




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
							import torch


class 				__SCREAMING_SNAKE_CASE							:

					def __init__(				self ,       __lowercase ,       __lowercase=14 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=False ,       __lowercase=True ,       __lowercase=99 ,       __lowercase=32 ,       __lowercase=4 ,       __lowercase=4 ,       __lowercase=[[["global", "local"], 2]] ,       __lowercase=37 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=512 ,       __lowercase=7 ,       __lowercase=0.02 ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							parent
						lowerCamelCase_							:		Any							   =							batch_size
						lowerCamelCase_							:		Tuple							   =							seq_length
						lowerCamelCase_							:		Optional[Any]							   =							is_training
						lowerCamelCase_							:		List[Any]							   =							use_input_mask
						lowerCamelCase_							:		Dict							   =							use_token_type_ids
						lowerCamelCase_							:		int							   =							use_labels
						lowerCamelCase_							:		Dict							   =							vocab_size
						lowerCamelCase_							:		List[Any]							   =							hidden_size
						lowerCamelCase_							:		Union[str, Any]							   =							num_hidden_layers
						lowerCamelCase_							:		List[str]							   =							num_attention_heads
						lowerCamelCase_							:		Optional[Any]							   =							attention_types
						lowerCamelCase_							:		Tuple							   =							intermediate_size
						lowerCamelCase_							:		List[str]							   =							hidden_act
						lowerCamelCase_							:		List[str]							   =							hidden_dropout_prob
						lowerCamelCase_							:		Tuple							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		List[str]							   =							max_position_embeddings
						lowerCamelCase_							:		List[str]							   =							window_size
						lowerCamelCase_							:		Tuple							   =							initializer_range
						lowerCamelCase_							:		Tuple							   =							None
						lowerCamelCase_							:		Optional[Any]							   =							vocab_size - 1
						lowerCamelCase_							:		Any							   =							vocab_size - 1
						lowerCamelCase_							:		str							   =							vocab_size - 1

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size					)

						lowerCamelCase_							:		Optional[int]							   =							None
						if self.use_input_mask:
							lowerCamelCase_							:		Any							   =							random_attention_mask([self.batch_size, self.seq_length]					)

						lowerCamelCase_							:		int							   =							GPTNeoConfig(
						    vocab_size=self.vocab_size ,       hidden_size=self.hidden_size ,       num_layers=self.num_hidden_layers ,       num_heads=self.num_attention_heads ,       max_position_embeddings=self.max_position_embeddings ,       use_cache=__lowercase ,       bos_token_id=self.bos_token_id ,       eos_token_id=self.eos_token_id ,       pad_token_id=self.pad_token_id ,       window_size=self.window_size ,       attention_types=self.attention_types ,       )

						return (config, input_ids, input_mask)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Union[str, Any]							   =							config_and_inputs
						lowerCamelCase_							:		Dict							   =							{'input_ids': input_ids, 'attention_mask': attention_mask}
						return config, inputs_dict

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							20
						lowerCamelCase_							:		Optional[Any]							   =							model_class_name(__lowercase					)

						lowerCamelCase_							:		List[Any]							   =							model.init_cache(input_ids.shape[0] ,       __lowercase					)
						lowerCamelCase_							:		Any							   =							jnp.ones((input_ids.shape[0], max_decoder_length) ,       dtype='i4'					)

						lowerCamelCase_							:		Union[str, Any]							   =							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1					)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)					)
						lowerCamelCase_							:		List[Any]							   =							model(
						    input_ids[:, :-1] ,       attention_mask=__lowercase ,       past_key_values=__lowercase ,       position_ids=__lowercase ,       )

						lowerCamelCase_							:		Any							   =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype='i4'					)
						lowerCamelCase_							:		int							   =							model(
						    input_ids[:, -1:] ,       attention_mask=__lowercase ,       past_key_values=outputs_cache.past_key_values ,       position_ids=__lowercase ,       )

						lowerCamelCase_							:		Optional[int]							   =							model(__lowercase					)

						lowerCamelCase_							:		Union[str, Any]							   =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
						self.parent.assertTrue(diff < 1E-3 ,       msg=f"Max diff is {diff}"					)





