code: 
  
  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       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   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 __UpperCamelCase    :
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=1_3							,			lowerCamelCase__=6_4							,			lowerCamelCase__=3							,			lowerCamelCase__=4							,			lowerCamelCase__=[2, 2, 2, 2]							,			lowerCamelCase__=[8, 4, 2, 1]							,			lowerCamelCase__=[1_6, 3_2, 6_4, 1_2_8]							,			lowerCamelCase__=[1, 4, 8, 1_6]							,			lowerCamelCase__=[1, 2, 4, 8]							,			lowerCamelCase__=True							,			lowerCamelCase__=True							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=0.1							,			lowerCamelCase__=0.1							,			lowerCamelCase__=0.02							,			lowerCamelCase__=1_6							,			lowerCamelCase__=3							,			lowerCamelCase__=None							,			):
  						UpperCAmelCase__:  Dict		       =   parent
  						UpperCAmelCase__:  Dict		       =   batch_size
  						UpperCAmelCase__:  List[Any]		       =   image_size
  						UpperCAmelCase__:  Dict		       =   num_channels
  						UpperCAmelCase__:  Union[str, Any]		       =   num_encoder_blocks
  						UpperCAmelCase__:  List[str]		       =   sr_ratios
  						UpperCAmelCase__:  Optional[int]		       =   depths
  						UpperCAmelCase__:  str		       =   hidden_sizes
  						UpperCAmelCase__:  List[str]		       =   downsampling_rates
  						UpperCAmelCase__:  Optional[Any]		       =   num_attention_heads
  						UpperCAmelCase__:  Optional[Any]		       =   is_training
  						UpperCAmelCase__:  int		       =   use_labels
  						UpperCAmelCase__:  Optional[Any]		       =   hidden_act
  						UpperCAmelCase__:  Dict		       =   hidden_dropout_prob
  						UpperCAmelCase__:  str		       =   attention_probs_dropout_prob
  						UpperCAmelCase__:  int		       =   initializer_range
  						UpperCAmelCase__:  Optional[int]		       =   decoder_hidden_size
  						UpperCAmelCase__:  Dict		       =   num_labels
  						UpperCAmelCase__:  Any		       =   scope
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
  
  						UpperCAmelCase__:  Any		       =   None
  						if self.use_labels:
  							UpperCAmelCase__:  int		       =   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       _UpperCAmelCase						(							self	):
  						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							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Tuple		       =   GLPNModel(config=lowerCamelCase__	)
  						model.to(lowerCamelCase__	)
  						model.eval()
  						UpperCAmelCase__:  Any		       =   model(lowerCamelCase__	)
  						UpperCAmelCase__:  List[str]		       =   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							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Dict		       =   self.num_labels
  						UpperCAmelCase__:  Optional[int]		       =   GLPNForDepthEstimation(lowerCamelCase__	)
  						model.to(lowerCamelCase__	)
  						model.eval()
  						UpperCAmelCase__:  str		       =   model(lowerCamelCase__	)
  						self.parent.assertEqual(result.predicted_depth.shape							,			(self.batch_size, self.image_size, self.image_size)	)
  						UpperCAmelCase__:  Any		       =   model(lowerCamelCase__							,			labels=lowerCamelCase__	)
  						self.parent.assertEqual(result.predicted_depth.shape							,			(self.batch_size, self.image_size, self.image_size)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Union[str, Any]		       =   self.prepare_config_and_inputs()
  						UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   config_and_inputs
  						UpperCAmelCase__:  List[str]		       =   {"pixel_values": pixel_values}
  						return config, inputs_dict
  
  @require_torch
  class __UpperCamelCase    (							_a				,_a				,unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
  					__magic_name__     			=      (
  					    {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
  					)
  
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Union[str, Any]		       =   GLPNModelTester(self	)
  						UpperCAmelCase__:  List[str]		       =   GLPNConfigTester(self							,			config_class=lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						self.config_tester.run_common_tests()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Tuple		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_model(*lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  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       _UpperCAmelCase						(							self	):
  						pass
  
  
  
  
  
  					@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"	)
  					def       _UpperCAmelCase						(							self	):
  						pass
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  							UpperCAmelCase__:  Union[str, Any]		       =   model_class(lowerCamelCase__	)
  							UpperCAmelCase__:  List[Any]		       =   inspect.signature(model.forward	)
  							# signature.parameters is an OrderedDict => so arg_names order is deterministic
  							UpperCAmelCase__:  Any		       =   [*signature.parameters.keys()]
  
