





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  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       __magic_name__				=       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  SCREAMING_SNAKE_CASE_  :


  """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, 1_28]	,  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	,  )		->   Tuple:





       '''simple docstring'''







       __magic_name__				=       parent
       __magic_name__				=       batch_size
       __magic_name__				=       image_size
       __magic_name__				=       num_channels
       __magic_name__				=       num_encoder_blocks
       __magic_name__				=       sr_ratios
       __magic_name__				=       depths
       __magic_name__				=       hidden_sizes
       __magic_name__				=       downsampling_rates
       __magic_name__				=       num_attention_heads
       __magic_name__				=       is_training
       __magic_name__				=       use_labels
       __magic_name__				=       hidden_act
       __magic_name__				=       hidden_dropout_prob
       __magic_name__				=       attention_probs_dropout_prob
       __magic_name__				=       initializer_range
       __magic_name__				=       decoder_hidden_size
       __magic_name__				=       num_labels
       __magic_name__				=       scope





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       __magic_name__				=       self.get_config()
       return config, pixel_values, labels





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       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 			__A      (  self	,  A	,  A	,  A						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       GLPNModel(config=A						)
       model.to(A						)
       model.eval()
       __magic_name__				=       model(A						)
       __magic_name__				=       __magic_name__				=       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 			__A      (  self	,  A	,  A	,  A						)		->   Tuple:





       '''simple docstring'''







       __magic_name__				=       self.num_labels
       __magic_name__				=       GLPNForDepthEstimation(A						)
       model.to(A						)
       model.eval()
       __magic_name__				=       model(A						)
       self.parent.assertEqual(result.predicted_depth.shape	,  (self.batch_size, self.image_size, self.image_size)						)
       __magic_name__				=       model(A	,  labels=A						)
       self.parent.assertEqual(result.predicted_depth.shape	,  (self.batch_size, self.image_size, self.image_size)						)







  def 			__A      (  self						)		->   str:





       '''simple docstring'''







       __magic_name__				=       self.prepare_config_and_inputs()
       __magic_name__		,  __magic_name__		,  __magic_name__				=       config_and_inputs
       __magic_name__				=       {'''pixel_values''': pixel_values}
       return config, inputs_dict



@require_torch
class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__   ,							SCREAMING_SNAKE_CASE__   ,							unittest.TestCase			):


  """simple docstring"""





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

  _a								=							False
  _a								=							False
  _a								=							False





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       __magic_name__				=       GLPNModelTester(self						)
       __magic_name__				=       GLPNConfigTester(self	,  config_class=A						)





  def 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       self.config_tester.run_common_tests()





  def 			__A      (  self						)		->   Any:





       '''simple docstring'''







       __magic_name__				=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       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 			__A      (  self						)		->   str:





       '''simple docstring'''







       pass





  @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''						)
  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       pass





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       __magic_name__		,  __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()

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

            __magic_name__				=       ['''pixel_values''']
            self.assertListEqual(arg_names[:1]	,  A						)





  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







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

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

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

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

            self.assertEqual(len(A						)	,  A						)

            # verify the first attentions (first block, first layer)
            __magic_name__				=       (self.model_tester.image_size // 4) ** 2
            __magic_name__				=       (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)
            __magic_name__				=       (self.model_tester.image_size // 32) ** 2
            __magic_name__				=       (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]	,  )
            __magic_name__				=       len(A						)

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

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

            __magic_name__				=       outputs.attentions

            self.assertEqual(len(A						)	,  A						)
            # verify the first attentions (first block, first layer)
            __magic_name__				=       (self.model_tester.image_size // 4) ** 2
            __magic_name__				=       (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 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       def check_hidden_states_output(A	,  A	,  A						):
            __magic_name__				=       model_class(A						)
            model.to(A						)
            model.eval()

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

            __magic_name__				=       outputs.hidden_states

            __magic_name__				=       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,
                ]	,  )

       __magic_name__		,  __magic_name__				=       self.model_tester.prepare_config_and_inputs_for_common()

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

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

            check_hidden_states_output(A	,  A	,  A						)





  def 			__A      (  self						)		->   Any:





       '''simple docstring'''







       if not self.model_tester.is_training:
            return

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







  @slow
  def 			__A      (  self						)		->   str:





       '''simple docstring'''







       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __magic_name__				=       GLPNModel.from_pretrained(A						)
            self.assertIsNotNone(A						)




def   _SCREAMING_SNAKE_CASE      (	):
     __magic_name__				=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
     return image



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


  """simple docstring"""





  @slow
  def 			__A      (  self						)		->   str:





       '''simple docstring'''







       __magic_name__				=       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
       __magic_name__				=       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(A						)

       __magic_name__				=       prepare_img()
       __magic_name__				=       image_processor(images=A	,  return_tensors='''pt'''						).to(A						)

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

       # verify the predicted depth
       __magic_name__				=       torch.Size([1, 4_80, 6_40]						)
       self.assertEqual(outputs.predicted_depth.shape	,  A						)

       __magic_name__				=       torch.tensor(
           [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]						).to(A						)

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






from ..utils import DummyObject, requires_backends



class  SCREAMING_SNAKE_CASE_  ( metaclass=SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							["""torch""", """transformers""", """k_diffusion"""]





  def __init__(  self	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(self	,  ['''torch''', '''transformers''', '''k_diffusion''']						)





  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Dict:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)







  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)