
'''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			(_lowerCamelCase):





       """simple docstring"""




       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          _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, 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    ,							)      ->     Dict:
          _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[Any]:
          _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 )      ->     List[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__ )      ->     Dict:
          _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     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     str:
          _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     UpperCamelCase			(							self )      ->     Tuple:
          _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			(_lowerCamelCase					,   _lowerCamelCase					,   unittest.TestCase):





       """simple docstring"""




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

       SCREAMING_SNAKE_CASE =					False
       SCREAMING_SNAKE_CASE =					False
       SCREAMING_SNAKE_CASE =					False






       def     UpperCamelCase			(							self )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			GLPNModelTester(self )
          _SCREAMING_SNAKE_CASE       	=			GLPNConfigTester(self    ,							config_class=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          self.config_tester.run_common_tests()






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__ )






       def     UpperCamelCase			(							self )      ->     List[str]:
          _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     UpperCamelCase			(							self )      ->     Dict:
          pass






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






       def     UpperCamelCase			(							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:
             _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     UpperCamelCase			(							self )      ->     List[Any]:
          _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     UpperCamelCase			(							self )      ->     Dict:
          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     UpperCamelCase			(							self )      ->     str:
          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     UpperCamelCase			(							self )      ->     Optional[Any]:
          for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _SCREAMING_SNAKE_CASE       	=			GLPNModel.from_pretrained(A__ )
             self.assertIsNotNone(A__ )






def 				lowerCAmelCase_		(			)       ->       Dict:




   """simple docstring"""
   _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     UpperCamelCase			(							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, 4_80, 6_40] )
          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 ) )








'''simple docstring'''



from cva import destroyAllWindows, imread, imshow, waitKey
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			img.shape[0], img.shape[1]

   def to_grayscale(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  ):
      return 0.2126 * red + 0.587 * green + 0.114 * blue

   def normalize(SCREAMING_SNAKE_CASE_  ):
      return min(SCREAMING_SNAKE_CASE_      , 2_55  )

   for i in range(SCREAMING_SNAKE_CASE_  ):
      for j in range(SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			int(to_grayscale(*img[i][j]  )  )
         _SCREAMING_SNAKE_CASE       	=			[
             normalize(SCREAMING_SNAKE_CASE_  ),
             normalize(greyscale + factor  ),
             normalize(greyscale + 2 * factor  ),
         ]

   return img


if __name__ == "__main__":
     # read original image
     UpperCamelCase__ :						Optional[int]    							=					{
         percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
     }

     for percentage, img in images.items():
          make_sepia(img, percentage)

     for percentage, img in images.items():
          imshow(f"""Original image with sepia (factor: {percentage})""", img)

     waitKey(0)
     destroyAllWindows()






