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    _snake_case      (  snake_case       ):
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	List[str]			     =  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    _snake_case      :
  
  
  
  
  
  
  
  			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		,     ):
  				__magic_name__	:	int			     =  parent
  				__magic_name__	:	Union[str, Any]			     =  batch_size
  				__magic_name__	:	Any			     =  image_size
  				__magic_name__	:	Tuple			     =  num_channels
  				__magic_name__	:	Union[str, Any]			     =  num_encoder_blocks
  				__magic_name__	:	Tuple			     =  sr_ratios
  				__magic_name__	:	Tuple			     =  depths
  				__magic_name__	:	int			     =  hidden_sizes
  				__magic_name__	:	Optional[int]			     =  downsampling_rates
  				__magic_name__	:	Any			     =  num_attention_heads
  				__magic_name__	:	Tuple			     =  is_training
  				__magic_name__	:	Optional[Any]			     =  use_labels
  				__magic_name__	:	int			     =  hidden_act
  				__magic_name__	:	Any			     =  hidden_dropout_prob
  				__magic_name__	:	List[Any]			     =  attention_probs_dropout_prob
  				__magic_name__	:	Dict			     =  initializer_range
  				__magic_name__	:	str			     =  decoder_hidden_size
  				__magic_name__	:	List[str]			     =  num_labels
  				__magic_name__	:	List[Any]			     =  scope
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Tuple			     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
  
  				__magic_name__	:	int			     =  None
  				if self.use_labels:
  					__magic_name__	:	Union[str, Any]			     =  ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels	)
  
  				__magic_name__	:	int			     =  self.get_config()
  				return config, pixel_values, labels
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a	):
  				__magic_name__	:	Dict			     =  GLPNModel(config=_a	)
  				model.to(_a	)
  				model.eval()
  				__magic_name__	:	Union[str, Any]			     =  model(_a	)
  				__magic_name__	:	List[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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a	):
  				__magic_name__	:	List[Any]			     =  self.num_labels
  				__magic_name__	:	Tuple			     =  GLPNForDepthEstimation(_a	)
  				model.to(_a	)
  				model.eval()
  				__magic_name__	:	Any			     =  model(_a	)
  				self.parent.assertEqual(result.predicted_depth.shape		,     (self.batch_size, self.image_size, self.image_size)	)
  				__magic_name__	:	int			     =  model(_a		,     labels=_a	)
  				self.parent.assertEqual(result.predicted_depth.shape		,     (self.batch_size, self.image_size, self.image_size)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	List[str]			     =  self.prepare_config_and_inputs()
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	str			     =  config_and_inputs
  				__magic_name__	:	Tuple			     =  {"pixel_values": pixel_values}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class    _snake_case      (  snake_case       ,		snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
  			UpperCamelCase__					   =					(
  			    {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
  			)
  
  			UpperCamelCase__					   =					False
  			UpperCamelCase__					   =					False
  			UpperCamelCase__					   =					False
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	int			     =  GLPNModelTester(self	)
  				__magic_name__	:	Union[str, Any]			     =  GLPNConfigTester(self		,     config_class=_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				self.config_tester.run_common_tests()
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Any			     =  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     SCREAMING_SNAKE_CASE					(       self	):
  				pass
  
  
  
  
  
  
  
  			@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"	)
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				pass
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  					__magic_name__	:	str			     =  model_class(_a	)
  					__magic_name__	:	Optional[Any]			     =  inspect.signature(model.forward	)
  					# signature.parameters is an OrderedDict => so arg_names order is deterministic
  					__magic_name__	:	Optional[int]			     =  [*signature.parameters.keys()]
  
