
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 				UpperCamelCase__		(	__lowerCamelCase	):






       """simple docstring"""


       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          SCREAMING_SNAKE_CASE_	=      self.config_class(**self.inputs_dict	)
          self.parent.assertTrue(hasattr(lowerCamelCase_ ,     '''hidden_sizes'''	)	)
          self.parent.assertTrue(hasattr(lowerCamelCase_ ,     '''num_attention_heads'''	)	)
          self.parent.assertTrue(hasattr(lowerCamelCase_ ,     '''num_encoder_blocks'''	)	)







class 				UpperCamelCase__		:






       """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     _UpperCamelCase	(							self	)				->				List[str]:
          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     _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 ,     _A ,     _A ,     _A	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      GLPNModel(config=lowerCamelCase_	)
          model.to(lowerCamelCase_	)
          model.eval()
          SCREAMING_SNAKE_CASE_	=      model(lowerCamelCase_	)
          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     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      self.num_labels
          SCREAMING_SNAKE_CASE_	=      GLPNForDepthEstimation(lowerCamelCase_	)
          model.to(lowerCamelCase_	)
          model.eval()
          SCREAMING_SNAKE_CASE_	=      model(lowerCamelCase_	)
          self.parent.assertEqual(result.predicted_depth.shape ,     (self.batch_size, self.image_size, self.image_size)	)
          SCREAMING_SNAKE_CASE_	=      model(lowerCamelCase_ ,     labels=lowerCamelCase_	)
          self.parent.assertEqual(result.predicted_depth.shape ,     (self.batch_size, self.image_size, self.image_size)	)




       def     _UpperCamelCase	(							self	)				->				str:
          SCREAMING_SNAKE_CASE_	=      self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_	=      config_and_inputs
          SCREAMING_SNAKE_CASE_	=      {'pixel_values': pixel_values}
          return config, inputs_dict







@require_torch
class 				UpperCamelCase__		(	__lowerCamelCase       ,    __lowerCamelCase       ,    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     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      GLPNModelTester(self	)
          SCREAMING_SNAKE_CASE_	=      GLPNConfigTester(self ,     config_class=lowerCamelCase_	)



       def     _UpperCamelCase	(							self	)				->				Dict:
          self.config_tester.run_common_tests()



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase_	)



       def     _UpperCamelCase	(							self	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase_	)



       @unittest.skip('''GLPN does not use inputs_embeds'''	)
       def     _UpperCamelCase	(							self	)				->				Union[str, 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	)				->				List[str]:
          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(lowerCamelCase_	)
             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] ,     lowerCamelCase_	)



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          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(lowerCamelCase_	)
             model.to(lowerCamelCase_	)
             model.eval()
             with torch.no_grad():
                SCREAMING_SNAKE_CASE_	=      model(**self._prepare_for_class(lowerCamelCase_ ,     lowerCamelCase_	)	)
             SCREAMING_SNAKE_CASE_	=      outputs.attentions

             SCREAMING_SNAKE_CASE_	=      sum(self.model_tester.depths	)
             self.assertEqual(len(lowerCamelCase_	) ,     lowerCamelCase_	)

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

             self.assertEqual(len(lowerCamelCase_	) ,     lowerCamelCase_	)

             # 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(lowerCamelCase_	)

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

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

             SCREAMING_SNAKE_CASE_	=      outputs.attentions

             self.assertEqual(len(lowerCamelCase_	) ,     lowerCamelCase_	)
             # 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     _UpperCamelCase	(							self	)				->				Optional[Any]:






          def check_hidden_states_output(_A ,     _A ,     _A	):
             SCREAMING_SNAKE_CASE_	=      model_class(lowerCamelCase_	)
             model.to(lowerCamelCase_	)
             model.eval()

             with torch.no_grad():
                SCREAMING_SNAKE_CASE_	=      model(**self._prepare_for_class(lowerCamelCase_ ,     lowerCamelCase_	)	)

             SCREAMING_SNAKE_CASE_	=      outputs.hidden_states

             SCREAMING_SNAKE_CASE_	=      self.model_tester.num_encoder_blocks
             self.assertEqual(len(lowerCamelCase_	) ,     lowerCamelCase_	)

             # 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_	=      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(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_	)

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

             check_hidden_states_output(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_	)



       def     _UpperCamelCase	(							self	)				->				int:






          if not self.model_tester.is_training:
             return

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




       @slow
       def     _UpperCamelCase	(							self	)				->				Any:






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







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





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






       """simple docstring"""


       @slow
       def     _UpperCamelCase	(							self	)				->				str:
          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(lowerCamelCase_	)

          SCREAMING_SNAKE_CASE_	=      prepare_img()
          SCREAMING_SNAKE_CASE_	=      image_processor(images=lowerCamelCase_ ,     return_tensors='''pt'''	).to(lowerCamelCase_	)

          # forward pass
          with torch.no_grad():
             SCREAMING_SNAKE_CASE_	=      model(**lowerCamelCase_	)

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

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

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



"""simple docstring"""







def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	if len(__UpperCAmelCase      ) > 1:
		_lowercase			:     Dict      					=				len(__UpperCAmelCase      ) // 2  # Finds the middle of the array
		_lowercase			:     List[str]      					=				arr[
		    :middle_length
		]  # Creates an array of the elements in the first half.
		_lowercase			:     Optional[int]      					=				arr[
		    middle_length:
		]  # Creates an array of the elements in the second half.
		_lowercase			:     Optional[Any]      					=				len(__UpperCAmelCase      )
		_lowercase			:     Optional[int]      					=				len(__UpperCAmelCase      )
		merge(__UpperCAmelCase      )  # Starts sorting the left.
		merge(__UpperCAmelCase      )  # Starts sorting the right
		_lowercase			:     Dict      					=				0  # Left Counter
		_lowercase			:     List[str]      					=				0  # Right Counter
		_lowercase			:     Optional[Any]      					=				0  # Position Counter
		while (
		    left_index < left_size and right_index < right_size
		):  # Runs until the lowers size of the left and right are sorted.
			if left_array[left_index] < right_array[right_index]:
				_lowercase			:     Optional[int]      					=				left_array[left_index]
				left_index += 1
			else:
				_lowercase			:     int      					=				right_array[right_index]
				right_index += 1
			index += 1
		while (
		    left_index < left_size
		):  # Adds the left over elements in the left half of the array
			_lowercase			:     Any      					=				left_array[left_index]
			left_index += 1
			index += 1
		while (
		    right_index < right_size
		):  # Adds the left over elements in the right half of the array
			_lowercase			:     str      					=				right_array[right_index]
			right_index += 1
			index += 1
	return arr


if __name__ == "__main__":
						import doctest

						doctest.testmod()



