





'''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       a_  (       snake_case_ ):

    '''simple docstring'''


    def     snake_case_(	self )	->     Dict:
        _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       a_  :

    '''simple docstring'''


    def __init__(	self				,	A				,	A=13				,	A=32				,	A=2				,	A=3				,	A=640				,	A=4				,	A="silu"				,	A=3				,	A=32				,	A=0.1				,	A=0.1				,	A=0.1				,	A=0.02				,	A=True				,	A=True				,	A=10				,	A=None				,	)	->     List[Any]:
        _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     snake_case_(	self )	->     List[str]:
        _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     snake_case_(	self )	->     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				,	A				,	A				,	A				,	A )	->     Optional[Any]:
        _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     snake_case_(	self				,	A				,	A				,	A				,	A )	->     Dict:
        _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     snake_case_(	self				,	A				,	A				,	A				,	A )	->     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     snake_case_(	self )	->     List[str]:
        _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       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):

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

    UpperCamelCase									=				False
    UpperCamelCase									=				False
    UpperCamelCase									=				False
    UpperCamelCase									=				False
    UpperCamelCase									=				False


    def     snake_case_(	self )	->     List[str]:
        _SCREAMING_SNAKE_CASE             =    TFMobileViTModelTester(self )
        _SCREAMING_SNAKE_CASE             =    TFMobileViTConfigTester(self				,	config_class=A				,	has_text_modality=A )


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


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


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


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


    def     snake_case_(	self )	->     Union[str, 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     snake_case_(	self )	->     List[Any]:
        _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A )


    def     snake_case_(	self )	->     Optional[Any]:
        def check_hidden_states_output(A				,	A				,	A ):
            _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     snake_case_(	self )	->     Optional[int]:
        _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A )


    def     snake_case_(	self )	->     List[Any]:
        _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     snake_case_(	self )	->     Any:
        super().test_dataset_conversion()


    def     snake_case_(	self				,	A				,	A				,	A=2e-1				,	A=2e-1 )	->     List[str]:
        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     snake_case_(	self )	->     Dict:
        _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     snake_case_(	self )	->     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     snake_case_(	self )	->     List[str]:
        for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE             =    TFMobileViTModel.from_pretrained(A )
            self.assertIsNotNone(A )





def lowerCamelCase		(  )		->Optional[int]:
    _SCREAMING_SNAKE_CASE             =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
    return image






@require_tf
class       a_  (       unittest.TestCase ):

    '''simple docstring'''


    @slow
    def     snake_case_(	self )	->     Optional[Any]:
        _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, 1000) )
        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-04 )





    @slow
    def     snake_case_(	self )	->     Union[str, Any]:
        # `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 )










'''simple docstring'''

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
      from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import GLPNImageProcessor


class       a_  (       snake_case_ ):

    '''simple docstring'''


    def     snake_case_(	self )	->     int:
        _SCREAMING_SNAKE_CASE             =    self.config_class(**self.inputs_dict )
        self.parent.assertTrue(hasattr(A				,	"""hidden_sizes""" ) )
        self.parent.assertTrue(hasattr(A				,	"""num_attention_heads""" ) )
        self.parent.assertTrue(hasattr(A				,	"""num_encoder_blocks""" ) )




class       a_  :

    '''simple docstring'''


    def __init__(	self				,	A				,	A=13				,	A=64				,	A=3				,	A=4				,	A=[2, 2, 2, 2]				,	A=[8, 4, 2, 1]				,	A=[16, 32, 64, 128]				,	A=[1, 4, 8, 16]				,	A=[1, 2, 4, 8]				,	A=True				,	A=True				,	A="gelu"				,	A=0.1				,	A=0.1				,	A=0.02				,	A=16				,	A=3				,	A=None				,	)	->     List[str]:
        _SCREAMING_SNAKE_CASE             =    parent
        _SCREAMING_SNAKE_CASE             =    batch_size
        _SCREAMING_SNAKE_CASE             =    image_size
        _SCREAMING_SNAKE_CASE             =    num_channels
        _SCREAMING_SNAKE_CASE             =    num_encoder_blocks
        _SCREAMING_SNAKE_CASE             =    sr_ratios
        _SCREAMING_SNAKE_CASE             =    depths
        _SCREAMING_SNAKE_CASE             =    hidden_sizes
        _SCREAMING_SNAKE_CASE             =    downsampling_rates
        _SCREAMING_SNAKE_CASE             =    num_attention_heads
        _SCREAMING_SNAKE_CASE             =    is_training
        _SCREAMING_SNAKE_CASE             =    use_labels
        _SCREAMING_SNAKE_CASE             =    hidden_act
        _SCREAMING_SNAKE_CASE             =    hidden_dropout_prob
        _SCREAMING_SNAKE_CASE             =    attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE             =    initializer_range
        _SCREAMING_SNAKE_CASE             =    decoder_hidden_size
        _SCREAMING_SNAKE_CASE             =    num_labels
        _SCREAMING_SNAKE_CASE             =    scope