					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							20
						lowerCamelCase_							:		List[str]							   =							model_class_name(__lowercase					)

						lowerCamelCase_							:		Optional[int]							   =							jnp.concatenate(
						    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)] ,       axis=-1 ,       )

						lowerCamelCase_							:		List[str]							   =							model.init_cache(input_ids.shape[0] ,       __lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1					)[None, :] ,       (input_ids.shape[0], input_ids.shape[-1] - 1)					)

						lowerCamelCase_							:		Tuple							   =							model(
						    input_ids[:, :-1] ,       attention_mask=__lowercase ,       past_key_values=__lowercase ,       position_ids=__lowercase ,       )
						lowerCamelCase_							:		Optional[int]							   =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,       dtype='i4'					)
						lowerCamelCase_							:		Optional[Any]							   =							model(
						    input_ids[:, -1:] ,       past_key_values=outputs_cache.past_key_values ,       attention_mask=__lowercase ,       position_ids=__lowercase ,       )

						lowerCamelCase_							:		Optional[Any]							   =							model(__lowercase ,       attention_mask=__lowercase					)

						lowerCamelCase_							:		List[str]							   =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
						self.parent.assertTrue(diff < 1E-3 ,       msg=f"Max diff is {diff}"					)


@require_flax
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					_lowerCamelCase		  = (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							FlaxGPTNeoModelTester(self					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(__lowercase ,       __lowercase ,       __lowercase ,       __lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		List[str]							   =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					)

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							GPTaTokenizer.from_pretrained('gpt2' ,       pad_token='<|endoftext|>' ,       padding_side='left'					)
						lowerCamelCase_							:		Dict							   =							tokenizer(['Hello this is a long string', 'Hey'] ,       return_tensors='np' ,       padding=__lowercase ,       truncation=__lowercase					)

						lowerCamelCase_							:		Optional[Any]							   =							FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'					)
						lowerCamelCase_							:		Union[str, Any]							   =							False
						lowerCamelCase_							:		Tuple							   =							model.config.eos_token_id

						lowerCamelCase_							:		str							   =							jax.jit(model.generate					)

						lowerCamelCase_							:		Union[str, Any]							   =							jit_generate(
						    inputs['input_ids'] ,       attention_mask=inputs['attention_mask'] ,       pad_token_id=tokenizer.pad_token_id					).sequences

						lowerCamelCase_							:		Tuple							   =							tokenizer.batch_decode(__lowercase ,       skip_special_tokens=__lowercase					)

						lowerCamelCase_							:		Dict							   =							[
						    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
						    'Hey, I\'m a little late to the party. I\'m going to',
						]

						self.assertListEqual(__lowercase ,       __lowercase					)

					@is_pt_flax_cross_test
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		List[Any]							   =							self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__					):
								# prepare inputs
								lowerCamelCase_							:		List[str]							   =							self._prepare_for_class(__lowercase ,       __lowercase					)
								lowerCamelCase_							:		List[Any]							   =							{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase_							:		Optional[Any]							   =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase_							:		Dict							   =							getattr(__lowercase ,       __lowercase					)

								lowerCamelCase_   ,			lowerCamelCase_							:		Optional[int]							   =							pt_inputs['input_ids'].shape
								lowerCamelCase_							:		int							   =							np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)					)
								for batch_idx, start_index in enumerate(__lowercase					):
									lowerCamelCase_							:		Optional[int]							   =							0
									lowerCamelCase_							:		List[Any]							   =							1
									lowerCamelCase_							:		Any							   =							0
									lowerCamelCase_							:		List[str]							   =							1
								lowerCamelCase_							:		Tuple							   =							pt_model_class(__lowercase					).eval()
								lowerCamelCase_							:		Tuple							   =							model_class(__lowercase ,       dtype=jnp.floataa					)

