





"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFLEDForConditionalGeneration, TFLEDModel



@require_tf
class      __SCREAMING_SNAKE_CASE    :







		'''simple docstring'''


		SCREAMING_SNAKE_CASE__							:str	     =					LEDConfig
		SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					{}
		SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					'''gelu'''

		def __init__(		self   :       Tuple					,   __a   :       Dict					,   __a   :       Union[str, Any]=13					,   __a   :       List[Any]=7					,   __a   :       List[Any]=True					,   __a   :       Optional[Any]=False					,   __a   :       List[Any]=99					,   __a   :       List[Any]=32					,   __a   :       List[Any]=2					,   __a   :       Optional[Any]=4					,   __a   :       int=37					,   __a   :       Any=0.1					,   __a   :       List[str]=0.1					,   __a   :       int=20					,   __a   :       Any=2					,   __a   :       Union[str, Any]=1					,   __a   :       str=0					,   __a   :       List[str]=4					,   )		->				List[Any]:
								_UpperCamelCase      :	List[Any]						=       parent
								_UpperCamelCase      :	List[str]						=       batch_size
								_UpperCamelCase      :	List[Any]						=       seq_length
								_UpperCamelCase      :	Dict						=       is_training
								_UpperCamelCase      :	Union[str, Any]						=       use_labels
								_UpperCamelCase      :	Union[str, Any]						=       vocab_size
								_UpperCamelCase      :	List[str]						=       hidden_size
								_UpperCamelCase      :	Tuple						=       num_hidden_layers
								_UpperCamelCase      :	Any						=       num_attention_heads
								_UpperCamelCase      :	Optional[Any]						=       intermediate_size

								_UpperCamelCase      :	Any						=       hidden_dropout_prob
								_UpperCamelCase      :	List[str]						=       attention_probs_dropout_prob
								_UpperCamelCase      :	int						=       max_position_embeddings
								_UpperCamelCase      :	Optional[Any]						=       eos_token_id
								_UpperCamelCase      :	Union[str, Any]						=       pad_token_id
								_UpperCamelCase      :	Optional[Any]						=       bos_token_id
								_UpperCamelCase      :	Tuple						=       attention_window

								# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
								# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
								# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
								# because its local attention only attends to `self.attention_window` and one before and one after
								_UpperCamelCase      :	int						=       self.attention_window + 2

								# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
								# the `test_attention_outputs` and `test_hidden_states_output` tests
								_UpperCamelCase      :	Optional[Any]						=       (
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)

		def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				List[str]:
								_UpperCamelCase      :	int						=       ids_tensor([self.batch_size, self.seq_length - 1]					,   self.vocab_size	)
								_UpperCamelCase      :	Optional[int]						=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)					,   1	)
								_UpperCamelCase      :	List[str]						=       tf.concat([input_ids, eos_tensor]					,   axis=1	)

								_UpperCamelCase      :	int						=       ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

								_UpperCamelCase      :	Optional[int]						=       self.config_cls(
								    vocab_size=self.vocab_size					,   d_model=self.hidden_size					,   encoder_layers=self.num_hidden_layers					,   decoder_layers=self.num_hidden_layers					,   encoder_attention_heads=self.num_attention_heads					,   decoder_attention_heads=self.num_attention_heads					,   encoder_ffn_dim=self.intermediate_size					,   decoder_ffn_dim=self.intermediate_size					,   dropout=self.hidden_dropout_prob					,   attention_dropout=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   eos_token_ids=[2]					,   bos_token_id=self.bos_token_id					,   pad_token_id=self.pad_token_id					,   decoder_start_token_id=self.pad_token_id					,   attention_window=self.attention_window					,   **self.config_updates					,   )
								_UpperCamelCase      :	List[Any]						=       prepare_led_inputs_dict(__a					,   __a					,   __a	)
								_UpperCamelCase      :	str						=       tf.concat(
								    [tf.zeros_like(__a	)[:, :-1], tf.ones_like(__a	)[:, -1:]]					,   axis=-1					,   )
								_UpperCamelCase      :	Optional[Any]						=       global_attention_mask
								return config, inputs_dict


		def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       Dict					,   __a   :       Optional[int]	)		->				Optional[Any]:
								_UpperCamelCase      :	str						=       TFLEDModel(config=__a	).get_decoder()
								_UpperCamelCase      :	Tuple						=       inputs_dict["input_ids"]

								_UpperCamelCase      :	List[str]						=       input_ids[:1, :]
								_UpperCamelCase      :	Tuple						=       inputs_dict["attention_mask"][:1, :]
								_UpperCamelCase      :	int						=       1

