





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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         __snake_case:		int					    =		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        __snake_case			:





       '''simple docstring'''







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




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Optional[int]					    =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

         __snake_case:		Any					    =		self.get_config()
         return config, pixel_values, labels




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         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 				UpperCAmelCase__					(    self					:       int				,							A					:       List[str]				,							A					:       str				,							A					:       Union[str, Any]						):
         __snake_case:		Any					    =		GLPNModel(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Optional[Any]					    =		model(A						)
         __snake_case:		str					    =		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 				UpperCAmelCase__					(    self					:       int				,							A					:       int				,							A					:       List[str]				,							A					:       Any						):
         __snake_case:		List[str]					    =		self.num_labels
         __snake_case:		List[Any]					    =		GLPNForDepthEstimation(A						)
         model.to(A						)
         model.eval()
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.predicted_depth.shape				,							(self.batch_size, self.image_size, self.image_size)						)
         __snake_case:		Union[str, Any]					    =		model(A				,							labels=A						)
         self.parent.assertEqual(result.predicted_depth.shape				,							(self.batch_size, self.image_size, self.image_size)						)






       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         __snake_case:		Dict					    =		self.prepare_config_and_inputs()
         __snake_case    ,  __snake_case    ,  __snake_case:		Optional[Any]					    =		config_and_inputs
         __snake_case:		Optional[Any]					    =		{"""pixel_values""": pixel_values}
         return config, inputs_dict







@require_torch
class        __snake_case			(				__lowerCamelCase  ,     __lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
       lowerCAmelCase__ 						=	(
           {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
       )

       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		Optional[int]					    =		GLPNModelTester(self						)
         __snake_case:		Tuple					    =		GLPNConfigTester(self				,							config_class=A						)




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         self.config_tester.run_common_tests()




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Dict					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		Tuple					    =		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 				UpperCAmelCase__					(    self					:       List[Any]						):
         pass




       @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""						)
       def 				UpperCAmelCase__					(    self					:       List[Any]						):
         pass




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case    ,  __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs_for_common()

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

           __snake_case:		Any					    =		["""pixel_values"""]
           self.assertListEqual(arg_names[:1]				,							A						)




       def 				UpperCAmelCase__					(    self					:       Union[str, Any]						):
         __snake_case    ,  __snake_case:		Tuple					    =		self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case:		Optional[int]					    =		True

         for model_class in self.all_model_classes:
           __snake_case:		List[str]					    =		True
           __snake_case:		List[Any]					    =		False
           __snake_case:		Optional[Any]					    =		True
           __snake_case:		List[Any]					    =		model_class(A						)
           model.to(A						)
           model.eval()
           with torch.no_grad():
             __snake_case:		List[str]					    =		model(**self._prepare_for_class(A				,							A						)						)
           __snake_case:		Any					    =		outputs.attentions

           __snake_case:		Any					    =		sum(self.model_tester.depths						)
           self.assertEqual(len(A						)				,							A						)

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

           self.assertEqual(len(A						)				,							A						)

           # verify the first attentions (first block, first layer)
           __snake_case:		List[Any]					    =		(self.model_tester.image_size // 4) ** 2
           __snake_case:		Union[str, Any]					    =		(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)
           __snake_case:		str					    =		(self.model_tester.image_size // 32) ** 2
           __snake_case:		List[str]					    =		(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]				,							)
           __snake_case:		Dict					    =		len(A						)

           # Check attention is always last and order is fine
           __snake_case:		Union[str, Any]					    =		True
           __snake_case:		Optional[int]					    =		True
           __snake_case:		List[str]					    =		model_class(A						)
           model.to(A						)
           model.eval()
           with torch.no_grad():
             __snake_case:		Union[str, Any]					    =		model(**self._prepare_for_class(A				,							A						)						)

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

           __snake_case:		Tuple					    =		outputs.attentions

           self.assertEqual(len(A						)				,							A						)
           # verify the first attentions (first block, first layer)
           __snake_case:		Optional[Any]					    =		(self.model_tester.image_size // 4) ** 2
           __snake_case:		int					    =		(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 				UpperCAmelCase__					(    self					:       int						):
         def check_hidden_states_output(A					:       Dict				,							A					:       int				,							A					:       Optional[int]						):
           __snake_case:		Optional[Any]					    =		model_class(A						)
           model.to(A						)
           model.eval()

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

           __snake_case:		Optional[Any]					    =		outputs.hidden_states

           __snake_case:		Optional[Any]					    =		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,
               ]				,							)

         __snake_case    ,  __snake_case:		Tuple					    =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           __snake_case:		Tuple					    =		True
           check_hidden_states_output(A				,							A				,							A						)

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

           check_hidden_states_output(A				,							A				,							A						)




       def 				UpperCAmelCase__					(    self					:       int						):
         if not self.model_tester.is_training:
           return

         __snake_case    ,  __snake_case:		str					    =		self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case:		str					    =		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":
             __snake_case    ,  __snake_case    ,  __snake_case    ,  __snake_case:		List[str]					    =		inputs_dict["""pixel_values"""].shape
             __snake_case:		str					    =		torch.zeros(
                 [self.model_tester.batch_size, height, width]				,							device=A						).long()
           __snake_case:		Dict					    =		model_class(A						)
           model.to(A						)
           model.train()
           __snake_case:		List[str]					    =		self._prepare_for_class(A				,							A				,							return_labels=A						)
           __snake_case:		Tuple					    =		model(**A						).loss
           loss.backward()






       @slow
       def 				UpperCAmelCase__					(    self					:       Tuple						):
         for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __snake_case:		Any					    =		GLPNModel.from_pretrained(A						)
           self.assertIsNotNone(A						)







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







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





       '''simple docstring'''







       @slow
       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Tuple					    =		GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
         __snake_case:		str					    =		GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(A						)

         __snake_case:		int					    =		prepare_img()
         __snake_case:		str					    =		image_processor(images=A				,							return_tensors="""pt"""						).to(A						)

         # forward pass
         with torch.no_grad():
           __snake_case:		Union[str, Any]					    =		model(**A						)

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

         __snake_case:		Dict					    =		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						)						)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase			:  List[str]										=				{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  Any										=				["LevitFeatureExtractor"]
      __UpperCAmelCase			:  Tuple										=				["LevitImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  str										=				[
          "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LevitForImageClassification",
          "LevitForImageClassificationWithTeacher",
          "LevitModel",
          "LevitPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_levit import LevitFeatureExtractor
            from .image_processing_levit import LevitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_levit import (
                LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                LevitForImageClassification,
                LevitForImageClassificationWithTeacher,
                LevitModel,
                LevitPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase			:  Union[str, Any]										=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






