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      __lowercase	(	__snake_case					):
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> List[Any]:
  							_lowercase					:  List[str]    			=      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      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   int			,  snake_case							:   Union[str, Any]			,  snake_case							:   Union[str, Any]=13			,  snake_case							:   Tuple=64			,  snake_case							:   Optional[Any]=3			,  snake_case							:   Tuple=4			,  snake_case							:   Dict=[2, 2, 2, 2]			,  snake_case							:   Union[str, Any]=[8, 4, 2, 1]			,  snake_case							:   Dict=[16, 32, 64, 128]			,  snake_case							:   Optional[int]=[1, 4, 8, 16]			,  snake_case							:   Any=[1, 2, 4, 8]			,  snake_case							:   Optional[Any]=True			,  snake_case							:   str=True			,  snake_case							:   Any="gelu"			,  snake_case							:   int=0.1			,  snake_case							:   List[str]=0.1			,  snake_case							:   str=0.02			,  snake_case							:   List[str]=16			,  snake_case							:   int=3			,  snake_case							:   List[str]=None			,  )   -> Optional[Any]:
  							_lowercase					:  List[Any]    			=      parent
  							_lowercase					:  Any    			=      batch_size
  							_lowercase					:  str    			=      image_size
  							_lowercase					:  Any    			=      num_channels
  							_lowercase					:  List[Any]    			=      num_encoder_blocks
  							_lowercase					:  Any    			=      sr_ratios
  							_lowercase					:  List[Any]    			=      depths
  							_lowercase					:  Optional[int]    			=      hidden_sizes
  							_lowercase					:  Optional[Any]    			=      downsampling_rates
  							_lowercase					:  int    			=      num_attention_heads
  							_lowercase					:  List[Any]    			=      is_training
  							_lowercase					:  List[str]    			=      use_labels
  							_lowercase					:  List[str]    			=      hidden_act
  							_lowercase					:  Union[str, Any]    			=      hidden_dropout_prob
  							_lowercase					:  str    			=      attention_probs_dropout_prob
  							_lowercase					:  Dict    			=      initializer_range
  							_lowercase					:  Dict    			=      decoder_hidden_size
  							_lowercase					:  List[Any]    			=      num_labels
  							_lowercase					:  str    			=      scope
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> Optional[int]:
  							_lowercase					:  Tuple    			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
  
  							_lowercase					:  List[Any]    			=      None
  							if self.use_labels:
  													_lowercase					:  Optional[int]    			=      ids_tensor([self.batch_size, self.image_size, self.image_size]			,  self.num_labels						)
  
  							_lowercase					:  Optional[int]    			=      self.get_config()
  							return config, pixel_values, labels
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> Union[str, 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 			_a(self							:   Optional[Any]			,  snake_case							:   List[str]			,  snake_case							:   int			,  snake_case							:   List[Any]						)   -> Union[str, Any]:
  							_lowercase					:  Any    			=      GLPNModel(config=snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  List[str]    			=      model(snake_case						)
  							_lowercase					:  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 			_a(self							:   Union[str, Any]			,  snake_case							:   Dict			,  snake_case							:   int			,  snake_case							:   List[Any]						)   -> Optional[int]:
  							_lowercase					:  Any    			=      self.num_labels
  							_lowercase					:  Dict    			=      GLPNForDepthEstimation(snake_case						)
  							model.to(snake_case						)
  							model.eval()
  							_lowercase					:  Union[str, Any]    			=      model(snake_case						)
  							self.parent.assertEqual(result.predicted_depth.shape			,  (self.batch_size, self.image_size, self.image_size)						)
  							_lowercase					:  Tuple    			=      model(snake_case			,  labels=snake_case						)
  							self.parent.assertEqual(result.predicted_depth.shape			,  (self.batch_size, self.image_size, self.image_size)						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> Optional[int]:
  							_lowercase					:  str    			=      self.prepare_config_and_inputs()
  							_lowercase			,						_lowercase			,						_lowercase					:  List[Any]    			=      config_and_inputs
  							_lowercase					:  Tuple    			=      {"pixel_values": pixel_values}
  							return config, inputs_dict
  
  
  
  
  @require_torch
  class      __lowercase	(	__snake_case      ,   __snake_case      ,   unittest.TestCase					):
  	_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 			_a(self							:   Tuple						)   -> Dict:
  							_lowercase					:  List[str]    			=      GLPNModelTester(self						)
  							_lowercase					:  Dict    			=      GLPNConfigTester(self			,  config_class=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> List[Any]:
  							self.config_tester.run_common_tests()
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> Any:
  							_lowercase					:  List[Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Any						)   -> Optional[int]:
  							_lowercase					:  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 			_a(self							:   int						)   -> Optional[int]:
  							pass
  
  
  
  
  
  
  	@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"						)
  	def 			_a(self							:   Dict						)   -> int:
  							pass
  
  
  