								# first forward pass
								_UpperCamelCase      :	Any						=       model(__a					,   attention_mask=__a					,   use_cache=__a	)

								_UpperCamelCase,  _UpperCamelCase      :	Union[str, Any]						=       outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								_UpperCamelCase      :	Optional[int]						=       ids_tensor((self.batch_size, 3)					,   config.vocab_size	)
								_UpperCamelCase      :	Tuple						=       tf.cast(ids_tensor((self.batch_size, 3)					,   2	)					,   tf.inta	)

								# append to next input_ids and
								_UpperCamelCase      :	Dict						=       tf.concat([input_ids, next_tokens]					,   axis=-1	)
								_UpperCamelCase      :	List[Any]						=       tf.concat([attention_mask, next_attn_mask]					,   axis=-1	)

								_UpperCamelCase      :	Dict						=       model(__a					,   attention_mask=__a	)[0]
								_UpperCamelCase      :	int						=       model(__a					,   attention_mask=__a					,   past_key_values=__a	)[0]

								self.parent.assertEqual(next_tokens.shape[1]					,   output_from_past.shape[1]	)

								# select random slice
								_UpperCamelCase      :	Union[str, Any]						=       int(ids_tensor((1,)					,   output_from_past.shape[-1]	)	)
								_UpperCamelCase      :	Optional[Any]						=       output_from_no_past[:, -3:, random_slice_idx]
								_UpperCamelCase      :	int						=       output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__a					,   __a					,   rtol=1e-3	)





def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_=None		,lowercase_=None		,)					->		int:




						"""simple docstring"""

						if attention_mask is None:
												_UpperCamelCase      :	Any						=       tf.cast(tf.math.not_equal(UpperCamelCase__		,config.pad_token_id			)		,tf.inta			)
						if decoder_attention_mask is None:
												_UpperCamelCase      :	Optional[int]						=       tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape		,dtype=tf.inta			),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,config.pad_token_id			)		,tf.inta			),
												    ]		,axis=-1		,)
						if head_mask is None:
												_UpperCamelCase      :	List[Any]						=       tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
						if decoder_head_mask is None:
												_UpperCamelCase      :	Any						=       tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						    "decoder_input_ids": decoder_input_ids,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						}



@require_tf
class      __SCREAMING_SNAKE_CASE    (						lowercase_		,  lowercase_		,  unittest.TestCase   ):







		'''simple docstring'''


		SCREAMING_SNAKE_CASE__							:Optional[int]	     =					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
		SCREAMING_SNAKE_CASE__							:int	     =					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
		SCREAMING_SNAKE_CASE__							:List[Any]	     =					(
		    {
		        '''conversational''': TFLEDForConditionalGeneration,
		        '''feature-extraction''': TFLEDModel,
		        '''summarization''': TFLEDForConditionalGeneration,
		        '''text2text-generation''': TFLEDForConditionalGeneration,
		        '''translation''': TFLEDForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		SCREAMING_SNAKE_CASE__							:Tuple	     =					True
		SCREAMING_SNAKE_CASE__							:Optional[int]	     =					False
		SCREAMING_SNAKE_CASE__							:Dict	     =					False
		SCREAMING_SNAKE_CASE__							:List[Any]	     =					False

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				str:
								_UpperCamelCase      :	List[Any]						=       TFLEDModelTester(self	)
								_UpperCamelCase      :	List[str]						=       ConfigTester(self					,   config_class=__a	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				int:
								self.config_tester.run_common_tests()

		def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Tuple:
								_UpperCamelCase      :	List[str]						=       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__a	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				str:
								_UpperCamelCase,  _UpperCamelCase      :	int						=       self.model_tester.prepare_config_and_inputs_for_common()
								_UpperCamelCase      :	List[str]						=       tf.zeros_like(inputs_dict["attention_mask"]	)
								_UpperCamelCase      :	Dict						=       2
								_UpperCamelCase      :	Optional[int]						=       tf.where(
								    tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices					,   1					,   inputs_dict["global_attention_mask"]					,   )