  					__magic_name__	:	int			     =  ["pixel_values"]
  					self.assertListEqual(arg_names[:1]		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.model_tester.prepare_config_and_inputs_for_common()
  				__magic_name__	:	Optional[int]			     =  True
  
  				for model_class in self.all_model_classes:
  					__magic_name__	:	List[Any]			     =  True
  					__magic_name__	:	Any			     =  False
  					__magic_name__	:	List[Any]			     =  True
  					__magic_name__	:	Optional[Any]			     =  model_class(_a	)
  					model.to(_a	)
  					model.eval()
  					with torch.no_grad():
  						__magic_name__	:	Optional[Any]			     =  model(**self._prepare_for_class(_a		,     _a	)	)
  					__magic_name__	:	List[str]			     =  outputs.attentions
  
  					__magic_name__	:	Tuple			     =  sum(self.model_tester.depths	)
  					self.assertEqual(len(_a	)		,     _a	)
  
  					# check that output_attentions also work using config
  					del inputs_dict["output_attentions"]
  					__magic_name__	:	int			     =  True
  					__magic_name__	:	Optional[Any]			     =  model_class(_a	)
  					model.to(_a	)
  					model.eval()
  					with torch.no_grad():
  						__magic_name__	:	Tuple			     =  model(**self._prepare_for_class(_a		,     _a	)	)
  					__magic_name__	:	Optional[Any]			     =  outputs.attentions
  
  					self.assertEqual(len(_a	)		,     _a	)
  
  					# verify the first attentions (first block, first layer)
  					__magic_name__	:	int			     =  (self.model_tester.image_size // 4) ** 2
  					__magic_name__	:	Tuple			     =  (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)
  					__magic_name__	:	Optional[int]			     =  (self.model_tester.image_size // 32) ** 2
  					__magic_name__	:	List[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]		,     )
  					__magic_name__	:	int			     =  len(_a	)
  
  					# Check attention is always last and order is fine
  					__magic_name__	:	int			     =  True
  					__magic_name__	:	Any			     =  True
  					__magic_name__	:	List[str]			     =  model_class(_a	)
  					model.to(_a	)
  					model.eval()
  					with torch.no_grad():
  						__magic_name__	:	Optional[int]			     =  model(**self._prepare_for_class(_a		,     _a	)	)
  
  					self.assertEqual(out_len + 1		,     len(_a	)	)
  
  					__magic_name__	:	str			     =  outputs.attentions
  
  					self.assertEqual(len(_a	)		,     _a	)
  					# verify the first attentions (first block, first layer)
  					__magic_name__	:	str			     =  (self.model_tester.image_size // 4) ** 2
  					__magic_name__	:	Optional[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     SCREAMING_SNAKE_CASE					(       self	):
  				def check_hidden_states_output(_a		,     _a		,     _a	):
  					__magic_name__	:	Optional[int]			     =  model_class(_a	)
  					model.to(_a	)
  					model.eval()
  
  					with torch.no_grad():
  						__magic_name__	:	List[str]			     =  model(**self._prepare_for_class(_a		,     _a	)	)
  
  					__magic_name__	:	List[str]			     =  outputs.hidden_states
  
  					__magic_name__	:	Tuple			     =  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,
  					    ]		,     )
  
  				__magic_name__     ,					__magic_name__	:	Tuple			     =  self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  					__magic_name__	:	Tuple			     =  True
  					check_hidden_states_output(_a		,     _a		,     _a	)
  
  					# check that output_hidden_states also work using config
  					del inputs_dict["output_hidden_states"]
  					__magic_name__	:	Dict			     =  True
  
  					check_hidden_states_output(_a		,     _a		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				if not self.model_tester.is_training:
  					return
  
  				__magic_name__     ,					__magic_name__	:	str			     =  self.model_tester.prepare_config_and_inputs_for_common()
  				__magic_name__	:	Union[str, 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":
  						__magic_name__     ,					__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Any			     =  inputs_dict["pixel_values"].shape
  						__magic_name__	:	Union[str, Any]			     =  torch.zeros(
  						    [self.model_tester.batch_size, height, width]		,     device=_a	).long()
  					__magic_name__	:	Union[str, Any]			     =  model_class(_a	)
  					model.to(_a	)
  					model.train()
  					__magic_name__	:	List[Any]			     =  self._prepare_for_class(_a		,     _a		,     return_labels=_a	)
  					__magic_name__	:	str			     =  model(**_a	).loss
  					loss.backward()
  
