

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__		):



   def 			__magic_name__       (				self				:	Any		):
     UpperCAmelCase			:				Union[str, Any]										=					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    __UpperCAmelCase	:



   def __init__(				self				:	Any,						__A				:	Tuple,						__A				:	Dict=1_3,						__A				:	Optional[int]=6_4,						__A				:	Optional[Any]=3,						__A				:	Dict=4,						__A				:	Any=[2, 2, 2, 2],						__A				:	int=[8, 4, 2, 1],						__A				:	Any=[1_6, 3_2, 6_4, 1_2_8],						__A				:	List[Any]=[1, 4, 8, 1_6],						__A				:	List[Any]=[1, 2, 4, 8],						__A				:	Union[str, Any]=True,						__A				:	int=True,						__A				:	Optional[int]="gelu",						__A				:	Union[str, Any]=0.1,						__A				:	List[Any]=0.1,						__A				:	List[Any]=0.0_2,						__A				:	List[str]=1_6,						__A				:	Tuple=3,						__A				:	Optional[int]=None,						):
     UpperCAmelCase			:				List[str]										=					parent
     UpperCAmelCase			:				List[Any]										=					batch_size
     UpperCAmelCase			:				Optional[Any]										=					image_size
     UpperCAmelCase			:				str										=					num_channels
     UpperCAmelCase			:				Tuple										=					num_encoder_blocks
     UpperCAmelCase			:				Union[str, Any]										=					sr_ratios
     UpperCAmelCase			:				List[Any]										=					depths
     UpperCAmelCase			:				int										=					hidden_sizes
     UpperCAmelCase			:				int										=					downsampling_rates
     UpperCAmelCase			:				List[Any]										=					num_attention_heads
     UpperCAmelCase			:				Optional[int]										=					is_training
     UpperCAmelCase			:				str										=					use_labels
     UpperCAmelCase			:				Tuple										=					hidden_act
     UpperCAmelCase			:				str										=					hidden_dropout_prob
     UpperCAmelCase			:				Optional[int]										=					attention_probs_dropout_prob
     UpperCAmelCase			:				List[str]										=					initializer_range
     UpperCAmelCase			:				Tuple										=					decoder_hidden_size
     UpperCAmelCase			:				Dict										=					num_labels
     UpperCAmelCase			:				List[Any]										=					scope



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

     UpperCAmelCase			:				Dict										=					None
     if self.use_labels:
       UpperCAmelCase			:				Optional[int]										=					ids_tensor([self.batch_size, self.image_size, self.image_size],						self.num_labels		)

     UpperCAmelCase			:				Union[str, Any]										=					self.get_config()
     return config, pixel_values, labels



   def 			__magic_name__       (				self				:	str		):
     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 			__magic_name__       (				self				:	List[str],						__A				:	str,						__A				:	Optional[int],						__A				:	Dict		):
     UpperCAmelCase			:				Any										=					GLPNModel(config=__A		)
     model.to(__A		)
     model.eval()
     UpperCAmelCase			:				Union[str, Any]										=					model(__A		)
     UpperCAmelCase			:				Optional[Any]										=					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 			__magic_name__       (				self				:	Optional[Any],						__A				:	int,						__A				:	Any,						__A				:	List[str]		):
     UpperCAmelCase			:				List[str]										=					self.num_labels
     UpperCAmelCase			:				Dict										=					GLPNForDepthEstimation(__A		)
     model.to(__A		)
     model.eval()
     UpperCAmelCase			:				Tuple										=					model(__A		)
     self.parent.assertEqual(result.predicted_depth.shape,						(self.batch_size, self.image_size, self.image_size)		)
     UpperCAmelCase			:				Any										=					model(__A,						labels=__A		)
     self.parent.assertEqual(result.predicted_depth.shape,						(self.batch_size, self.image_size, self.image_size)		)





   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				Optional[int]										=					self.prepare_config_and_inputs()
     UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				Tuple										=					config_and_inputs
     UpperCAmelCase			:				List[str]										=					{'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class    __UpperCAmelCase	(			lowerCamelCase__     ,    lowerCamelCase__     ,    unittest.TestCase		):
   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 			__magic_name__       (				self				:	Optional[Any]		):
     UpperCAmelCase			:				Tuple										=					GLPNModelTester(self		)
     UpperCAmelCase			:				Optional[Any]										=					GLPNConfigTester(self,						config_class=__A		)



   def 			__magic_name__       (				self				:	Optional[Any]		):
     self.config_tester.run_common_tests()



   def 			__magic_name__       (				self				:	Any		):
     UpperCAmelCase			:				List[Any]										=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A		)



   def 			__magic_name__       (				self				:	Dict		):
     UpperCAmelCase			:				Union[str, Any]										=					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 			__magic_name__       (				self				:	Optional[int]		):
     pass