    def     snake_case_(	self )	->     Optional[int]:
        _SCREAMING_SNAKE_CASE             =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        _SCREAMING_SNAKE_CASE             =    None
        if self.use_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


    def     snake_case_(	self )	->     Optional[int]:
        return GLPNConfig(
            image_size=self.image_size				,	num_channels=self.num_channels				,	num_encoder_blocks=self.num_encoder_blocks				,	depths=self.depths				,	hidden_sizes=self.hidden_sizes				,	num_attention_heads=self.num_attention_heads				,	hidden_act=self.hidden_act				,	hidden_dropout_prob=self.hidden_dropout_prob				,	attention_probs_dropout_prob=self.attention_probs_dropout_prob				,	initializer_range=self.initializer_range				,	decoder_hidden_size=self.decoder_hidden_size				,	)


    def     snake_case_(	self				,	A				,	A				,	A )	->     str:
        _SCREAMING_SNAKE_CASE             =    GLPNModel(config=A )
        model.to(A )
        model.eval()
        _SCREAMING_SNAKE_CASE             =    model(A )
        _SCREAMING_SNAKE_CASE             =    _SCREAMING_SNAKE_CASE             =    self.image_size // (self.downsampling_rates[-1] * 2)
        self.parent.assertEqual(
            result.last_hidden_state.shape				,	(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )


    def     snake_case_(	self				,	A				,	A				,	A )	->     Any:
        _SCREAMING_SNAKE_CASE             =    self.num_labels
        _SCREAMING_SNAKE_CASE             =    GLPNForDepthEstimation(A )
        model.to(A )
        model.eval()
        _SCREAMING_SNAKE_CASE             =    model(A )
        self.parent.assertEqual(result.predicted_depth.shape				,	(self.batch_size, self.image_size, self.image_size) )
        _SCREAMING_SNAKE_CASE             =    model(A				,	labels=A )
        self.parent.assertEqual(result.predicted_depth.shape				,	(self.batch_size, self.image_size, self.image_size) )





    def     snake_case_(	self )	->     Any:
        _SCREAMING_SNAKE_CASE             =    self.prepare_config_and_inputs()
        _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    config_and_inputs
        _SCREAMING_SNAKE_CASE             =    {"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_torch
class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):

    '''simple docstring'''
    UpperCamelCase									=				(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
    UpperCamelCase									=				(
        {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
    )

    UpperCamelCase									=				False
    UpperCamelCase									=				False
    UpperCamelCase									=				False


    def     snake_case_(	self )	->     str:
        _SCREAMING_SNAKE_CASE             =    GLPNModelTester(self )
        _SCREAMING_SNAKE_CASE             =    GLPNConfigTester(self				,	config_class=A )


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


    def     snake_case_(	self )	->     Tuple:
        _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A )


    def     snake_case_(	self )	->     List[Any]:
        _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*A )


    @unittest.skip("""GLPN does not use inputs_embeds""" )
    def     snake_case_(	self )	->     List[Any]:
        pass


    @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods""" )
    def     snake_case_(	self )	->     Any:
        pass


    def     snake_case_(	self )	->     str:
        _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.forward )
            # 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     snake_case_(	self )	->     List[str]:
        _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
        _SCREAMING_SNAKE_CASE             =    True

        for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE             =    True
            _SCREAMING_SNAKE_CASE             =    False
            _SCREAMING_SNAKE_CASE             =    True
            _SCREAMING_SNAKE_CASE             =    model_class(A )
            model.to(A )
            model.eval()
            with torch.no_grad():
                _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
            _SCREAMING_SNAKE_CASE             =    outputs.attentions

            _SCREAMING_SNAKE_CASE             =    sum(self.model_tester.depths )
            self.assertEqual(len(A )				,	A )

