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     __a			(					lowerCAmelCase_			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					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     __a			:
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=13							,lowerCamelCase_=64							,lowerCamelCase_=3							,lowerCamelCase_=4							,lowerCamelCase_=[2, 2, 2, 2]							,lowerCamelCase_=[8, 4, 2, 1]							,lowerCamelCase_=[16, 32, 64, 128]							,lowerCamelCase_=[1, 4, 8, 16]							,lowerCamelCase_=[1, 2, 4, 8]							,lowerCamelCase_=True							,lowerCamelCase_=True							,lowerCamelCase_="gelu"							,lowerCamelCase_=0.1							,lowerCamelCase_=0.1							,lowerCamelCase_=0.0_2							,lowerCamelCase_=16							,lowerCamelCase_=3							,lowerCamelCase_=None							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					parent
            _UpperCAmelCase   	=					batch_size
            _UpperCAmelCase   	=					image_size
            _UpperCAmelCase   	=					num_channels
            _UpperCAmelCase   	=					num_encoder_blocks
            _UpperCAmelCase   	=					sr_ratios
            _UpperCAmelCase   	=					depths
            _UpperCAmelCase   	=					hidden_sizes
            _UpperCAmelCase   	=					downsampling_rates
            _UpperCAmelCase   	=					num_attention_heads
            _UpperCAmelCase   	=					is_training
            _UpperCAmelCase   	=					use_labels
            _UpperCAmelCase   	=					hidden_act
            _UpperCAmelCase   	=					hidden_dropout_prob
            _UpperCAmelCase   	=					attention_probs_dropout_prob
            _UpperCAmelCase   	=					initializer_range
            _UpperCAmelCase   	=					decoder_hidden_size
            _UpperCAmelCase   	=					num_labels
            _UpperCAmelCase   	=					scope
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
  
            _UpperCAmelCase   	=					None
            if self.use_labels:
               _UpperCAmelCase   	=					ids_tensor([self.batch_size, self.image_size, self.image_size]							,self.num_labels		)
  
            _UpperCAmelCase   	=					self.get_config()
            return config, pixel_values, labels
  
  
  
  
  
  
  
         def    _snake_case			(					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    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					GLPNModel(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
            _UpperCAmelCase   	=					_UpperCAmelCase   	=					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    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.num_labels
            _UpperCAmelCase   	=					GLPNForDepthEstimation(lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
            self.parent.assertEqual(result.predicted_depth.shape							,(self.batch_size, self.image_size, self.image_size)		)
            _UpperCAmelCase   	=					model(lowerCamelCase_							,labels=lowerCamelCase_		)
            self.parent.assertEqual(result.predicted_depth.shape							,(self.batch_size, self.image_size, self.image_size)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.prepare_config_and_inputs()
            _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					config_and_inputs
            _UpperCAmelCase   	=					{"pixel_values": pixel_values}
            return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class     __a			(					lowerCAmelCase_				,	lowerCAmelCase_				,	unittest.TestCase			):
         _lowerCamelCase							:							Any       =			(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
         _lowerCamelCase							:							int       =			(
             {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
         )
  
         _lowerCamelCase							:							List[Any]       =			False
         _lowerCamelCase							:							Any       =			False
         _lowerCamelCase							:							List[str]       =			False
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					GLPNModelTester(self		)
            _UpperCAmelCase   	=					GLPNConfigTester(self							,config_class=lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            self.config_tester.run_common_tests()
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					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    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            pass
  
  
  
  
  
  
  
         @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"		)
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            pass
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               _UpperCAmelCase   	=					inspect.signature(model.forward		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _UpperCAmelCase   	=					[*signature.parameters.keys()]
  
               _UpperCAmelCase   	=					["pixel_values"]
               self.assertListEqual(arg_names[:1]							,lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
            _UpperCAmelCase   	=					True
  
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					True
               _UpperCAmelCase   	=					False
               _UpperCAmelCase   	=					True
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               model.to(lowerCamelCase_		)
               model.eval()
               with torch.no_grad():
                  _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)
               _UpperCAmelCase   	=					outputs.attentions
  
               _UpperCAmelCase   	=					sum(self.model_tester.depths		)
               self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)
  
               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               _UpperCAmelCase   	=					True
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               model.to(lowerCamelCase_		)
               model.eval()
               with torch.no_grad():
                  _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)
               _UpperCAmelCase   	=					outputs.attentions
  
               self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)
  