								_UpperCamelCase      :	List[str]						=       True
								_UpperCamelCase      :	Tuple						=       self.model_tester.seq_length
								_UpperCamelCase      :	Dict						=       self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(__a   :       Optional[Any]	):
														_UpperCamelCase      :	Optional[int]						=       outputs.decoder_attentions
														self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
														self.assertListEqual(
														    list(decoder_attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_length, seq_length]					,   )

								def check_encoder_attentions_output(__a   :       Union[str, Any]	):
														_UpperCamelCase      :	List[str]						=       [t.numpy() for t in outputs.encoder_attentions]
														_UpperCamelCase      :	Optional[Any]						=       [t.numpy() for t in outputs.encoder_global_attentions]
														self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
														self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
														self.assertListEqual(
														    list(attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_length, seq_length]					,   )
														self.assertListEqual(
														    list(global_attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]					,   )

								for model_class in self.all_model_classes:
														_UpperCamelCase      :	Union[str, Any]						=       True
														_UpperCamelCase      :	Dict						=       False
														_UpperCamelCase      :	Union[str, Any]						=       False
														_UpperCamelCase      :	int						=       model_class(__a	)
														_UpperCamelCase      :	Tuple						=       model(self._prepare_for_class(__a					,   __a	)	)
														_UpperCamelCase      :	Any						=       len(__a	)
														self.assertEqual(config.output_hidden_states					,   __a	)
														check_encoder_attentions_output(__a	)

														if self.is_encoder_decoder:
																				_UpperCamelCase      :	int						=       model_class(__a	)
																				_UpperCamelCase      :	int						=       model(self._prepare_for_class(__a					,   __a	)	)
																				self.assertEqual(config.output_hidden_states					,   __a	)
																				check_decoder_attentions_output(__a	)

														# Check that output attentions can also be changed via the config
														del inputs_dict["output_attentions"]
														_UpperCamelCase      :	Optional[Any]						=       True
														_UpperCamelCase      :	int						=       model_class(__a	)
														_UpperCamelCase      :	int						=       model(self._prepare_for_class(__a					,   __a	)	)
														self.assertEqual(config.output_hidden_states					,   __a	)
														check_encoder_attentions_output(__a	)

														# Check attention is always last and order is fine
														_UpperCamelCase      :	List[str]						=       True
														_UpperCamelCase      :	Union[str, Any]						=       True
														_UpperCamelCase      :	Optional[Any]						=       model_class(__a	)
														_UpperCamelCase      :	Dict						=       model(self._prepare_for_class(__a					,   __a	)	)

														self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)					,   len(__a	)	)
														self.assertEqual(model.config.output_hidden_states					,   __a	)
														check_encoder_attentions_output(__a	)

		@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."	)
		def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				List[Any]:
								pass


		def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				str:
								# TODO: Head-masking not yet implement
								pass





def 					lowercase__    (					lowercase_			)					->		str:




						"""simple docstring"""

						return tf.constant(UpperCamelCase__		,dtype=tf.intaa			)


lowerCamelCase__				  =   1E-4



@slow
@require_tf
class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







		'''simple docstring'''


		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				List[Any]:
								_UpperCamelCase      :	List[Any]						=       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

								# change to intended input here
								_UpperCamelCase      :	List[str]						=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
								_UpperCamelCase      :	int						=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
								_UpperCamelCase      :	int						=       prepare_led_inputs_dict(model.config					,   __a					,   __a	)
								_UpperCamelCase      :	Tuple						=       model(**__a	)[0]
								_UpperCamelCase      :	List[str]						=       (1, 1024, 768)
								self.assertEqual(output.shape					,   __a	)
								# change to expected output here
								_UpperCamelCase      :	int						=       tf.convert_to_tensor(
								    [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]					,   )
								tf.debugging.assert_near(output[:, :3, :3]					,   __a					,   atol=1e-3	)


		def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[Any]:
								_UpperCamelCase      :	Optional[Any]						=       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

								# change to intended input here
								_UpperCamelCase      :	int						=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
								_UpperCamelCase      :	Union[str, Any]						=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
								_UpperCamelCase      :	Optional[Any]						=       prepare_led_inputs_dict(model.config					,   __a					,   __a	)
								_UpperCamelCase      :	Optional[Any]						=       model(**__a	)[0]
								_UpperCamelCase      :	Any						=       (1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape					,   __a	)
								# change to expected output here
								_UpperCamelCase      :	Union[str, Any]						=       tf.convert_to_tensor(
								    [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]					,   )
								tf.debugging.assert_near(output[:, :3, :3]					,   __a					,   atol=1e-3					,   rtol=1e-3	)







"""simple docstring"""


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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
lowerCamelCase__				  =   "src/transformers"
lowerCamelCase__				  =   "docs/source/en"
lowerCamelCase__				  =   "."