  							UpperCAmelCase__:  str		       =   ["pixel_values"]
  							self.assertListEqual(arg_names[:1]							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Any		       =   self.model_tester.prepare_config_and_inputs_for_common()
  						UpperCAmelCase__:  Optional[int]		       =   True
  
  						for model_class in self.all_model_classes:
  							UpperCAmelCase__:  str		       =   True
  							UpperCAmelCase__:  Optional[int]		       =   False
  							UpperCAmelCase__:  Union[str, Any]		       =   True
  							UpperCAmelCase__:  List[Any]		       =   model_class(lowerCamelCase__	)
  							model.to(lowerCamelCase__	)
  							model.eval()
  							with torch.no_grad():
  								UpperCAmelCase__:  List[str]		       =   model(**self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)	)
  							UpperCAmelCase__:  Optional[Any]		       =   outputs.attentions
  
  							UpperCAmelCase__:  Union[str, Any]		       =   sum(self.model_tester.depths	)
  							self.assertEqual(len(lowerCamelCase__	)							,			lowerCamelCase__	)
  
  							# check that output_attentions also work using config
  							del inputs_dict["output_attentions"]
  							UpperCAmelCase__:  Tuple		       =   True
  							UpperCAmelCase__:  List[str]		       =   model_class(lowerCamelCase__	)
  							model.to(lowerCamelCase__	)
  							model.eval()
  							with torch.no_grad():
  								UpperCAmelCase__:  int		       =   model(**self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)	)
  							UpperCAmelCase__:  Dict		       =   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__:  Any		       =   (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__:  int		       =   (self.model_tester.image_size // 3_2) ** 2
  							UpperCAmelCase__:  Tuple		       =   (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__:  Tuple		       =   len(lowerCamelCase__	)
  
  							# Check attention is always last and order is fine
  							UpperCAmelCase__:  str		       =   True
  							UpperCAmelCase__:  int		       =   True
  							UpperCAmelCase__:  Any		       =   model_class(lowerCamelCase__	)
  							model.to(lowerCamelCase__	)
  							model.eval()
  							with torch.no_grad():
  								UpperCAmelCase__:  Union[str, Any]		       =   model(**self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)	)
  
  							self.assertEqual(out_len + 1							,			len(lowerCamelCase__	)	)
  
  							UpperCAmelCase__:  Union[str, Any]		       =   outputs.attentions
  
  							self.assertEqual(len(lowerCamelCase__	)							,			lowerCamelCase__	)
  							# verify the first attentions (first block, first layer)
  							UpperCAmelCase__:  int		       =   (self.model_tester.image_size // 4) ** 2
  							UpperCAmelCase__:  List[str]		       =   (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	):
  						def check_hidden_states_output(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  							UpperCAmelCase__:  int		       =   model_class(lowerCamelCase__	)
  							model.to(lowerCamelCase__	)
  							model.eval()
  
  							with torch.no_grad():
  								UpperCAmelCase__:  Dict		       =   model(**self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)	)
  
  							UpperCAmelCase__:  str		       =   outputs.hidden_states
  
  							UpperCAmelCase__:  Union[str, 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__:  Union[str, Any]		       =   self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  							UpperCAmelCase__:  Any		       =   True
  							check_hidden_states_output(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  							# check that output_hidden_states also work using config
  							del inputs_dict["output_hidden_states"]
  							UpperCAmelCase__:  str		       =   True
  
  							check_hidden_states_output(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						if not self.model_tester.is_training:
  							return
  
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   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__:  int		       =   inputs_dict["pixel_values"].shape
  								UpperCAmelCase__:  Optional[int]		       =   torch.zeros(
  								    [self.model_tester.batch_size, height, width]							,			device=lowerCamelCase__	).long()
  							UpperCAmelCase__:  Optional[Any]		       =   model_class(lowerCamelCase__	)
  							model.to(lowerCamelCase__	)
  							model.train()
  							UpperCAmelCase__:  Any		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
  							UpperCAmelCase__:  Union[str, Any]		       =   model(**lowerCamelCase__	).loss
  							loss.backward()
  
  
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  							UpperCAmelCase__:  List[Any]		       =   GLPNModel.from_pretrained(lowerCamelCase__	)
  							self.assertIsNotNone(lowerCamelCase__	)
  
  
  
  def  _A  (			):
  	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       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
  						UpperCAmelCase__:  List[str]		       =   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(lowerCamelCase__	)
  
  						UpperCAmelCase__:  str		       =   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__:  Any		       =   torch.Size([1, 4_8_0, 6_4_0]	)
  						self.assertEqual(outputs.predicted_depth.shape							,			lowerCamelCase__	)
  
  						UpperCAmelCase__:  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(lowerCamelCase__	)
  
  						self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]							,			lowerCamelCase__							,			atol=1e-4	)	)
style_context: 
  
  import numpy as np
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  List[Any]		       =   np.exp(SCREAMING_SNAKE_CASE			)
  
  	# Add up the all the exponentials
  	UpperCAmelCase__:  str		       =   np.sum(SCREAMING_SNAKE_CASE			)
  
  	# Divide every exponent by the sum of all exponents
  	UpperCAmelCase__:  Tuple		       =   exponent_vector / sum_of_exponents
  
  	return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))