck_hidden_sizes'))
								self.parent.assertTrue(hasattr(lowerCamelCase_     ,      'num_attention_heads'))



class        lowercase    :


							def __init__(      self     ,      _snake_case     ,      _snake_case=13     ,      _snake_case=32     ,      _snake_case=2     ,      _snake_case=3     ,      _snake_case=640     ,      _snake_case=4     ,      _snake_case="silu"     ,      _snake_case=3     ,      _snake_case=32     ,      _snake_case=0.1     ,      _snake_case=0.1     ,      _snake_case=0.1     ,      _snake_case=0.02     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case=10     ,      _snake_case=None     ,      )				->					int:
								UpperCAmelCase_		:   Any													=       parent
								UpperCAmelCase_		:   str													=       batch_size
								UpperCAmelCase_		:   Dict													=       image_size
								UpperCAmelCase_		:   Tuple													=       patch_size
								UpperCAmelCase_		:   List[str]													=       num_channels
								UpperCAmelCase_		:   List[Any]													=       last_hidden_size
								UpperCAmelCase_		:   Optional[Any]													=       num_attention_heads
								UpperCAmelCase_		:   Any													=       hidden_act
								UpperCAmelCase_		:   Dict													=       conv_kernel_size
								UpperCAmelCase_		:   List[Any]													=       output_stride
								UpperCAmelCase_		:   List[Any]													=       hidden_dropout_prob
								UpperCAmelCase_		:   List[str]													=       attention_probs_dropout_prob
								UpperCAmelCase_		:   Dict													=       classifier_dropout_prob
								UpperCAmelCase_		:   List[str]													=       use_labels
								UpperCAmelCase_		:   Union[str, Any]													=       is_training
								UpperCAmelCase_		:   Optional[int]													=       num_labels
								UpperCAmelCase_		:   Dict													=       initializer_range
								UpperCAmelCase_		:   int													=       scope


							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_		:   List[str]													=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCAmelCase_		:   List[Any]													=       None
								UpperCAmelCase_		:   Optional[Any]													=       None
								if self.use_labels:
									UpperCAmelCase_		:   Dict													=       ids_tensor([self.batch_size]     ,      self.num_labels)
									UpperCAmelCase_		:   List[Any]													=       ids_tensor([self.batch_size, self.image_size, self.image_size]     ,      self.num_labels)

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

								return config, pixel_values, labels, pixel_labels


							def     _snake_case				(      self)				->					Optional[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     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   str													=       TFMobileViTModel(config=lowerCamelCase_)
								UpperCAmelCase_		:   Union[str, Any]													=       model(lowerCamelCase_     ,      training=lowerCamelCase_)
								UpperCAmelCase_		:   str													=       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     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case)				->					int:
								UpperCAmelCase_		:   int													=       self.num_labels
								UpperCAmelCase_		:   Optional[int]													=       TFMobileViTForImageClassification(lowerCamelCase_)
								UpperCAmelCase_		:   Optional[Any]													=       model(lowerCamelCase_     ,      labels=lowerCamelCase_     ,      training=lowerCamelCase_)
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels))


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case)				->					Dict:
								UpperCAmelCase_		:   Union[str, Any]													=       self.num_labels
								UpperCAmelCase_		:   List[str]													=       TFMobileViTForSemanticSegmentation(lowerCamelCase_)
								UpperCAmelCase_		:   int													=       self.image_size // self.output_stride

								UpperCAmelCase_		:   Optional[Any]													=       model(lowerCamelCase_     ,      training=lowerCamelCase_)
								self.parent.assertEqual(
								    result.logits.shape     ,      (self.batch_size, self.num_labels, expected_height, expected_width))

								UpperCAmelCase_		:   str													=       model(lowerCamelCase_     ,      labels=lowerCamelCase_     ,      training=lowerCamelCase_)
								self.parent.assertEqual(
								    result.logits.shape     ,      (self.batch_size, self.num_labels, expected_height, expected_width))





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



@require_tf
class        lowercase    (	snake_case_,       snake_case_,       unittest.TestCase			):
							_lowerCamelCase		:   str= (
							    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
							    if is_tf_available()
							    else ()
							)
							_lowerCamelCase		:   Union[str, Any]= (
							    {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							_lowerCamelCase		:   List[Any]= False
							_lowerCamelCase		:   Any= False
							_lowerCamelCase		:   int= False
							_lowerCamelCase		:   Optional[int]= False
							_lowerCamelCase		:   str= False


							def     _snake_case				(      self)				->					List[str]:
								UpperCAmelCase_		:   str													=       TFMobileViTModelTester(self)
								UpperCAmelCase_		:   int													=       TFMobileViTConfigTester(self     ,      config_class=lowerCamelCase_     ,      has_text_modality=lowerCamelCase_)


							def     _snake_case				(      self)				->					List[Any]:
								self.config_tester.run_common_tests()


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


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


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


							def     _snake_case				(      self)				->					List[Any]:
								UpperCAmelCase_		:   Optional[Any]													=       self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCAmelCase_		:   List[str]													=       ['pixel_values']
									self.assertListEqual(arg_names[:1]     ,      lowerCamelCase_)


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   int													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase_)


							def     _snake_case				(      self)				->					Union[str, Any]:



								def check_hidden_states_output(_snake_case     ,      _snake_case     ,      _snake_case):
									UpperCAmelCase_		:   Tuple													=       model_class(lowerCamelCase_)