  
  
  
  	def 			_a(self							:   int						)   -> Union[str, Any]:
  							_lowercase			,						_lowercase					:  Optional[Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
  							for model_class in self.all_model_classes:
  													_lowercase					:  int    			=      model_class(snake_case						)
  													_lowercase					:  List[str]    			=      inspect.signature(model.forward						)
  													# signature.parameters is an OrderedDict => so arg_names order is deterministic
  													_lowercase					:  Union[str, Any]    			=      [*signature.parameters.keys()]
  
  													_lowercase					:  List[str]    			=      ["pixel_values"]
  													self.assertListEqual(arg_names[:1]			,  snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]						)   -> Optional[int]:
  							_lowercase			,						_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()
  							_lowercase					:  str    			=      True
  
  							for model_class in self.all_model_classes:
  													_lowercase					:  Union[str, Any]    			=      True
  													_lowercase					:  List[Any]    			=      False
  													_lowercase					:  int    			=      True
  													_lowercase					:  Union[str, Any]    			=      model_class(snake_case						)
  													model.to(snake_case						)
  													model.eval()
  													with torch.no_grad():
  																			_lowercase					:  Optional[int]    			=      model(**self._prepare_for_class(snake_case			,  snake_case						)						)
  													_lowercase					:  Union[str, Any]    			=      outputs.attentions
  
  													_lowercase					:  Optional[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"]
  													_lowercase					:  List[Any]    			=      True
  													_lowercase					:  int    			=      model_class(snake_case						)
  													model.to(snake_case						)
  													model.eval()
  													with torch.no_grad():
  																			_lowercase					:  List[Any]    			=      model(**self._prepare_for_class(snake_case			,  snake_case						)						)
  													_lowercase					:  List[str]    			=      outputs.attentions
  
  													self.assertEqual(len(snake_case						)			,  snake_case						)
  
  													# verify the first attentions (first block, first layer)
  													_lowercase					:  Optional[Any]    			=      (self.model_tester.image_size // 4) ** 2
  													_lowercase					:  List[str]    			=      (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)
  													_lowercase					:  Dict    			=      (self.model_tester.image_size // 32) ** 2
  													_lowercase					:  Optional[Any]    			=      (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]			,  )
  													_lowercase					:  Tuple    			=      len(snake_case						)
  
  													# Check attention is always last and order is fine
  													_lowercase					:  Optional[Any]    			=      True
  													_lowercase					:  Optional[Any]    			=      True
  													_lowercase					:  int    			=      model_class(snake_case						)
  													model.to(snake_case						)
  													model.eval()
  													with torch.no_grad():
  																			_lowercase					:  int    			=      model(**self._prepare_for_class(snake_case			,  snake_case						)						)
  
  													self.assertEqual(out_len + 1			,  len(snake_case						)						)
  
  													_lowercase					:  Tuple    			=      outputs.attentions
  
  													self.assertEqual(len(snake_case						)			,  snake_case						)
  													# verify the first attentions (first block, first layer)
  													_lowercase					:  List[str]    			=      (self.model_tester.image_size // 4) ** 2
  													_lowercase					:  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 			_a(self							:   Optional[int]						)   -> Union[str, Any]:
  							def check_hidden_states_output(snake_case							:   Optional[Any]			,  snake_case							:   Tuple			,  snake_case							:   Optional[Any]						):
  													_lowercase					:  List[str]    			=      model_class(snake_case						)
  													model.to(snake_case						)
  													model.eval()
  
  													with torch.no_grad():
  																			_lowercase					:  Tuple    			=      model(**self._prepare_for_class(snake_case			,  snake_case						)						)
  
  													_lowercase					:  List[Any]    			=      outputs.hidden_states
  
  													_lowercase					:  int    			=      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,
  													    ]			,  )
  
  							_lowercase			,						_lowercase					:  Any    			=      self.model_tester.prepare_config_and_inputs_for_common()
  
  							for model_class in self.all_model_classes:
  													_lowercase					:  List[str]    			=      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"]
  													_lowercase					:  str    			=      True
  
  													check_hidden_states_output(snake_case			,  snake_case			,  snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]						)   -> Any:
  							if not self.model_tester.is_training:
  													return
  
  							_lowercase			,						_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()
  							_lowercase					:  List[Any]    			=      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":
  																			_lowercase			,						_lowercase			,						_lowercase			,						_lowercase					:  Any    			=      inputs_dict["pixel_values"].shape
  																			_lowercase					:  Optional[Any]    			=      torch.zeros(
  																			    [self.model_tester.batch_size, height, width]			,  device=snake_case						).long()
  													_lowercase					:  int    			=      model_class(snake_case						)
  													model.to(snake_case						)
  													model.train()
  													_lowercase					:  Tuple    			=      self._prepare_for_class(snake_case			,  snake_case			,  return_labels=snake_case						)
  													_lowercase					:  int    			=      model(**snake_case						).loss
  													loss.backward()
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   Union[str, Any]						)   -> Tuple:
  							for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  													_lowercase					:  int    			=      GLPNModel.from_pretrained(snake_case						)
  													self.assertIsNotNone(snake_case						)
  