   @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''		)
   def 			__magic_name__       (				self				:	Union[str, Any]		):
     pass



   def 			__magic_name__       (				self				:	Tuple		):
     UpperCAmelCase					,		UpperCAmelCase			:				Optional[int]										=					self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase			:				Any										=					['''pixel_values''']
       self.assertListEqual(arg_names[:1],						__A		)



   def 			__magic_name__       (				self				:	Union[str, Any]		):
     UpperCAmelCase					,		UpperCAmelCase			:				Optional[Any]										=					self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase			:				str										=					True

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

       UpperCAmelCase			:				Any										=					sum(self.model_tester.depths		)
       self.assertEqual(len(__A		),						__A		)

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

       self.assertEqual(len(__A		),						__A		)

       # verify the first attentions (first block, first layer)
       UpperCAmelCase			:				str										=					(self.model_tester.image_size // 4) ** 2
       UpperCAmelCase			:				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)
       UpperCAmelCase			:				Tuple										=					(self.model_tester.image_size // 3_2) ** 2
       UpperCAmelCase			:				Union[str, Any]										=					(self.model_tester.image_size // (3_2 * 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],						)
       UpperCAmelCase			:				Union[str, Any]										=					len(__A		)

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

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

       UpperCAmelCase			:				List[Any]										=					outputs.attentions

       self.assertEqual(len(__A		),						__A		)
       # verify the first attentions (first block, first layer)
       UpperCAmelCase			:				Optional[int]										=					(self.model_tester.image_size // 4) ** 2
       UpperCAmelCase			:				List[str]										=					(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 			__magic_name__       (				self				:	Dict		):
     def check_hidden_states_output(__A				:	List[Any],						__A				:	Optional[int],						__A				:	Tuple		):
       UpperCAmelCase			:				List[str]										=					model_class(__A		)
       model.to(__A		)
       model.eval()

       with torch.no_grad():
         UpperCAmelCase			:				Optional[int]										=					model(**self._prepare_for_class(__A,						__A		)		)

       UpperCAmelCase			:				List[Any]										=					outputs.hidden_states

       UpperCAmelCase			:				Tuple										=					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,
           ],						)

     UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					self.model_tester.prepare_config_and_inputs_for_common()

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

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       UpperCAmelCase			:				Dict										=					True

       check_hidden_states_output(__A,						__A,						__A		)



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

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





   @slow
   def 			__magic_name__       (				self				:	int		):
     for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase			:				Optional[Any]										=					GLPNModel.from_pretrained(__A		)
       self.assertIsNotNone(__A		)






def      a__	(						)    ->		Dict:
  UpperCAmelCase			:				Optional[int]										=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
  return image






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



   @slow
   def 			__magic_name__       (				self				:	Any		):
     UpperCAmelCase			:				int										=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
     UpperCAmelCase			:				Optional[Any]										=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(__A		)

     UpperCAmelCase			:				str										=					prepare_img()
     UpperCAmelCase			:				List[str]										=					image_processor(images=__A,						return_tensors='''pt'''		).to(__A		)

     # forward pass
     with torch.no_grad():
       UpperCAmelCase			:				List[Any]										=					model(**__A		)

     # verify the predicted depth
     UpperCAmelCase			:				List[Any]										=					torch.Size([1, 4_8_0, 6_4_0]		)
     self.assertEqual(outputs.predicted_depth.shape,						__A		)

     UpperCAmelCase			:				Optional[int]										=					torch.tensor(
         [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]		).to(__A		)

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








def      a__	(						UpperCAmelCase				:     list     )    ->		list:
  UpperCAmelCase			:				Tuple										=					len(UpperCAmelCase     )

  # Set the gap value to be decreased by a factor of 1.3
  # after each iteration
  UpperCAmelCase			:				Optional[Any]										=					1.3

  # Continue sorting until the gap is 1
  while gap > 1:
    # Decrease the gap value
    UpperCAmelCase			:				int										=					int(gap / shrink     )

    # Sort the elements using insertion sort
    for i in range(UpperCAmelCase							,					len(UpperCAmelCase     )     ):
      UpperCAmelCase			:				Union[str, Any]										=					collection[i]
      UpperCAmelCase			:				List[Any]										=					i
      while j >= gap and collection[j - gap] > temp:
        UpperCAmelCase			:				int										=					collection[j - gap]
        j -= gap
      UpperCAmelCase			:				List[Any]										=					temp

  return collection


if __name__ == "__main__":
     import doctest

     doctest.testmod()





