
'''simple docstring'''
import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
				from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor


class       _lowerCAmelCase   (   __lowercase				):







							'''simple docstring'''

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







							'''simple docstring'''

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

							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

														_snake_case       =  self.get_config()

														return config, pixel_values, labels, pixel_labels

							def 	lowercase							(self  )					->       int:
														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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Union[str, Any]:
														_snake_case       =  MobileViTModel(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  )
														self.parent.assertEqual(
														    result.last_hidden_state.shape  ,  (
														        self.batch_size,
														        self.last_hidden_size,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )  ,  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[int]:
														_snake_case       =  self.num_labels
														_snake_case       =  MobileViTForImageClassification(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
														_snake_case       =  self.num_labels
														_snake_case       =  MobileViTForSemanticSegmentation(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  )
														self.parent.assertEqual(
														    result.logits.shape  ,  (
														        self.batch_size,
														        self.num_labels,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )  ,  )
														_snake_case       =  model(UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(
														    result.logits.shape  ,  (
														        self.batch_size,
														        self.num_labels,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )  ,  )





							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  self.prepare_config_and_inputs()
														_snake_case       =  config_and_inputs
														_snake_case       =  {"""pixel_values""": pixel_values}
														return config, inputs_dict


@require_torch
class       _lowerCAmelCase   (   __lowercase					,							__lowercase					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						(
							    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase_ 						=						(
							    {
							        '''feature-extraction''': MobileViTModel,
							        '''image-classification''': MobileViTForImageClassification,
							        '''image-segmentation''': MobileViTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       List[str]:
														_snake_case       =  MobileViTModelTester(self  )
														_snake_case       =  MobileViTConfigTester(self  ,  config_class=UpperCAmelCase  ,  has_text_modality=UpperCAmelCase  )

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

							@unittest.skip(reason="""MobileViT does not use inputs_embeds"""  )
							def 	lowercase							(self  )					->       List[str]:
														pass

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

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

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

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

																					_snake_case       =  ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]  ,  UpperCAmelCase  )

							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
							def 	lowercase							(self  )					->       List[Any]:
														pass

							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase  )

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

														def check_hidden_states_output(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  model_class(UpperCAmelCase  )
																					model.to(UpperCAmelCase  )
																					model.eval()

																					with torch.no_grad():
																												_snake_case       =  model(**self._prepare_for_class(UpperCAmelCase  ,  UpperCAmelCase  )  )

																					_snake_case       =  outputs.hidden_states

																					_snake_case       =  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.
																					_snake_case       =  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  )

														_snake_case       =  self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_snake_case       =  True
																					check_hidden_states_output(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )

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

																					check_hidden_states_output(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase  )





							@slow
							def 	lowercase							(self  )					->       List[Any]:

														for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_snake_case       =  MobileViTModel.from_pretrained(UpperCAmelCase  )
																					self.assertIsNotNone(UpperCAmelCase  )






def  __SCREAMING_SNAKE_CASE				(  ) ->    Any:
							_snake_case       =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image


@require_torch
@require_vision
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							@cached_property
							def 	lowercase							(self  )					->       Any:
														return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""  ) if is_vision_available() else None

							@slow
							def 	lowercase							(self  )					->       Any:
														_snake_case       =  MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""  ).to(UpperCAmelCase  )

														_snake_case       =  self.default_image_processor
														_snake_case       =  prepare_img()
														_snake_case       =  image_processor(images=UpperCAmelCase  ,  return_tensors="""pt"""  ).to(UpperCAmelCase  )

														# forward pass
														with torch.no_grad():
																					_snake_case       =  model(**UpperCAmelCase  )

														# verify the logits
														_snake_case       =  torch.Size((1, 1000)  )
														self.assertEqual(outputs.logits.shape  ,  UpperCAmelCase  )

														_snake_case       =  torch.tensor([-1.9364, -1.2327, -0.4653]  ).to(UpperCAmelCase  )

														self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,  UpperCAmelCase  ,  atol=1e-4  )  )

							@slow
							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )
														_snake_case       =  model.to(UpperCAmelCase  )

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

														_snake_case       =  prepare_img()
														_snake_case       =  image_processor(images=UpperCAmelCase  ,  return_tensors="""pt"""  ).to(UpperCAmelCase  )

														# forward pass
														with torch.no_grad():
																					_snake_case       =  model(**UpperCAmelCase  )
														_snake_case       =  outputs.logits

														# verify the logits
														_snake_case       =  torch.Size((1, 21, 32, 32)  )
														self.assertEqual(logits.shape  ,  UpperCAmelCase  )

														_snake_case       =  torch.tensor(
														    [
														        [[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]],
														    ]  ,  device=UpperCAmelCase  ,  )

														self.assertTrue(torch.allclose(logits[0, :3, :3, :3]  ,  UpperCAmelCase  ,  atol=1e-4  )  )





							@slow
							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )
														_snake_case       =  model.to(UpperCAmelCase  )

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

														_snake_case       =  prepare_img()
														_snake_case       =  image_processor(images=UpperCAmelCase  ,  return_tensors="""pt"""  ).to(UpperCAmelCase  )

														# forward pass
														with torch.no_grad():
																					_snake_case       =  model(**UpperCAmelCase  )

														_snake_case       =  outputs.logits.detach().cpu()

														_snake_case       =  image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase  ,  target_sizes=[(50, 60)]  )
														_snake_case       =  torch.Size((50, 60)  )
														self.assertEqual(segmentation[0].shape  ,  UpperCAmelCase  )

														_snake_case       =  image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase  )
														_snake_case       =  torch.Size((32, 32)  )
														self.assertEqual(segmentation[0].shape  ,  UpperCAmelCase  )

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase												=	{
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	['EfficientFormerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'EfficientFormerForImageClassification',
				    'EfficientFormerForImageClassificationWithTeacher',
				    'EfficientFormerModel',
				    'EfficientFormerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFEfficientFormerForImageClassification',
				    'TFEfficientFormerForImageClassificationWithTeacher',
				    'TFEfficientFormerModel',
				    'TFEfficientFormerPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_efficientformer import EfficientFormerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_efficientformer import (
								    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EfficientFormerForImageClassification,
								    EfficientFormerForImageClassificationWithTeacher,
								    EfficientFormerModel,
								    EfficientFormerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_efficientformer import (
								    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFEfficientFormerForImageClassification,
								    TFEfficientFormerForImageClassificationWithTeacher,
								    TFEfficientFormerModel,
								    TFEfficientFormerPreTrainedModel,
								)

else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)