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 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      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							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A=1_3 ,			A=6_4 ,			A=3 ,			A=4 ,			A=[2, 2, 2, 2] ,			A=[8, 4, 2, 1] ,			A=[1_6, 3_2, 6_4, 1_2_8] ,			A=[1, 4, 8, 1_6] ,			A=[1, 2, 4, 8] ,			A=True ,			A=True ,			A="gelu" ,			A=0.1 ,			A=0.1 ,			A=0.02 ,			A=1_6 ,			A=3 ,			A=None ,			)							->      Dict:
           _UpperCAmelCase							:						Dict       							=					parent
           _UpperCAmelCase							:						Dict       							=					batch_size
           _UpperCAmelCase							:						Tuple       							=					image_size
           _UpperCAmelCase							:						Tuple       							=					num_channels
           _UpperCAmelCase							:						int       							=					num_encoder_blocks
           _UpperCAmelCase							:						Any       							=					sr_ratios
           _UpperCAmelCase							:						str       							=					depths
           _UpperCAmelCase							:						Optional[int]       							=					hidden_sizes
           _UpperCAmelCase							:						str       							=					downsampling_rates
           _UpperCAmelCase							:						Union[str, Any]       							=					num_attention_heads
           _UpperCAmelCase							:						int       							=					is_training
           _UpperCAmelCase							:						Union[str, Any]       							=					use_labels
           _UpperCAmelCase							:						List[Any]       							=					hidden_act
           _UpperCAmelCase							:						Tuple       							=					hidden_dropout_prob
           _UpperCAmelCase							:						str       							=					attention_probs_dropout_prob
           _UpperCAmelCase							:						Optional[Any]       							=					initializer_range
           _UpperCAmelCase							:						Optional[int]       							=					decoder_hidden_size
           _UpperCAmelCase							:						Dict       							=					num_labels
           _UpperCAmelCase							:						int       							=					scope
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[str]:
           _UpperCAmelCase							:						Any       							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
  
           _UpperCAmelCase							:						int       							=					None
           if self.use_labels:
               _UpperCAmelCase							:						List[Any]       							=					ids_tensor([self.batch_size, self.image_size, self.image_size] ,			self.num_labels	)
  
           _UpperCAmelCase							:						int       							=					self.get_config()
           return config, pixel_values, labels
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      int:
           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 				__lowerCAmelCase			(      self ,			A ,			A ,			A	)							->      str:
           _UpperCAmelCase							:						Union[str, Any]       							=					GLPNModel(config=A	)
           model.to(A	)
           model.eval()
           _UpperCAmelCase							:						Optional[int]       							=					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 				__lowerCAmelCase			(      self ,			A ,			A ,			A	)							->      List[str]:
           _UpperCAmelCase							:						Optional[int]       							=					self.num_labels
           _UpperCAmelCase							:						Tuple       							=					GLPNForDepthEstimation(A	)
           model.to(A	)
           model.eval()
           _UpperCAmelCase							:						List[Any]       							=					model(A	)
           self.parent.assertEqual(result.predicted_depth.shape ,			(self.batch_size, self.image_size, self.image_size)	)
           _UpperCAmelCase							:						Dict       							=					model(A ,			labels=A	)
           self.parent.assertEqual(result.predicted_depth.shape ,			(self.batch_size, self.image_size, self.image_size)	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      int:
           _UpperCAmelCase							:						Optional[int]       							=					self.prepare_config_and_inputs()
           _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						List[Any]       							=					config_and_inputs
           _UpperCAmelCase							:						Tuple       							=					{'''pixel_values''': pixel_values}
           return config, inputs_dict
  
  
  
  @require_torch
  class 							_UpperCAmelCase							(				a	,a	,unittest.TestCase   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
       a__             =(
           {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
       )
  
       a__             =False
       a__             =False
       a__             =False
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      str:
           _UpperCAmelCase							:						List[Any]       							=					GLPNModelTester(self	)
           _UpperCAmelCase							:						List[str]       							=					GLPNConfigTester(self ,			config_class=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Union[str, Any]:
           self.config_tester.run_common_tests()
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						Optional[Any]       							=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Tuple:
           _UpperCAmelCase							:						Dict       							=					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 				__lowerCAmelCase			(      self	)							->      List[str]:
           pass
  
  
  
  
  