               # verify the first attentions (first block, first layer)
               _UpperCAmelCase   	=					(self.model_tester.image_size // 4) ** 2
               _UpperCAmelCase   	=					(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   	=					(self.model_tester.image_size // 32) ** 2
               _UpperCAmelCase   	=					(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   	=					len(lowerCamelCase_		)
  
               # Check attention is always last and order is fine
               _UpperCAmelCase   	=					True
               _UpperCAmelCase   	=					True
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               model.to(lowerCamelCase_		)
               model.eval()
               with torch.no_grad():
                  _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)
  
               self.assertEqual(out_len + 1							,len(lowerCamelCase_		)		)
  
               _UpperCAmelCase   	=					outputs.attentions
  
               self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)
               # verify the first attentions (first block, first layer)
               _UpperCAmelCase   	=					(self.model_tester.image_size // 4) ** 2
               _UpperCAmelCase   	=					(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    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            def check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               model.to(lowerCamelCase_		)
               model.eval()
  
               with torch.no_grad():
                  _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)
  
               _UpperCAmelCase   	=					outputs.hidden_states
  
               _UpperCAmelCase   	=					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   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					True
               check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		)
  
               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _UpperCAmelCase   	=					True
  
               check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            if not self.model_tester.is_training:
               return
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
            _UpperCAmelCase   	=					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   	=					inputs_dict["pixel_values"].shape
                  _UpperCAmelCase   	=					torch.zeros(
                      [self.model_tester.batch_size, height, width]							,device=lowerCamelCase_		).long()
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               model.to(lowerCamelCase_		)
               model.train()
               _UpperCAmelCase   	=					self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_							,return_labels=lowerCamelCase_		)
               _UpperCAmelCase   	=					model(**lowerCamelCase_		).loss
               loss.backward()
  
  
  
  
  
  
  
         @slow
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase   	=					GLPNModel.from_pretrained(lowerCamelCase_		)
               self.assertIsNotNone(lowerCamelCase_		)
  
  
  
  def 			__UpperCAmelCase		(		)     ->       int:
     _UpperCAmelCase   	=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     return image
  
  
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         @slow
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
            _UpperCAmelCase   	=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(lowerCamelCase_		)
  
            _UpperCAmelCase   	=					prepare_img()
            _UpperCAmelCase   	=					image_processor(images=lowerCamelCase_							,return_tensors="pt"		).to(lowerCamelCase_		)
  
            # forward pass
            with torch.no_grad():
               _UpperCAmelCase   	=					model(**lowerCamelCase_		)
  
            # verify the predicted depth
            _UpperCAmelCase   	=					torch.Size([1, 480, 640]		)
            self.assertEqual(outputs.predicted_depth.shape							,lowerCamelCase_		)
  
            _UpperCAmelCase   	=					torch.tensor(
                [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]		).to(lowerCamelCase_		)
  
            self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]							,lowerCamelCase_							,atol=1e-4		)		)
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  import collections
  import importlib.util
  import os
  import re
  
  
  # All paths are set with the intent you should run this script from the root of the repo with the command
  # python utils/check_table.py
  SCREAMING_SNAKE_CASE_     				=   'src/diffusers'
  SCREAMING_SNAKE_CASE_     				=   'docs/source/en'
  SCREAMING_SNAKE_CASE_     				=   '.'
  def 			__UpperCAmelCase		(		a_			:		Optional[Any]    ,  a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
     with open(a_    ,  "r"    ,  encoding="utf-8"    ,  newline="\n"				) as f:
        _UpperCAmelCase   	=					f.readlines()
     # Find the start prompt.
     _UpperCAmelCase   	=					0
     while not lines[start_index].startswith(a_				):
        start_index += 1
     start_index += 1
  
     _UpperCAmelCase   	=					start_index
     while not lines[end_index].startswith(a_				):
        end_index += 1
     end_index -= 1
  
     while len(lines[start_index]				) <= 1:
        start_index += 1
     while len(lines[end_index]				) <= 1:
        end_index -= 1
     end_index += 1
     return "".join(lines[start_index:end_index]				), start_index, end_index, lines
  
  
  # Add here suffixes that are used to identify models, separated by |
  SCREAMING_SNAKE_CASE_     				=   'Model|Encoder|Decoder|ForConditionalGeneration'
  # Regexes that match TF/Flax/PT model names.
  SCREAMING_SNAKE_CASE_     				=   re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  SCREAMING_SNAKE_CASE_     				=   re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  # Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
  SCREAMING_SNAKE_CASE_     				=   re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  
  
