code: 
  
  """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 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						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 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase=13       ,					_lowerCamelCase=64       ,					_lowerCamelCase=3       ,					_lowerCamelCase=4       ,					_lowerCamelCase=[2, 2, 2, 2]       ,					_lowerCamelCase=[8, 4, 2, 1]       ,					_lowerCamelCase=[16, 32, 64, 128]       ,					_lowerCamelCase=[1, 4, 8, 16]       ,					_lowerCamelCase=[1, 2, 4, 8]       ,					_lowerCamelCase=True       ,					_lowerCamelCase=True       ,					_lowerCamelCase="gelu"       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.02       ,					_lowerCamelCase=16       ,					_lowerCamelCase=3       ,					_lowerCamelCase=None       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						parent
          UpperCAmelCase__ :       int						=						batch_size
          UpperCAmelCase__ :       str						=						image_size
          UpperCAmelCase__ :       Tuple						=						num_channels
          UpperCAmelCase__ :       Any						=						num_encoder_blocks
          UpperCAmelCase__ :       List[Any]						=						sr_ratios
          UpperCAmelCase__ :       Dict						=						depths
          UpperCAmelCase__ :       Dict						=						hidden_sizes
          UpperCAmelCase__ :       Dict						=						downsampling_rates
          UpperCAmelCase__ :       Union[str, Any]						=						num_attention_heads
          UpperCAmelCase__ :       Any						=						is_training
          UpperCAmelCase__ :       Optional[Any]						=						use_labels
          UpperCAmelCase__ :       int						=						hidden_act
          UpperCAmelCase__ :       Optional[int]						=						hidden_dropout_prob
          UpperCAmelCase__ :       Tuple						=						attention_probs_dropout_prob
          UpperCAmelCase__ :       Tuple						=						initializer_range
          UpperCAmelCase__ :       Optional[Any]						=						decoder_hidden_size
          UpperCAmelCase__ :       Optional[int]						=						num_labels
          UpperCAmelCase__ :       str						=						scope
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
  
          UpperCAmelCase__ :       Optional[int]						=						None
          if self.use_labels:
                UpperCAmelCase__ :       str						=						ids_tensor([self.batch_size, self.image_size, self.image_size]       ,					self.num_labels  )
  
          UpperCAmelCase__ :       Optional[int]						=						self.get_config()
          return config, pixel_values, labels
  
  
  
    def    _a			(self  ):
  
  
          """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       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						GLPNModel(config=_lowerCamelCase  )
          model.to(_lowerCamelCase  )
          model.eval()
          UpperCAmelCase__ :       Dict						=						model(_lowerCamelCase  )
          UpperCAmelCase__ :       List[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    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.num_labels
          UpperCAmelCase__ :       int						=						GLPNForDepthEstimation(_lowerCamelCase  )
          model.to(_lowerCamelCase  )
          model.eval()
          UpperCAmelCase__ :       List[Any]						=						model(_lowerCamelCase  )
          self.parent.assertEqual(result.predicted_depth.shape       ,					(self.batch_size, self.image_size, self.image_size)  )
          UpperCAmelCase__ :       Optional[Any]						=						model(_lowerCamelCase       ,					labels=_lowerCamelCase  )
          self.parent.assertEqual(result.predicted_depth.shape       ,					(self.batch_size, self.image_size, self.image_size)  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						self.prepare_config_and_inputs()
          UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__ :       Dict						=						config_and_inputs
          UpperCAmelCase__ :       str						=						{"""pixel_values""": pixel_values}
          return config, inputs_dict
  
  
  
  
  @require_torch
  class 			lowerCamelCase  (					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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						GLPNModelTester(self  )
          UpperCAmelCase__ :       int						=						GLPNConfigTester(self       ,					config_class=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          self.config_tester.run_common_tests()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						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    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
  
    @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""  )
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       Union[str, Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                UpperCAmelCase__ :       List[str]						=						model_class(_lowerCamelCase  )
                UpperCAmelCase__ :       Dict						=						inspect.signature(model.forward  )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                UpperCAmelCase__ :       int						=						[*signature.parameters.keys()]
  
                UpperCAmelCase__ :       str						=						["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       Any						=						self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__ :       int						=						True
  
          for model_class in self.all_model_classes:
                UpperCAmelCase__ :       List[str]						=						True
                UpperCAmelCase__ :       Optional[Any]						=						False
                UpperCAmelCase__ :       Union[str, Any]						=						True
                UpperCAmelCase__ :       List[str]						=						model_class(_lowerCamelCase  )
                model.to(_lowerCamelCase  )
                model.eval()
                with torch.no_grad():
                      UpperCAmelCase__ :       Any						=						model(**self._prepare_for_class(_lowerCamelCase       ,					_lowerCamelCase  )  )
                UpperCAmelCase__ :       Tuple						=						outputs.attentions
  
                UpperCAmelCase__ :       Tuple						=						sum(self.model_tester.depths  )
                self.assertEqual(len(_lowerCamelCase  )       ,					_lowerCamelCase  )
  
                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                UpperCAmelCase__ :       List[Any]						=						True
                UpperCAmelCase__ :       List[str]						=						model_class(_lowerCamelCase  )
                model.to(_lowerCamelCase  )
                model.eval()
                with torch.no_grad():
                      UpperCAmelCase__ :       str						=						model(**self._prepare_for_class(_lowerCamelCase       ,					_lowerCamelCase  )  )
                UpperCAmelCase__ :       str						=						outputs.attentions
  
                self.assertEqual(len(_lowerCamelCase  )       ,					_lowerCamelCase  )
  