  
  
  def 		UpperCamelCase    (					)      -> Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Any    			=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
  						return image
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   Any						)   -> Optional[Any]:
  							_lowercase					:  Tuple    			=      GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
  							_lowercase					:  List[Any]    			=      GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(snake_case						)
  
  							_lowercase					:  Dict    			=      prepare_img()
  							_lowercase					:  List[str]    			=      image_processor(images=snake_case			,  return_tensors="pt"						).to(snake_case						)
  
  							# forward pass
  							with torch.no_grad():
  													_lowercase					:  Dict    			=      model(**snake_case						)
  
  							# verify the predicted depth
  							_lowercase					:  Tuple    			=      torch.Size([1, 480, 640]						)
  							self.assertEqual(outputs.predicted_depth.shape			,  snake_case						)
  
  							_lowercase					:  Tuple    			=      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(snake_case						)
  
  							self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]			,  snake_case			,  atol=1e-4						)						)
  
  
  
  
  
style_context: 
  from dataclasses import dataclass
  
  UpperCamelCase_	:    List[str]  					=       """0.1"""
  UpperCamelCase_	:    Optional[Any]  					=       """Lucia Harcekova"""
  
  
  
  
  @dataclass
  class      __lowercase	:
  	_A   			=      42
  	_A   			=      42
  	_A   			=      42
  
  
  
  
  
  
  	def __repr__(self							:   List[str]						)   -> str:
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   Union[str, Any]			,  snake_case							:   int = 13			,  snake_case							:   int = 6						)   -> None:
  							_lowercase					:  int    			=      window_size
  							_lowercase					:  List[Any]    			=      lookahead_buffer_size
  							_lowercase					:  str    			=      self.window_size - self.lookahead_buffer_size
  
  
  
  
  
  
  	def 			_a(self							:   List[str]			,  snake_case							:   str						)   -> list[Token]:
  							_lowercase					:  List[Any]    			=      []
  							_lowercase					:  str    			=      ""
  
  							# while there are still characters in text to compress
  							while text:
  													# find the next encoding phrase
  													# - triplet with offset, length, indicator (the next encoding character)
  													_lowercase					:  Union[str, Any]    			=      self._find_encoding_token(snake_case			,  snake_case						)
  
  													# update the search buffer:
  													# - add new characters from text into it
  													# - check if size exceed the max search buffer size, if so, drop the
  													#   oldest elements
  													search_buffer += text[: token.length + 1]
  													if len(snake_case						) > self.search_buffer_size:
  																			_lowercase					:  Optional[int]    			=      search_buffer[-self.search_buffer_size :]
  
  													# update the text
  													_lowercase					:  str    			=      text[token.length + 1 :]
  
  													# append the token to output
  													output.append(snake_case						)
  
  							return output
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   list[Token]						)   -> str:
  							_lowercase					:  Optional[int]    			=      ""
  
  							for token in tokens:
  													for _ in range(token.length						):
  																			output += output[-token.offset]
  													output += token.indicator
  
  							return output
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   str			,  snake_case							:   str						)   -> Token:
  
  							if not text:
  													raise ValueError("We need some text to work with."						)
  
  							# Initialise result parameters to default values
  							_lowercase			,						_lowercase					:  Tuple    			=      0, 0
  
  							if not search_buffer:
  													return Token(snake_case			,  snake_case			,  text[length]						)
  
  							for i, character in enumerate(snake_case						):
  													_lowercase					:  Tuple    			=      len(snake_case						) - i
  													if character == text[0]:
  																			_lowercase					:  List[str]    			=      self._match_length_from_index(snake_case			,  snake_case			,  0			,  snake_case						)
  																			# if the found length is bigger than the current or if it's equal,
  																			# which means it's offset is smaller: update offset and length
  																			if found_length >= length:
  																									_lowercase			,						_lowercase					:  Optional[Any]    			=      found_offset, found_length
  
  							return Token(snake_case			,  snake_case			,  text[length]						)
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]			,  snake_case							:   str			,  snake_case							:   str			,  snake_case							:   int			,  snake_case							:   int						)   -> int:
  							if not text or text[text_index] != window[window_index]:
  													return 0
  							return 1 + self._match_length_from_index(
  							    snake_case			,  window + text[text_index]			,  text_index + 1			,  window_index + 1						)
  
  
  if __name__ == "__main__":
  							from doctest import testmod
  
  							testmod()
  							# Initialize compressor class
  							UpperCamelCase_	:    Optional[int]  					=       LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  							# Example
  							UpperCamelCase_	:    Optional[int]  					=       """cabracadabrarrarrad"""
  							UpperCamelCase_	:    Tuple  					=       lzaa_compressor.compress(TEXT)
  							print(lzaa_compressor.compress("""ababcbababaa"""))
  							UpperCamelCase_	:    Union[str, Any]  					=       lzaa_compressor.decompress(compressed_text)
  							assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  