  
  
  
  
  
  
  			@slow
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  					__magic_name__	:	Union[str, Any]			     =  GLPNModel.from_pretrained(_a	)
  					self.assertIsNotNone(_a	)
  
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	Union[str, Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Tuple			     =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
  	return image
  
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class    _snake_case      (  unittest.TestCase       ):
  
  
  
  
  
  
  
  			@slow
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[int]			     =  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
  				__magic_name__	:	Optional[int]			     =  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_a	)
  
  				__magic_name__	:	Tuple			     =  prepare_img()
  				__magic_name__	:	Optional[Any]			     =  image_processor(images=_a		,     return_tensors="pt"	).to(_a	)
  
  				# forward pass
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	)
  
  				# verify the predicted depth
  				__magic_name__	:	Optional[Any]			     =  torch.Size([1, 480, 640]	)
  				self.assertEqual(outputs.predicted_depth.shape		,     _a	)
  
  				__magic_name__	:	List[Any]			     =  torch.tensor(
  				    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]	).to(_a	)
  
  				self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]		,     _a		,     atol=1e-4	)	)
  
style_context: 
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
  				from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
  				snake_case				:	Any							   =						None
  
  
  snake_case				:	Union[str, Any]							   =						logging.get_logger(__name__)
  
  snake_case				:	Any							   =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  snake_case				:	Optional[Any]							   =						{
      "vocab_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
          ),
      },
      "tokenizer_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
          ),
      },
  }
  
  snake_case				:	Optional[int]							   =						{
      "xlm-roberta-base": 512,
      "xlm-roberta-large": 512,
      "xlm-roberta-large-finetuned-conll02-dutch": 512,
      "xlm-roberta-large-finetuned-conll02-spanish": 512,
      "xlm-roberta-large-finetuned-conll03-english": 512,
      "xlm-roberta-large-finetuned-conll03-german": 512,
  }
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					VOCAB_FILES_NAMES
  			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
  			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			UpperCamelCase__					   =					['input_ids', 'attention_mask']
  			UpperCamelCase__					   =					XLMRobertaTokenizer
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=None		,     _a=None		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<s>"		,     _a="<unk>"		,     _a="<pad>"		,     _a="<mask>"		,     **_a		,     ):
  				# Mask token behave like a normal word, i.e. include the space before it
  				__magic_name__	:	List[str]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else mask_token
  
  				super().__init__(
  				    _a		,     tokenizer_file=_a		,     bos_token=_a		,     eos_token=_a		,     sep_token=_a		,     cls_token=_a		,     unk_token=_a		,     pad_token=_a		,     mask_token=_a		,     **_a		,     )
  
  				__magic_name__	:	Optional[int]			     =  vocab_file
  				__magic_name__	:	List[str]			     =  False if not self.vocab_file else True
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  
  				if token_ids_a is None:
  					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				__magic_name__	:	Union[str, Any]			     =  [self.cls_token_id]
  				__magic_name__	:	str			     =  [self.sep_token_id]
  				return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	List[str]			     =  [self.sep_token_id]
  				__magic_name__	:	List[str]			     =  [self.cls_token_id]
  
  				if token_ids_a is None:
  					return len(cls + token_ids_a + sep	) * [0]
  				return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				if not self.can_save_slow_tokenizer:
  					raise ValueError(
  					    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
  					    "tokenizer."	)
  
  				if not os.path.isdir(_a	):
  					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''	)
  					return
  				__magic_name__	:	Any			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  
  				if os.path.abspath(self.vocab_file	) != os.path.abspath(_a	):
  					copyfile(self.vocab_file		,     _a	)
  
  				return (out_vocab_file,)
  