       @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''	)
       def 				__lowerCAmelCase			(      self	)							->      List[Any]:
           pass
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Tuple:
           _UpperCAmelCase							,       _UpperCAmelCase							:						List[str]       							=					self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
               _UpperCAmelCase							:						Any       							=					model_class(A	)
               _UpperCAmelCase							:						List[str]       							=					inspect.signature(model.forward	)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _UpperCAmelCase							:						Optional[Any]       							=					[*signature.parameters.keys()]
  
               _UpperCAmelCase							:						int       							=					['''pixel_values''']
               self.assertListEqual(arg_names[:1] ,			A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Tuple:
           _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					self.model_tester.prepare_config_and_inputs_for_common()
           _UpperCAmelCase							:						Any       							=					True
  
           for model_class in self.all_model_classes:
               _UpperCAmelCase							:						List[Any]       							=					True
               _UpperCAmelCase							:						List[str]       							=					False
               _UpperCAmelCase							:						Tuple       							=					True
               _UpperCAmelCase							:						Any       							=					model_class(A	)
               model.to(A	)
               model.eval()
               with torch.no_grad():
                   _UpperCAmelCase							:						List[Any]       							=					model(**self._prepare_for_class(A ,			A	)	)
               _UpperCAmelCase							:						List[Any]       							=					outputs.attentions
  
               _UpperCAmelCase							:						List[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							:						Any       							=					True
               _UpperCAmelCase							:						int       							=					model_class(A	)
               model.to(A	)
               model.eval()
               with torch.no_grad():
                   _UpperCAmelCase							:						Dict       							=					model(**self._prepare_for_class(A ,			A	)	)
               _UpperCAmelCase							:						List[Any]       							=					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							:						Optional[int]       							=					(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[Any]       							=					(self.model_tester.image_size // 3_2) ** 2
               _UpperCAmelCase							:						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							:						Any       							=					len(A	)
  
               # Check attention is always last and order is fine
               _UpperCAmelCase							:						List[str]       							=					True
               _UpperCAmelCase							:						Optional[Any]       							=					True
               _UpperCAmelCase							:						Any       							=					model_class(A	)
               model.to(A	)
               model.eval()
               with torch.no_grad():
                   _UpperCAmelCase							:						Tuple       							=					model(**self._prepare_for_class(A ,			A	)	)
  
               self.assertEqual(out_len + 1 ,			len(A	)	)
  
               _UpperCAmelCase							:						Tuple       							=					outputs.attentions
  
               self.assertEqual(len(A	) ,			A	)
               # verify the first attentions (first block, first layer)
               _UpperCAmelCase							:						Tuple       							=					(self.model_tester.image_size // 4) ** 2
               _UpperCAmelCase							:						Optional[int]       							=					(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 				__lowerCAmelCase			(      self	)							->      Dict:
           def check_hidden_states_output(A ,			A ,			A	):
               _UpperCAmelCase							:						Optional[int]       							=					model_class(A	)
               model.to(A	)
               model.eval()
  
               with torch.no_grad():
                   _UpperCAmelCase							:						Optional[Any]       							=					model(**self._prepare_for_class(A ,			A	)	)
  
               _UpperCAmelCase							:						Union[str, Any]       							=					outputs.hidden_states
  
               _UpperCAmelCase							:						Any       							=					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							:						Any       							=					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							:						Union[str, Any]       							=					True
  
               check_hidden_states_output(A ,			A ,			A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[Any]:
           if not self.model_tester.is_training:
               return
  
           _UpperCAmelCase							,       _UpperCAmelCase							:						str       							=					self.model_tester.prepare_config_and_inputs_for_common()
           _UpperCAmelCase							:						Any       							=					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							:						Any       							=					inputs_dict['''pixel_values'''].shape
                   _UpperCAmelCase							:						str       							=					torch.zeros(
                       [self.model_tester.batch_size, height, width] ,			device=A	).long()
               _UpperCAmelCase							:						Tuple       							=					model_class(A	)
               model.to(A	)
               model.train()
               _UpperCAmelCase							:						Dict       							=					self._prepare_for_class(A ,			A ,			return_labels=A	)
               _UpperCAmelCase							:						str       							=					model(**A	).loss
               loss.backward()
  
  
  
  
  
       @slow
       def 				__lowerCAmelCase			(      self	)							->      Any:
           for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase							:						Union[str, Any]       							=					GLPNModel.from_pretrained(A	)
               self.assertIsNotNone(A	)
  
  
  
  
  
  def 				lowerCamelCase_		():
      _UpperCAmelCase							:						Any       							=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image
  