                # verify the first attentions (first block, first layer)
                UpperCAmelCase__ :       Optional[Any]						=						(self.model_tester.image_size // 4) ** 2
                UpperCAmelCase__ :       Tuple						=						(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__ :       List[str]						=						(self.model_tester.image_size // 32) ** 2
                UpperCAmelCase__ :       Any						=						(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]       ,					)
                UpperCAmelCase__ :       str						=						len(_lowerCamelCase  )
  
                # Check attention is always last and order is fine
                UpperCAmelCase__ :       int						=						True
                UpperCAmelCase__ :       Optional[int]						=						True
                UpperCAmelCase__ :       Optional[Any]						=						model_class(_lowerCamelCase  )
                model.to(_lowerCamelCase  )
                model.eval()
                with torch.no_grad():
                      UpperCAmelCase__ :       Tuple						=						model(**self._prepare_for_class(_lowerCamelCase       ,					_lowerCamelCase  )  )
  
                self.assertEqual(out_len + 1       ,					len(_lowerCamelCase  )  )
  
                UpperCAmelCase__ :       Tuple						=						outputs.attentions
  
                self.assertEqual(len(_lowerCamelCase  )       ,					_lowerCamelCase  )
                # verify the first attentions (first block, first layer)
                UpperCAmelCase__ :       Any						=						(self.model_tester.image_size // 4) ** 2
                UpperCAmelCase__ :       Optional[Any]						=						(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  ):
  
  
          """simple docstring"""
  
          def check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Optional[Any]						=						model_class(_lowerCamelCase  )
                model.to(_lowerCamelCase  )
                model.eval()
  
                with torch.no_grad():
                      UpperCAmelCase__ :       Dict						=						model(**self._prepare_for_class(_lowerCamelCase       ,					_lowerCamelCase  )  )
  
                UpperCAmelCase__ :       Optional[Any]						=						outputs.hidden_states
  
                UpperCAmelCase__ :       Optional[Any]						=						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,
                    ]       ,					)
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       int						=						self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                UpperCAmelCase__ :       Optional[int]						=						True
                check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCAmelCase__ :       List[Any]						=						True
  
                check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          if not self.model_tester.is_training:
                return
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       int						=						self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__ :       List[Any]						=						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":
                      UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__ :       Any						=						inputs_dict["""pixel_values"""].shape
                      UpperCAmelCase__ :       int						=						torch.zeros(
                          [self.model_tester.batch_size, height, width]       ,					device=_lowerCamelCase  ).long()
                UpperCAmelCase__ :       List[str]						=						model_class(_lowerCamelCase  )
                model.to(_lowerCamelCase  )
                model.train()
                UpperCAmelCase__ :       List[str]						=						self._prepare_for_class(_lowerCamelCase       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
                UpperCAmelCase__ :       int						=						model(**_lowerCamelCase  ).loss
                loss.backward()
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__ :       Dict						=						GLPNModel.from_pretrained(_lowerCamelCase  )
                self.assertIsNotNone(_lowerCamelCase  )
  
  
  
  
  def 				a__	(	)  ->   Optional[int]:
        UpperCAmelCase__ :       Optional[Any]						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
        return image
  
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
          UpperCAmelCase__ :       List[str]						=						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[Any]						=						prepare_img()
          UpperCAmelCase__ :       Tuple						=						image_processor(images=_lowerCamelCase       ,					return_tensors="""pt"""  ).to(_lowerCamelCase  )
  
          # forward pass
          with torch.no_grad():
                UpperCAmelCase__ :       int						=						model(**_lowerCamelCase  )
  
          # verify the predicted depth
          UpperCAmelCase__ :       Tuple						=						torch.Size([1, 480, 640]  )
          self.assertEqual(outputs.predicted_depth.shape       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						torch.tensor(
              [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]  ).to(_lowerCamelCase  )
  
          self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]       ,					_lowerCamelCase       ,					atol=1e-4  )  )
  
  
style_context: 
  
  """simple docstring"""
  from __future__ import annotations
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[Any]						=						{1}
        UpperCAmelCase__ :       Optional[int]						=						[1]
        for _ in range(2 ,  depth + 1						):
              UpperCAmelCase__ :       Tuple						=						[*previous_coefficients, 0]
              UpperCAmelCase__ :       List[str]						=						[0, *previous_coefficients]
              UpperCAmelCase__ :       Dict						=						[]
              for x, y in zip(lowerCAmelCase ,  lowerCAmelCase						):
                    coefficients.add(x + y						)
                    previous_coefficients.append(x + y						)
        return coefficients
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[str]						=						set()
        for number in unique_coefficients:
              UpperCAmelCase__ :       int						=						2
              UpperCAmelCase__ :       List[Any]						=						number
              while divisor**2 <= copy_number:
                    UpperCAmelCase__ :       Tuple						=						0
                    while copy_number % divisor == 0:
                          copy_number //= divisor
                          multiplicity += 1
                    if multiplicity >= 2:
                          non_squarefrees.add(lowerCAmelCase						)
                          break
                    divisor += 1
  
        return unique_coefficients.difference(lowerCAmelCase						)
  
  
  
  
  def 				a__	(	lowerCAmelCase = 51						)  ->   int:
        UpperCAmelCase__ :       Any						=						get_pascal_triangle_unique_coefficients(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						get_squarefrees(lowerCAmelCase						)
        return sum(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  
  