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 _lowercase  (    self     )				->    Optional[Any]:
  				_UpperCamelCase       : Dict    =      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 			UpperCAmelCase				:
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					_snake_case  ,					_snake_case=13  ,					_snake_case=64  ,					_snake_case=3  ,					_snake_case=4  ,					_snake_case=[2, 2, 2, 2]  ,					_snake_case=[8, 4, 2, 1]  ,					_snake_case=[16, 32, 64, 128]  ,					_snake_case=[1, 4, 8, 16]  ,					_snake_case=[1, 2, 4, 8]  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case="gelu"  ,					_snake_case=0.1  ,					_snake_case=0.1  ,					_snake_case=0.02  ,					_snake_case=16  ,					_snake_case=3  ,					_snake_case=None  ,					)				->    Union[str, Any]:
  				_UpperCamelCase       : Union[str, Any]    =      parent
  				_UpperCamelCase       : Dict    =      batch_size
  				_UpperCamelCase       : List[str]    =      image_size
  				_UpperCamelCase       : Any    =      num_channels
  				_UpperCamelCase       : Union[str, Any]    =      num_encoder_blocks
  				_UpperCamelCase       : Optional[int]    =      sr_ratios
  				_UpperCamelCase       : str    =      depths
  				_UpperCamelCase       : List[str]    =      hidden_sizes
  				_UpperCamelCase       : Optional[int]    =      downsampling_rates
  				_UpperCamelCase       : str    =      num_attention_heads
  				_UpperCamelCase       : List[str]    =      is_training
  				_UpperCamelCase       : Optional[Any]    =      use_labels
  				_UpperCamelCase       : Any    =      hidden_act
  				_UpperCamelCase       : int    =      hidden_dropout_prob
  				_UpperCamelCase       : List[Any]    =      attention_probs_dropout_prob
  				_UpperCamelCase       : Optional[Any]    =      initializer_range
  				_UpperCamelCase       : List[str]    =      decoder_hidden_size
  				_UpperCamelCase       : Dict    =      num_labels
  				_UpperCamelCase       : str    =      scope
  		def _lowercase  (    self     )				->    str:
  				_UpperCamelCase       : Union[str, Any]    =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )
  
  				_UpperCamelCase       : Optional[int]    =      None
  				if self.use_labels:
  						_UpperCamelCase       : int    =      ids_tensor([self.batch_size, self.image_size, self.image_size]  ,					self.num_labels     )
  
  				_UpperCamelCase       : List[Any]    =      self.get_config()
  				return config, pixel_values, labels
  		def _lowercase  (    self     )				->    Any:
  				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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Union[str, Any]:
  				_UpperCamelCase       : Tuple    =      GLPNModel(config=_snake_case     )
  				model.to(_snake_case     )
  				model.eval()
  				_UpperCamelCase       : List[Any]    =      model(_snake_case     )
  				_UpperCamelCase       : Union[str, 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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Any:
  				_UpperCamelCase       : str    =      self.num_labels
  				_UpperCamelCase       : List[Any]    =      GLPNForDepthEstimation(_snake_case     )
  				model.to(_snake_case     )
  				model.eval()
  				_UpperCamelCase       : str    =      model(_snake_case     )
  				self.parent.assertEqual(result.predicted_depth.shape  ,					(self.batch_size, self.image_size, self.image_size)     )
  				_UpperCamelCase       : Optional[int]    =      model(_snake_case  ,					labels=_snake_case     )
  				self.parent.assertEqual(result.predicted_depth.shape  ,					(self.batch_size, self.image_size, self.image_size)     )
  
  
  
  
  		def _lowercase  (    self     )				->    Any:
  				_UpperCamelCase       : Dict    =      self.prepare_config_and_inputs()
  				_UpperCamelCase,							_UpperCamelCase,							_UpperCamelCase       : Union[str, Any]    =      config_and_inputs
  				_UpperCamelCase       : List[Any]    =      {'''pixel_values''': pixel_values}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 			UpperCAmelCase				(					a_  ,		a_  ,		unittest.TestCase ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   List[Any]          =    (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
  		A__  :   Union[str, Any]          =    (
  		    {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
  		)
  
  		A__  :   Optional[Any]          =    False
  		A__  :   Tuple          =    False
  		A__  :   str          =    False
  		def _lowercase  (    self     )				->    int:
  				_UpperCamelCase       : Optional[Any]    =      GLPNModelTester(self     )
  				_UpperCamelCase       : Any    =      GLPNConfigTester(self  ,					config_class=_snake_case     )
  		def _lowercase  (    self     )				->    str:
  				self.config_tester.run_common_tests()
  		def _lowercase  (    self     )				->    Tuple:
  				_UpperCamelCase       : Union[str, Any]    =      self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*_snake_case     )
  		def _lowercase  (    self     )				->    Optional[int]:
  				_UpperCamelCase       : List[Any]    =      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 _lowercase  (    self     )				->    Tuple:
  				pass
  		@unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''     )
  		def _lowercase  (    self     )				->    Optional[int]:
  				pass
  		def _lowercase  (    self     )				->    Any:
  				_UpperCamelCase,							_UpperCamelCase       : Optional[Any]    =      self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						_UpperCamelCase       : Any    =      model_class(_snake_case     )
  						_UpperCamelCase       : Optional[int]    =      inspect.signature(model.forward     )
  						# signature.parameters is an OrderedDict => so arg_names order is deterministic
  						_UpperCamelCase       : Dict    =      [*signature.parameters.keys()]
  