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		List[str]:




      """simple docstring"""

      with open(lowercase_		,"r"		,encoding="utf-8"		,newline="\n"			) as f:
            _UpperCamelCase      :	Union[str, Any]						=       f.readlines()
      # Find the start prompt.
      _UpperCamelCase      :	Dict						=       0
      while not lines[start_index].startswith(lowercase_			):
            start_index += 1
      start_index += 1

      _UpperCamelCase      :	Optional[int]						=       start_index
      while not lines[end_index].startswith(lowercase_			):
            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 |
lowerCamelCase__				  =   "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
lowerCamelCase__				  =   re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCamelCase__				  =   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.
lowerCamelCase__				  =   re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase__				  =   direct_transformers_import(TRANSFORMERS_PATH)




def 					lowercase__    (					lowercase_			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,lowercase_			)
      return [m.group(0			) for m in matches]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       2 if text == "✅" or text == "❌" else len(lowercase_			)
      _UpperCamelCase      :	Union[str, Any]						=       (width - text_length) // 2
      _UpperCamelCase      :	Dict						=       width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent




def 					lowercase__    (					)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      _UpperCamelCase      :	str						=       {
          name: config_maping_names[code]
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if code in config_maping_names
      }
      _UpperCamelCase      :	Dict						=       {name: config.replace("Config"		,""			) 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      :	int						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	Dict						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	Dict						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	int						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	str						=       collections.defaultdict(lowercase_			)

      # Let's lookup through all transformers object (once).
      for attr_name in dir(lowercase_			):
            _UpperCamelCase      :	List[str]						=       None
            if attr_name.endswith("Tokenizer"			):
                  _UpperCamelCase      :	Tuple						=       slow_tokenizers
                  _UpperCamelCase      :	Any						=       attr_name[:-9]
            elif attr_name.endswith("TokenizerFast"			):
                  _UpperCamelCase      :	Optional[Any]						=       fast_tokenizers
                  _UpperCamelCase      :	List[str]						=       attr_name[:-13]
            elif _re_tf_models.match(lowercase_			) is not None:
                  _UpperCamelCase      :	List[Any]						=       tf_models
                  _UpperCamelCase      :	Dict						=       _re_tf_models.match(lowercase_			).groups()[0]
            elif _re_flax_models.match(lowercase_			) is not None:
                  _UpperCamelCase      :	Dict						=       flax_models
                  _UpperCamelCase      :	Union[str, Any]						=       _re_flax_models.match(lowercase_			).groups()[0]
            elif _re_pt_models.match(lowercase_			) is not None:
                  _UpperCamelCase      :	Optional[int]						=       pt_models
                  _UpperCamelCase      :	Any						=       _re_pt_models.match(lowercase_			).groups()[0]

            if lookup_dict is not None:
                  while len(lowercase_			) > 0:
                        if attr_name in model_name_to_prefix.values():
                              _UpperCamelCase      :	Dict						=       True
                              break
                        # Try again after removing the last word in the name
                        _UpperCamelCase      :	List[str]						=       "".join(camel_case_split(lowercase_			)[:-1]			)

    # Let's build that table!
      _UpperCamelCase      :	Any						=       list(model_name_to_config.keys()			)
      model_names.sort(key=str.lower			)
      _UpperCamelCase      :	List[str]						=       ["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      :	Union[str, Any]						=       [len(lowercase_			) + 2 for c in columns]
      _UpperCamelCase      :	Any						=       max([len(lowercase_			) for name in model_names]			) + 2

      # Build the table per se
      _UpperCamelCase      :	Tuple						=       "|" + "|".join([_center_text(lowercase_		,lowercase_			) for c, w in zip(lowercase_		,lowercase_			)]			) + "|\n"
      # Use ":-----:" format to center-aligned table cell texts
      table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]			) + "|\n"

      _UpperCamelCase      :	Union[str, Any]						=       {True: "✅", False: "❌"}
      for name in model_names:
            _UpperCamelCase      :	Optional[int]						=       model_name_to_prefix[name]
            _UpperCamelCase      :	Tuple						=       [
                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(lowercase_		,lowercase_			) for l, w in zip(lowercase_		,lowercase_			)]			) + "|\n"
      return table




def 					lowercase__    (					lowercase_=False			)					->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	str						=       _find_text_in_file(
          filename=os.path.join(lowercase_		,"index.md"			)		,start_prompt="<!--This table is updated automatically from the auto modules"		,end_prompt="<!-- End table-->"		,)
      _UpperCamelCase      :	Any						=       get_model_table_from_auto_modules()

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(lowercase_		,"index.md"			)		,"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.md` has not been updated. Run `make fix-copies` to fix this."			)


if __name__ == "__main__":
  lowerCamelCase__				  =   argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  lowerCamelCase__				  =   parser.parse_args()

  check_model_table(args.fix_and_overwrite)