  
  @require_torch
  @require_vision
  @slow
  class 							_UpperCAmelCase							(				unittest.TestCase   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       @slow
       def 				__lowerCAmelCase			(      self	)							->      Tuple:
           _UpperCAmelCase							:						Dict       							=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
           _UpperCAmelCase							:						Union[str, Any]       							=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(A	)
  
           _UpperCAmelCase							:						Optional[int]       							=					prepare_img()
           _UpperCAmelCase							:						Tuple       							=					image_processor(images=A ,			return_tensors='''pt'''	).to(A	)
  
           # forward pass
           with torch.no_grad():
               _UpperCAmelCase							:						Union[str, Any]       							=					model(**A	)
  
           # verify the predicted depth
           _UpperCAmelCase							:						Tuple       							=					torch.Size([1, 4_8_0, 6_4_0]	)
           self.assertEqual(outputs.predicted_depth.shape ,			A	)
  
           _UpperCAmelCase							:						Optional[int]       							=					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(A	)
  
           self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3] ,			A ,			atol=1E-4	)	)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import random
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A ,			A = 0.01 ,			A = 1_0_0_0 ,			A = -1 ,			)							->      None:
           _UpperCAmelCase							:						Dict       							=					sample
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           _UpperCAmelCase							:						Tuple       							=					target
           if len(self.target	) == 0:
               raise ValueError('''Target data can not be empty'''	)
           if len(self.sample	) != len(self.target	):
               raise ValueError('''Sample data and Target data do not have matching lengths'''	)
           _UpperCAmelCase							:						List[Any]       							=					learning_rate
           _UpperCAmelCase							:						Union[str, Any]       							=					epoch_number
           _UpperCAmelCase							:						Any       							=					bias
           _UpperCAmelCase							:						str       							=					len(A	)
           _UpperCAmelCase							:						Tuple       							=					len(sample[0]	)  # number of columns in dataset
           _UpperCAmelCase							:						list       							=					[]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      None:
           for sample in self.sample:
               sample.insert(0 ,			self.bias	)
  
           for _ in range(self.col_sample	):
               self.weight.append(random.random()	)
  
           self.weight.insert(0 ,			self.bias	)
  
           _UpperCAmelCase							:						Tuple       							=					0
  
           while True:
               _UpperCAmelCase							:						Tuple       							=					False
               for i in range(self.number_sample	):
                   _UpperCAmelCase							:						Optional[int]       							=					0
                   for j in range(self.col_sample + 1	):
                       _UpperCAmelCase							:						int       							=					u + self.weight[j] * self.sample[i][j]
                   _UpperCAmelCase							:						Tuple       							=					self.sign(A	)
                   if y != self.target[i]:
                       for j in range(self.col_sample + 1	):
                           _UpperCAmelCase							:						int       							=					(
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                       _UpperCAmelCase							:						Union[str, Any]       							=					True
              # print('Epoch: \n',epoch_count)
               _UpperCAmelCase							:						int       							=					epoch_count + 1
               # if you want control the epoch or just by error
               if not has_misclassified:
                   print(('''\nEpoch:\n''', epoch_count)	)
                   print('''------------------------\n'''	)
                   # if epoch_count > self.epoch_number or not error:
                   break
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      None:
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           sample.insert(0 ,			self.bias	)
           _UpperCAmelCase							:						str       							=					0
           for i in range(self.col_sample + 1	):
               _UpperCAmelCase							:						Optional[Any]       							=					u + self.weight[i] * sample[i]
  
           _UpperCAmelCase							:						Optional[int]       							=					self.sign(A	)
  
           if y == -1:
               print(('''Sample: ''', sample)	)
               print('''classification: P1'''	)
           else:
               print(('''Sample: ''', sample)	)
               print('''classification: P2'''	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      int:
           return 1 if u >= 0 else -1
  
  
  
  _lowerCAmelCase					:Optional[int]  =      [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _lowerCAmelCase					:Dict  =      [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
    import doctest
  
    doctest.testmod()
  
    _lowerCAmelCase					:int  =      Perceptron(
        sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
    )
    network.training()
    print('Finished training perceptron')
    print('Enter values to predict or q to exit')
    while True:
      _lowerCAmelCase					:list  =      []
      for i in range(len(samples[0])):
        _lowerCAmelCase					:List[Any]  =      input('value: ').strip()
        if user_input == "q":
          break
        _lowerCAmelCase					:Any  =      float(user_input)
        sample.insert(i, observation)
      network.sort(sample)
  
  
  