  						_UpperCamelCase       : Dict    =      ['''pixel_values''']
  						self.assertListEqual(arg_names[:1]  ,					_snake_case     )
  		def _lowercase  (    self     )				->    int:
  				_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       : Optional[int]    =      True
  						_UpperCamelCase       : Tuple    =      False
  						_UpperCamelCase       : str    =      True
  						_UpperCamelCase       : List[str]    =      model_class(_snake_case     )
  						model.to(_snake_case     )
  						model.eval()
  						with torch.no_grad():
  								_UpperCamelCase       : Optional[Any]    =      model(**self._prepare_for_class(_snake_case  ,					_snake_case     )     )
  						_UpperCamelCase       : List[Any]    =      outputs.attentions
  
  						_UpperCamelCase       : List[Any]    =      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"]
  						_UpperCamelCase       : Optional[Any]    =      True
  						_UpperCamelCase       : Dict    =      model_class(_snake_case     )
  						model.to(_snake_case     )
  						model.eval()
  						with torch.no_grad():
  								_UpperCamelCase       : Union[str, Any]    =      model(**self._prepare_for_class(_snake_case  ,					_snake_case     )     )
  						_UpperCamelCase       : Union[str, Any]    =      outputs.attentions
  
  						self.assertEqual(len(_snake_case     )  ,					_snake_case     )
  
  						# verify the first attentions (first block, first layer)
  						_UpperCamelCase       : int    =      (self.model_tester.image_size // 4) ** 2
  						_UpperCamelCase       : List[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       : Optional[Any]    =      (self.model_tester.image_size // 32) ** 2
  						_UpperCamelCase       : str    =      (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       : Tuple    =      len(_snake_case     )
  
  						# Check attention is always last and order is fine
  						_UpperCamelCase       : List[Any]    =      True
  						_UpperCamelCase       : List[str]    =      True
  						_UpperCamelCase       : Union[str, Any]    =      model_class(_snake_case     )
  						model.to(_snake_case     )
  						model.eval()
  						with torch.no_grad():
  								_UpperCamelCase       : Optional[int]    =      model(**self._prepare_for_class(_snake_case  ,					_snake_case     )     )
  
  						self.assertEqual(out_len + 1  ,					len(_snake_case     )     )
  
  						_UpperCamelCase       : int    =      outputs.attentions
  
  						self.assertEqual(len(_snake_case     )  ,					_snake_case     )
  						# verify the first attentions (first block, first layer)
  						_UpperCamelCase       : List[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(self_attentions[0].shape[-3:]     )  ,					[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]  ,					)
  		def _lowercase  (    self     )				->    str:
  				def check_hidden_states_output(_snake_case  ,					_snake_case  ,					_snake_case     ):
  						_UpperCamelCase       : List[Any]    =      model_class(_snake_case     )
  						model.to(_snake_case     )
  						model.eval()
  
  						with torch.no_grad():
  								_UpperCamelCase       : List[str]    =      model(**self._prepare_for_class(_snake_case  ,					_snake_case     )     )
  
  						_UpperCamelCase       : str    =      outputs.hidden_states
  
  						_UpperCamelCase       : Optional[Any]    =      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,
  						    ]  ,					)
  
  				_UpperCamelCase,							_UpperCamelCase       : int    =      self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  						_UpperCamelCase       : Any    =      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"]
  						_UpperCamelCase       : List[str]    =      True
  
  						check_hidden_states_output(_snake_case  ,					_snake_case  ,					_snake_case     )
  		def _lowercase  (    self     )				->    Dict:
  				if not self.model_tester.is_training:
  						return
  
  				_UpperCamelCase,							_UpperCamelCase       : int    =      self.model_tester.prepare_config_and_inputs_for_common()
  				_UpperCamelCase       : int    =      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":
  								_UpperCamelCase,							_UpperCamelCase,							_UpperCamelCase,							_UpperCamelCase       : List[Any]    =      inputs_dict['''pixel_values'''].shape
  								_UpperCamelCase       : Optional[int]    =      torch.zeros(
  								    [self.model_tester.batch_size, height, width]  ,					device=_snake_case     ).long()
  						_UpperCamelCase       : int    =      model_class(_snake_case     )
  						model.to(_snake_case     )
  						model.train()
  						_UpperCamelCase       : List[Any]    =      self._prepare_for_class(_snake_case  ,					_snake_case  ,					return_labels=_snake_case     )
  						_UpperCamelCase       : Tuple    =      model(**_snake_case     ).loss
  						loss.backward()
  
