"""simple docstring"""







import random






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = 0.01 ,				_SCREAMING_SNAKE_CASE = 1000 ,				_SCREAMING_SNAKE_CASE = -1 ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					sample
      if len(self.sample	) == 0:
         raise ValueError('Sample data can not be empty'	)
      __lowerCamelCase    		=					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'	)
      __lowerCamelCase    		=					learning_rate
      __lowerCamelCase    		=					epoch_number
      __lowerCamelCase    		=					bias
      __lowerCamelCase    		=					len(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					len(sample[0]	)  # number of columns in dataset
      __lowerCamelCase    		=					[]







   def    _A				(	self	):


      '''simple docstring'''

      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	)

      __lowerCamelCase    		=					0

      while True:
         __lowerCamelCase    		=					False
         for i in range(self.number_sample	):
            __lowerCamelCase    		=					0
            for j in range(self.col_sample + 1	):
               __lowerCamelCase    		=					u + self.weight[j] * self.sample[i][j]
            __lowerCamelCase    		=					self.sign(_SCREAMING_SNAKE_CASE	)
            if y != self.target[i]:
               for j in range(self.col_sample + 1	):
                  __lowerCamelCase    		=					(
                      self.weight[j]
                      + self.learning_rate
                      * (self.target[i] - y)
                      * self.sample[i][j]
                  )
               __lowerCamelCase    		=					True
            # print('Epoch: \n',epoch_count)
         __lowerCamelCase    		=					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    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      if len(self.sample	) == 0:
         raise ValueError('Sample data can not be empty'	)
      sample.insert(0 ,				self.bias	)
      __lowerCamelCase    		=					0
      for i in range(self.col_sample + 1	):
         __lowerCamelCase    		=					u + self.weight[i] * sample[i]

      __lowerCamelCase    		=					self.sign(_SCREAMING_SNAKE_CASE	)

      if y == -1:
         print(('Sample: ', sample)	)
         print('classification: P1'	)
      else:
         print(('Sample: ', sample)	)
         print('classification: P2'	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return 1 if u >= 0 else -1

_SCREAMING_SNAKE_CASE	:		Tuple   =   [
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

_SCREAMING_SNAKE_CASE	:		List[str]   =   [
    -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()

     _SCREAMING_SNAKE_CASE	:		int   =   Perceptron(
         sample=samples, target=target, learning_rate=0.0_1, epoch_number=1_0_0_0, bias=-1
     )
     network.training()
     print('''Finished training perceptron''')
     print('''Enter values to predict or q to exit''')
     while True:
          _SCREAMING_SNAKE_CASE	:		list   =   []
          for i in range(len(samples[0])):
               _SCREAMING_SNAKE_CASE	:		Optional[int]   =   input('''value: ''').strip()
               if user_input == "q":
                    break
               _SCREAMING_SNAKE_CASE	:		Optional[Any]   =   float(user_input)
               sample.insert(i, observation)
          network.sort(sample)







"""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      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.config_class(**self.inputs_dict	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'hidden_sizes'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_attention_heads'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_encoder_blocks'	)	)






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=64 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=[2, 2, 2, 2] ,				_SCREAMING_SNAKE_CASE=[8, 4, 2, 1] ,				_SCREAMING_SNAKE_CASE=[16, 32, 64, 128] ,				_SCREAMING_SNAKE_CASE=[1, 4, 8, 16] ,				_SCREAMING_SNAKE_CASE=[1, 2, 4, 8] ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=None ,				):


      '''simple docstring'''

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







   def    _A				(	self	):


      '''simple docstring'''

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

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

      __lowerCamelCase    		=					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 ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					__lowerCamelCase    		=					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 ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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







   def    _A				(	self	):


      '''simple docstring'''

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






@require_torch
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



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

   __UpperCAmelCase	:							Optional[Any]    =    False
   __UpperCAmelCase	:							Optional[int]    =    False
   __UpperCAmelCase	:							int    =    False







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModelTester(self	)
      __lowerCamelCase    		=					GLPNConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE	)







   @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'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

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

         __lowerCamelCase    		=					['pixel_values']
         self.assertListEqual(arg_names[:1] ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

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

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

         __lowerCamelCase    		=					sum(self.model_tester.depths	)
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

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

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

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

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

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

         __lowerCamelCase    		=					outputs.attentions

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)
         # verify the first attentions (first block, first layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 4) ** 2
         __lowerCamelCase    		=					(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(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()

         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)

         __lowerCamelCase    		=					outputs.hidden_states

         __lowerCamelCase    		=					self.model_tester.num_encoder_blocks
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_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,
             ] ,				)

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					True
         check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

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

         check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      if not self.model_tester.is_training:
         return

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

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







   @slow
   def    _A				(	self	):


      '''simple docstring'''

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



def       A__    (	)  ->							Union[str, Any]:
   __lowerCamelCase    		=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
   return image






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



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
      __lowerCamelCase    		=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					prepare_img()
      __lowerCamelCase    		=					image_processor(images=_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).to(_SCREAMING_SNAKE_CASE	)

      # forward pass
      with torch.no_grad():
         __lowerCamelCase    		=					model(**_SCREAMING_SNAKE_CASE	)

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

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

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






