



'''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      (  A__						):



 '''simple docstring'''







 def  a__  (self	)   ->					Union[str, Any]:


      """simple docstring"""



      _a     =  self.config_class(**self.inputs_dict	)
      self.parent.assertTrue(hasattr(snake_case_			,       '''hidden_sizes'''	)	)
      self.parent.assertTrue(hasattr(snake_case_			,       '''num_attention_heads'''	)	)
      self.parent.assertTrue(hasattr(snake_case_			,       '''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, 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			,       )   ->					Any:


      """simple docstring"""



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



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



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

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

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



 def  a__  (self	)   ->					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	)   ->					str:


      """simple docstring"""



      _a     =  GLPNModel(config=snake_case_	)
      model.to(snake_case_	)
      model.eval()
      _a     =  model(snake_case_	)
      _a     =  _a     =  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	)   ->					Dict:


      """simple docstring"""



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



 def  a__  (self	)   ->					Any:


      """simple docstring"""



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






@require_torch
class __A      (  A__       ,       A__       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	Union[str, Any]										=	(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 __lowerCamelCase   :	Union[str, Any]										=	(
     {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
 )

 __lowerCamelCase   :	Union[str, Any]										=	False
 __lowerCamelCase   :	Any										=	False
 __lowerCamelCase   :	Union[str, Any]										=	False



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


      """simple docstring"""



      _a     =  GLPNModelTester(self	)
      _a     =  GLPNConfigTester(self			,       config_class=snake_case_	)



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_	)



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


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*snake_case_	)



 @unittest.skip('''GLPN does not use inputs_embeds'''	)
 def  a__  (self	)   ->					int:


      """simple docstring"""



      pass



 @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''	)
 def  a__  (self	)   ->					Any:


      """simple docstring"""



      pass



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


      """simple docstring"""



      _a  ,   _a     =  self.model_tester.prepare_config_and_inputs_for_common()

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

           _a     =  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]			,       snake_case_	)



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


      """simple docstring"""



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

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

           _a     =  sum(self.model_tester.depths	)
           self.assertEqual(len(snake_case_	)			,       snake_case_	)

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

           self.assertEqual(len(snake_case_	)			,       snake_case_	)

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

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

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

           _a     =  outputs.attentions

           self.assertEqual(len(snake_case_	)			,       snake_case_	)
           # verify the first attentions (first block, first layer)
           _a     =  (self.model_tester.image_size // 4) ** 2
           _a     =  (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[Any]:


      """simple docstring"""



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

           with torch.no_grad():
                _a     =  model(**self._prepare_for_class(snake_case_			,       snake_case_	)	)

           _a     =  outputs.hidden_states

           _a     =  self.model_tester.num_encoder_blocks
           self.assertEqual(len(snake_case_	)			,       snake_case_	)

           # 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,
               ]			,       )

      _a  ,   _a     =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a     =  True
           check_hidden_states_output(snake_case_			,       snake_case_			,       snake_case_	)

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

           check_hidden_states_output(snake_case_			,       snake_case_			,       snake_case_	)



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


      """simple docstring"""



      if not self.model_tester.is_training:
           return

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

      for model_class in self.all_model_classes:
           if model_class in get_values(snake_case_	):
                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":
                _a  ,   _a  ,   _a  ,   _a     =  inputs_dict['''pixel_values'''].shape
                _a     =  torch.zeros(
                    [self.model_tester.batch_size, height, width]			,       device=snake_case_	).long()
           _a     =  model_class(snake_case_	)
           model.to(snake_case_	)
           model.train()
           _a     =  self._prepare_for_class(snake_case_			,       snake_case_			,       return_labels=snake_case_	)
           _a     =  model(**snake_case_	).loss
           loss.backward()



 @slow
 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



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




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image






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



 '''simple docstring'''







 @slow
 def  a__  (self	)   ->					Any:


      """simple docstring"""



      _a     =  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
      _a     =  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(snake_case_	)

      _a     =  prepare_img()
      _a     =  image_processor(images=snake_case_			,       return_tensors='''pt'''	).to(snake_case_	)

      # forward pass
      with torch.no_grad():
           _a     =  model(**snake_case_	)

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

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

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








'''simple docstring'''





from math import gcd




def lowerCAmelCase  (__A = 12_000):



     """simple docstring"""



     _a     =  0
     for d in range(max_d + 1):
          for n in range(d // 3 + 1  ,     (d + 1) // 2):
               if gcd(__A  ,     __A) == 1:
                    fractions_number += 1
     return fractions_number


if __name__ == "__main__":
  print(F"""{solution() = }""")