  
  
  
  		@slow
  		def _lowercase  (    self     )				->    Union[str, Any]:
  				for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  						_UpperCamelCase       : Dict    =      GLPNModel.from_pretrained(_snake_case     )
  						self.assertIsNotNone(_snake_case     )
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      Any:
  		_UpperCamelCase       : Dict    =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  		return image
  
  
  @require_torch
  @require_vision
  @slow
  class 			UpperCAmelCase				(					unittest.TestCase ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		@slow
  		def _lowercase  (    self     )				->    Dict:
  				_UpperCamelCase       : Optional[int]    =      GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
  				_UpperCamelCase       : Optional[int]    =      GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(_snake_case     )
  
  				_UpperCamelCase       : Any    =      prepare_img()
  				_UpperCamelCase       : str    =      image_processor(images=_snake_case  ,					return_tensors='''pt'''     ).to(_snake_case     )
  
  				# forward pass
  				with torch.no_grad():
  						_UpperCamelCase       : str    =      model(**_snake_case     )
  
  				# verify the predicted depth
  				_UpperCamelCase       : Dict    =      torch.Size([1, 480, 640]     )
  				self.assertEqual(outputs.predicted_depth.shape  ,					_snake_case     )
  
  				_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(_snake_case     )
  
  				self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]  ,					_snake_case  ,					atol=1E-4     )     )
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  _UpperCAmelCase   :      List[Any] 						=  logging.get_logger(__name__)
  
  _UpperCAmelCase   :      List[Any] 						=  {
      """nghuyong/ernie-1.0-base-zh""": """https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json""",
      """nghuyong/ernie-2.0-base-en""": """https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json""",
      """nghuyong/ernie-2.0-large-en""": """https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json""",
      """nghuyong/ernie-3.0-base-zh""": """https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-medium-zh""": """https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-mini-zh""": """https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-micro-zh""": """https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-nano-zh""": """https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json""",
      """nghuyong/ernie-gram-zh""": """https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json""",
      """nghuyong/ernie-health-zh""": """https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json""",
  }
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   str          =    'ernie'
  		def __init__(    self  ,					_snake_case=30522  ,					_snake_case=768  ,					_snake_case=12  ,					_snake_case=12  ,					_snake_case=3072  ,					_snake_case="gelu"  ,					_snake_case=0.1  ,					_snake_case=0.1  ,					_snake_case=512  ,					_snake_case=2  ,					_snake_case=3  ,					_snake_case=False  ,					_snake_case=0.02  ,					_snake_case=1E-12  ,					_snake_case=0  ,					_snake_case="absolute"  ,					_snake_case=True  ,					_snake_case=None  ,					**_snake_case  ,					)				->    List[Any]:
  				super().__init__(pad_token_id=_snake_case  ,					**_snake_case     )
  
  				_UpperCamelCase       : Dict    =      vocab_size
  				_UpperCamelCase       : int    =      hidden_size
  				_UpperCamelCase       : List[Any]    =      num_hidden_layers
  				_UpperCamelCase       : Tuple    =      num_attention_heads
  				_UpperCamelCase       : List[str]    =      hidden_act
  				_UpperCamelCase       : Optional[int]    =      intermediate_size
  				_UpperCamelCase       : Tuple    =      hidden_dropout_prob
  				_UpperCamelCase       : Optional[int]    =      attention_probs_dropout_prob
  				_UpperCamelCase       : List[str]    =      max_position_embeddings
  				_UpperCamelCase       : Tuple    =      type_vocab_size
  				_UpperCamelCase       : Union[str, Any]    =      task_type_vocab_size
  				_UpperCamelCase       : Union[str, Any]    =      use_task_id
  				_UpperCamelCase       : Any    =      initializer_range
  				_UpperCamelCase       : Optional[int]    =      layer_norm_eps
  				_UpperCamelCase       : str    =      position_embedding_type
  				_UpperCamelCase       : Any    =      use_cache
  				_UpperCamelCase       : str    =      classifier_dropout
  
  
  
  
  
  
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		@property
  		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
  				if self.task == "multiple-choice":
  						_UpperCamelCase       : str    =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
  				else:
  						_UpperCamelCase       : Dict    =      {0: '''batch''', 1: '''sequence'''}
  				return OrderedDict(
  				    [
  				        ('''input_ids''', dynamic_axis),
  				        ('''attention_mask''', dynamic_axis),
  				        ('''token_type_ids''', dynamic_axis),
  				        ('''task_type_ids''', dynamic_axis),
  				    ]     )
  
  
  
  
  
  