  # This is to make sure the diffusers module imported is the one in the repo.
  SCREAMING_SNAKE_CASE_     				=   importlib.util.spec_from_file_location(
      'diffusers',
      os.path.join(TRANSFORMERS_PATH, '__init__.py'),
      submodule_search_locations=[TRANSFORMERS_PATH],
  )
  SCREAMING_SNAKE_CASE_     				=   spec.loader.load_module()
  def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
     _UpperCAmelCase   	=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,  a_				)
     return [m.group(0				) for m in matches]
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
     _UpperCAmelCase   	=					2 if text == "✅" or text == "❌" else len(a_				)
     _UpperCAmelCase   	=					(width - text_length) // 2
     _UpperCAmelCase   	=					width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent
  def 			__UpperCAmelCase		(		)     ->       List[Any]:
     _UpperCAmelCase   	=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     _UpperCAmelCase   	=					{
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     _UpperCAmelCase   	=					{name: config.replace("ConfigMixin"    ,  ""				) for name, config in model_name_to_config.items()}
  
     # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
     _UpperCAmelCase   	=					collections.defaultdict(a_				)
  
     # Let's lookup through all diffusers object (once).
     for attr_name in dir(a_				):
        _UpperCAmelCase   	=					None
        if attr_name.endswith("Tokenizer"				):
           _UpperCAmelCase   	=					slow_tokenizers
           _UpperCAmelCase   	=					attr_name[:-9]
        elif attr_name.endswith("TokenizerFast"				):
           _UpperCAmelCase   	=					fast_tokenizers
           _UpperCAmelCase   	=					attr_name[:-13]
        elif _re_tf_models.match(a_				) is not None:
           _UpperCAmelCase   	=					tf_models
           _UpperCAmelCase   	=					_re_tf_models.match(a_				).groups()[0]
        elif _re_flax_models.match(a_				) is not None:
           _UpperCAmelCase   	=					flax_models
           _UpperCAmelCase   	=					_re_flax_models.match(a_				).groups()[0]
        elif _re_pt_models.match(a_				) is not None:
           _UpperCAmelCase   	=					pt_models
           _UpperCAmelCase   	=					_re_pt_models.match(a_				).groups()[0]
  
        if lookup_dict is not None:
           while len(a_				) > 0:
              if attr_name in model_name_to_prefix.values():
                 _UpperCAmelCase   	=					True
                 break
              # Try again after removing the last word in the name
              _UpperCAmelCase   	=					"".join(camel_case_split(a_				)[:-1]				)
  
      # Let's build that table!
     _UpperCAmelCase   	=					list(model_name_to_config.keys()				)
     model_names.sort(key=str.lower				)
     _UpperCAmelCase   	=					["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
     # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
     _UpperCAmelCase   	=					[len(a_				) + 2 for c in columns]
     _UpperCAmelCase   	=					max([len(a_				) for name in model_names]				) + 2
  
     # Build the table per se
     _UpperCAmelCase   	=					"|" + "|".join([_center_text(a_    ,  a_				) for c, w in zip(a_    ,  a_				)]				) + "|\n"
     # Use ":-----:" format to center-aligned table cell texts
     table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]				) + "|\n"
  
     _UpperCAmelCase   	=					{True: "✅", False: "❌"}
     for name in model_names:
        _UpperCAmelCase   	=					model_name_to_prefix[name]
        _UpperCAmelCase   	=					[
            name,
            check[slow_tokenizers[prefix]],
            check[fast_tokenizers[prefix]],
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(a_    ,  a_				) for l, w in zip(a_    ,  a_				)]				) + "|\n"
     return table
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]=False				)     ->       int:
     _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					_find_text_in_file(
         filename=os.path.join(a_    ,  "index.mdx"				)    ,  start_prompt="<!--This table is updated automatically from the auto modules"    ,  end_prompt="<!-- End table-->"    ,  )
     _UpperCAmelCase   	=					get_model_table_from_auto_modules()
  
     if current_table != new_table:
        if overwrite:
           with open(os.path.join(a_    ,  "index.mdx"				)    ,  "w"    ,  encoding="utf-8"    ,  newline="\n"				) as f:
              f.writelines(lines[:start_index] + [new_table] + lines[end_index:]				)
        else:
           raise ValueError(
               "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."				)
  
  
  if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  
    check_model_table(args.fix_and_overwrite)
  
  