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            _SCREAMING_SNAKE_CASE             =    True
            _SCREAMING_SNAKE_CASE             =    model_class(A )
            model.to(A )
            model.eval()
            with torch.no_grad():
                _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
            _SCREAMING_SNAKE_CASE             =    outputs.attentions

            self.assertEqual(len(A )				,	A )

            # verify the first attentions (first block, first layer)
            _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // 4) ** 2
            _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
            self.assertListEqual(
                list(attentions[0].shape[-3:] )				,	[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]				,	)

            # verify the last attentions (last block, last layer)
            _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // 32) ** 2
            _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
            self.assertListEqual(
                list(attentions[-1].shape[-3:] )				,	[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]				,	)
            _SCREAMING_SNAKE_CASE             =    len(A )

            # Check attention is always last and order is fine
            _SCREAMING_SNAKE_CASE             =    True
            _SCREAMING_SNAKE_CASE             =    True
            _SCREAMING_SNAKE_CASE             =    model_class(A )
            model.to(A )
            model.eval()
            with torch.no_grad():
                _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )

            self.assertEqual(out_len + 1				,	len(A ) )

            _SCREAMING_SNAKE_CASE             =    outputs.attentions

            self.assertEqual(len(A )				,	A )
            # verify the first attentions (first block, first layer)
            _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // 4) ** 2
            _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
            self.assertListEqual(
                list(self_attentions[0].shape[-3:] )				,	[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]				,	)


    def     snake_case_(	self )	->     List[Any]:
        def check_hidden_states_output(A				,	A				,	A ):
            _SCREAMING_SNAKE_CASE             =    model_class(A )
            model.to(A )
            model.eval()

            with torch.no_grad():
                _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )

            _SCREAMING_SNAKE_CASE             =    outputs.hidden_states

            _SCREAMING_SNAKE_CASE             =    self.model_tester.num_encoder_blocks
            self.assertEqual(len(A )				,	A )

            # verify the first hidden states (first block)
            self.assertListEqual(
                list(hidden_states[0].shape[-3:] )				,	[
                    self.model_tester.hidden_sizes[0],
                    self.model_tester.image_size // 4,
                    self.model_tester.image_size // 4,
                ]				,	)

        _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     snake_case_(	self )	->     int:
        if not self.model_tester.is_training:
            return

        _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
        _SCREAMING_SNAKE_CASE             =    True

        for model_class in self.all_model_classes:
            if model_class in get_values(A ):
                continue
            # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
            # this can then be incorporated into _prepare_for_class in test_modeling_common.py
            if model_class.__name__ == "GLPNForDepthEstimation":
                _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    inputs_dict["""pixel_values"""].shape
                _SCREAMING_SNAKE_CASE             =    torch.zeros(
                    [self.model_tester.batch_size, height, width]				,	device=A ).long()
            _SCREAMING_SNAKE_CASE             =    model_class(A )
            model.to(A )
            model.train()
            _SCREAMING_SNAKE_CASE             =    self._prepare_for_class(A				,	A				,	return_labels=A )
            _SCREAMING_SNAKE_CASE             =    model(**A ).loss
            loss.backward()





    @slow
    def     snake_case_(	self )	->     str:
        for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE             =    GLPNModel.from_pretrained(A )
            self.assertIsNotNone(A )





def lowerCamelCase		(  )		->Union[str, Any]:
    _SCREAMING_SNAKE_CASE             =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
    return image






@require_torch
@require_vision
@slow
class       a_  (       unittest.TestCase ):

    '''simple docstring'''


    @slow
    def     snake_case_(	self )	->     Optional[Any]:
        _SCREAMING_SNAKE_CASE             =    GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
        _SCREAMING_SNAKE_CASE             =    GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(A )

        _SCREAMING_SNAKE_CASE             =    prepare_img()
        _SCREAMING_SNAKE_CASE             =    image_processor(images=A				,	return_tensors="""pt""" ).to(A )

        # forward pass
        with torch.no_grad():
            _SCREAMING_SNAKE_CASE             =    model(**A )

        # verify the predicted depth
        _SCREAMING_SNAKE_CASE             =    torch.Size([1, 480, 640] )
        self.assertEqual(outputs.predicted_depth.shape				,	A )

        _SCREAMING_SNAKE_CASE             =    torch.tensor(
            [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] ).to(A )

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