									UpperCAmelCase_		:   Union[str, Any]													=       model(**self._prepare_for_class(lowerCamelCase_     ,      lowerCamelCase_))

									UpperCAmelCase_		:   Optional[int]													=       outputs.hidden_states

									UpperCAmelCase_		:   str													=       5
									self.assertEqual(len(lowerCamelCase_)     ,      lowerCamelCase_)

									# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
									# with the width and height being successively divided by 2.
									UpperCAmelCase_		:   Dict													=       2
									for i in range(len(lowerCamelCase_)):
										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)

								UpperCAmelCase_		:   Optional[int]													=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									UpperCAmelCase_		:   Union[str, Any]													=       True
									check_hidden_states_output(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

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

									check_hidden_states_output(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)


							def     _snake_case				(      self)				->					Optional[Any]:
								UpperCAmelCase_		:   List[Any]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_)


							def     _snake_case				(      self)				->					Tuple:
								UpperCAmelCase_		:   str													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_)


							@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     _snake_case				(      self)				->					Dict:
								super().test_dataset_conversion()


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case=2e-1     ,      _snake_case=2e-1)				->					Optional[int]:
								self.assertTrue(np.allclose(lowerCamelCase_     ,      lowerCamelCase_     ,      atol=lowerCamelCase_     ,      rtol=lowerCamelCase_))


							@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     _snake_case				(      self)				->					Tuple:
								UpperCAmelCase_		:   int													=       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":
										UpperCAmelCase_		:   Optional[Any]													=       model_class(lowerCamelCase_)
										if getattr(lowerCamelCase_     ,      'hf_compute_loss'     ,      lowerCamelCase_):
											super().test_keras_fit()


							def     _snake_case				(      self)				->					Optional[Any]:
								UpperCAmelCase_		:   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":
										UpperCAmelCase_		:   Any													=       5

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

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

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

										# Get keys that were added with the _prepare_for_class function
										UpperCAmelCase_		:   str													=       prepared_for_class.keys() - inputs_dict.keys()
										UpperCAmelCase_		:   List[str]													=       inspect.signature(model.call).parameters
										UpperCAmelCase_		:   List[str]													=       list(signature.keys())

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

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

										for index, value in sorted_tuple_index_mapping:
											UpperCAmelCase_		:   Union[str, Any]													=       prepared_for_class[value]

										UpperCAmelCase_		:   str													=       tuple(lowerCamelCase_)

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

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





							@slow
							def     _snake_case				(      self)				->					int:



								for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_		:   Union[str, Any]													=       TFMobileViTModel.from_pretrained(lowerCamelCase_)
									self.assertIsNotNone(lowerCamelCase_)







def     SCREAMING_SNAKE_CASE(	)     ->     Optional[int]:
	UpperCAmelCase_		:   Tuple													=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







@require_tf
class        lowercase    (	unittest.TestCase			):


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

								UpperCAmelCase_		:   List[str]													=       MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small')
								UpperCAmelCase_		:   Dict													=       prepare_img()
								UpperCAmelCase_		:   Any													=       image_processor(images=lowerCamelCase_     ,      return_tensors='tf')

								# forward pass
								UpperCAmelCase_		:   Optional[Any]													=       model(**lowerCamelCase_     ,      training=lowerCamelCase_)

								# verify the logits
								UpperCAmelCase_		:   List[str]													=       tf.TensorShape((1, 1000))
								self.assertEqual(outputs.logits.shape     ,      lowerCamelCase_)

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

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





							@slow
							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   List[str]													=       TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

								UpperCAmelCase_		:   Union[str, Any]													=       MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

								UpperCAmelCase_		:   str													=       prepare_img()
								UpperCAmelCase_		:   int													=       image_processor(images=lowerCamelCase_     ,      return_tensors='tf')

								# forward pass
								UpperCAmelCase_		:   Optional[int]													=       model(inputs.pixel_values     ,      training=lowerCamelCase_)
								UpperCAmelCase_		:   List[Any]													=       outputs.logits

								# verify the logits
								UpperCAmelCase_		:   Any													=       tf.TensorShape((1, 21, 32, 32))
								self.assertEqual(logits.shape     ,      lowerCamelCase_)

								UpperCAmelCase_		:   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]     ,      lowerCamelCase_     ,      rtol=1e-4     ,      atol=1e-4)














"""simple docstring"""
from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		HfArgumentParser(_lowerCamelCase							)
 try:
  lowerCamelCase__	:     Dict								=		parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  lowerCamelCase__	:     Optional[int]								=		'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  lowerCamelCase__	:     Optional[Any]								=		' '.join(str(_lowerCamelCase							).split(' '							)[:-1]							)
  lowerCamelCase__	:     int								=		''
  lowerCamelCase__	:     Optional[int]								=		eval(str(_lowerCamelCase							).split(' '							)[-1]							)
  lowerCamelCase__	:     Tuple								=		[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]							)
   else:
    wrong_args.append(_lowerCamelCase							)
  if len(_lowerCamelCase							) > 0:
   lowerCamelCase__	:     Optional[int]								=		full_error_msg + begin_error_msg + str(_lowerCamelCase							)
  raise ValueError(_lowerCamelCase							)

 lowerCamelCase__	:     Any								=		PyTorchBenchmark(args=_lowerCamelCase							)
 benchmark.run()


if __name__ == "__main__":
      main()