								lowerCamelCase_							:		Optional[int]							   =							convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,       __lowercase					)
								lowerCamelCase_							:		List[str]							   =							fx_state

								with torch.no_grad():
									lowerCamelCase_							:		Dict							   =							pt_model(**__lowercase					).to_tuple()

								lowerCamelCase_							:		Union[str, Any]							   =							fx_model(**__lowercase					).to_tuple()
								self.assertEqual(len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)

								with tempfile.TemporaryDirectory() as tmpdirname:
									pt_model.save_pretrained(__lowercase					)
									lowerCamelCase_							:		Dict							   =							model_class.from_pretrained(__lowercase ,       from_pt=__lowercase					)

								lowerCamelCase_							:		Optional[int]							   =							fx_model_loaded(**__lowercase					).to_tuple()
								self.assertEqual(
								    len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output_loaded, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output_loaded[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)

					@is_pt_flax_cross_test
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__					):
								# prepare inputs
								lowerCamelCase_							:		Optional[int]							   =							self._prepare_for_class(__lowercase ,       __lowercase					)
								lowerCamelCase_							:		Optional[int]							   =							{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase_							:		Any							   =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase_							:		List[str]							   =							getattr(__lowercase ,       __lowercase					)

								lowerCamelCase_							:		int							   =							pt_model_class(__lowercase					).eval()
								lowerCamelCase_							:		Tuple							   =							model_class(__lowercase ,       dtype=jnp.floataa					)

								lowerCamelCase_							:		Any							   =							load_flax_weights_in_pytorch_model(__lowercase ,       fx_model.params					)
								lowerCamelCase_   ,			lowerCamelCase_							:		Any							   =							pt_inputs['input_ids'].shape
								lowerCamelCase_							:		Optional[Any]							   =							np.random.randint(0 ,       seq_length - 1 ,       size=(batch_size,)					)
								for batch_idx, start_index in enumerate(__lowercase					):
									lowerCamelCase_							:		Optional[Any]							   =							0
									lowerCamelCase_							:		List[str]							   =							1
									lowerCamelCase_							:		Dict							   =							0
									lowerCamelCase_							:		str							   =							1

								# make sure weights are tied in PyTorch
								pt_model.tie_weights()

								with torch.no_grad():
									lowerCamelCase_							:		Union[str, Any]							   =							pt_model(**__lowercase					).to_tuple()

								lowerCamelCase_							:		List[Any]							   =							fx_model(**__lowercase					).to_tuple()
								self.assertEqual(len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)

								with tempfile.TemporaryDirectory() as tmpdirname:
									fx_model.save_pretrained(__lowercase					)
									lowerCamelCase_							:		str							   =							pt_model_class.from_pretrained(__lowercase ,       from_flax=__lowercase					)

								with torch.no_grad():
									lowerCamelCase_							:		Union[str, Any]							   =							pt_model_loaded(**__lowercase					).to_tuple()

								self.assertEqual(
								    len(__lowercase					) ,       len(__lowercase					) ,       'Output lengths differ between Flax and PyTorch'					)
								for fx_output, pt_output in zip(__lowercase ,       __lowercase					):
									self.assert_almost_equals(fx_output[:, -1] ,       pt_output[:, -1].numpy() ,       4E-2					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_							:		int							   =							model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'					)
							lowerCamelCase_							:		Union[str, Any]							   =							model(np.ones((1, 1)					)					)
							self.assertIsNotNone(__lowercase					)






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
UpperCamelCase__              =						'src/diffusers'
UpperCamelCase__              =						'docs/source/en'
UpperCamelCase__              =						'.'






def 					snake_case					(							A						,   A						,   A			)							->							int:




	'''simple docstring'''


	with open(A						,   'r'						,   encoding='utf-8'						,   newline='\n'			) as f:
		lowerCamelCase_							:		int							   =							f.readlines()
	# Find the start prompt.
	lowerCamelCase_							:		Tuple							   =							0
	while not lines[start_index].startswith(A			):
		start_index += 1
	start_index += 1

	lowerCamelCase_							:		Tuple							   =							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 |
UpperCamelCase__              =						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
UpperCamelCase__              =						re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
UpperCamelCase__              =						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.
UpperCamelCase__              =						re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
UpperCamelCase__              =						importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
UpperCamelCase__              =						spec.loader.load_module()






def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Dict							   =							re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'						,   A			)
	return [m.group(0			) for m in matches]






def 					snake_case					(							A						,   A			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							   =							2 if text == '✅' or text == '❌' else len(A			)
	lowerCamelCase_							:		List[str]							   =							(width - text_length) // 2
	lowerCamelCase_							:		int							   =							width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def 					snake_case					(							)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	lowerCamelCase_							:		Optional[Any]							   =							{
	    name: config_mapping_names[code]
	    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_mapping_names
	}
	lowerCamelCase_							:		Optional[int]							   =							{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.
	lowerCamelCase_							:		Tuple							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Any							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		int							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Optional[int]							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Dict							   =							collections.defaultdict(A			)

	# Let's lookup through all diffusers object (once).
	for attr_name in dir(A			):
		lowerCamelCase_							:		Tuple							   =							None
		if attr_name.endswith('Tokenizer'			):
			lowerCamelCase_							:		Union[str, Any]							   =							slow_tokenizers
			lowerCamelCase_							:		Optional[Any]							   =							attr_name[:-9]
		elif attr_name.endswith('TokenizerFast'			):
			lowerCamelCase_							:		Any							   =							fast_tokenizers
			lowerCamelCase_							:		Optional[Any]							   =							attr_name[:-13]
		elif _re_tf_models.match(A			) is not None:
			lowerCamelCase_							:		Tuple							   =							tf_models
			lowerCamelCase_							:		Optional[int]							   =							_re_tf_models.match(A			).groups()[0]
		elif _re_flax_models.match(A			) is not None:
			lowerCamelCase_							:		Union[str, Any]							   =							flax_models
			lowerCamelCase_							:		Union[str, Any]							   =							_re_flax_models.match(A			).groups()[0]
		elif _re_pt_models.match(A			) is not None:
			lowerCamelCase_							:		Union[str, Any]							   =							pt_models
			lowerCamelCase_							:		Optional[int]							   =							_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():
					lowerCamelCase_							:		List[Any]							   =							True
					break
				# Try again after removing the last word in the name
				lowerCamelCase_							:		int							   =							''.join(camel_case_split(A			)[:-1]			)

    # Let's build that table!
	lowerCamelCase_							:		Optional[int]							   =							list(model_name_to_config.keys()			)
	model_names.sort(key=str.lower			)
	lowerCamelCase_							:		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).
	lowerCamelCase_							:		str							   =							[len(A			) + 2 for c in columns]
	lowerCamelCase_							:		Optional[int]							   =							max([len(A			) for name in model_names]			) + 2

	# Build the table per se
	lowerCamelCase_							:		Optional[Any]							   =							'|' + '|'.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"

	lowerCamelCase_							:		str							   =							{True: '✅', False: '❌'}
	for name in model_names:
		lowerCamelCase_							:		List[str]							   =							model_name_to_prefix[name]
		lowerCamelCase_							:		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(A						,   A			) for l, w in zip(A						,   A			)]			) + "|\n"
	return table






def 					snake_case					(							A=False			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							_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-->'						,   )
	lowerCamelCase_							:		Any							   =							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__":
							UpperCamelCase__              =						argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							UpperCamelCase__              =						parser.parse_args()

							check_model_table(args.fix_and_overwrite)
