




"""simple docstring"""







from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


a      =							logging.get_logger(__name__)


# General docstring
a      =							'MobileNetV1Config'

# Base docstring
a      =							'google/mobilenet_v1_1.0_224'
a      =							[1, 1_0_2_4, 7, 7]

# Image classification docstring
a      =							'google/mobilenet_v1_1.0_224'
a      =							'tabby, tabby cat'


a      =							[
    'google/mobilenet_v1_1.0_224',
    'google/mobilenet_v1_0.75_192',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]





def 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : Dict      ,       snake_case__   : List[Any]=None )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				{}

				if isinstance(snake_case_      ,       snake_case_ ):
								lowerCAmelCase  =				model.mobilenet_va
				else:
								lowerCAmelCase  =				model

				lowerCAmelCase  =				"""MobilenetV1/Conv2d_0/"""
				lowerCAmelCase  =				backbone.conv_stem.convolution.weight
				lowerCAmelCase  =				backbone.conv_stem.normalization.bias
				lowerCAmelCase  =				backbone.conv_stem.normalization.weight
				lowerCAmelCase  =				backbone.conv_stem.normalization.running_mean
				lowerCAmelCase  =				backbone.conv_stem.normalization.running_var

				for i in range(13 ):
								lowerCAmelCase  =				i + 1
								lowerCAmelCase  =				i * 2

								lowerCAmelCase  =				backbone.layer[pt_index]
								lowerCAmelCase  =				f'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
								lowerCAmelCase  =				pointer.convolution.weight
								lowerCAmelCase  =				pointer.normalization.bias
								lowerCAmelCase  =				pointer.normalization.weight
								lowerCAmelCase  =				pointer.normalization.running_mean
								lowerCAmelCase  =				pointer.normalization.running_var

								lowerCAmelCase  =				backbone.layer[pt_index + 1]
								lowerCAmelCase  =				f'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
								lowerCAmelCase  =				pointer.convolution.weight
								lowerCAmelCase  =				pointer.normalization.bias
								lowerCAmelCase  =				pointer.normalization.weight
								lowerCAmelCase  =				pointer.normalization.running_mean
								lowerCAmelCase  =				pointer.normalization.running_var

				if isinstance(snake_case_      ,       snake_case_ ):
								lowerCAmelCase  =				"""MobilenetV1/Logits/Conv2d_1c_1x1/"""
								lowerCAmelCase  =				model.classifier.weight
								lowerCAmelCase  =				model.classifier.bias

				return tf_to_pt_map





def 							lowercase					(snake_case__   : Optional[Any]      ,       snake_case__   : int      ,       snake_case__   : Dict )     ->  List[Any]:


				'''simple docstring'''






				try:
								import numpy as np
								import tensorflow as tf
				except ImportError:
								logger.error(
								    """Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see """
								    """https://www.tensorflow.org/install/ for installation instructions.""" )
								raise

				# Load weights from TF model
				lowerCAmelCase  =				tf.train.list_variables(snake_case_ )
				lowerCAmelCase  =				{}
				for name, shape in init_vars:
								logger.info(f'''Loading TF weight {name} with shape {shape}''' )
								lowerCAmelCase  =				tf.train.load_variable(snake_case_      ,       snake_case_ )
								lowerCAmelCase  =				array

				# Build TF to PyTorch weights loading map
				lowerCAmelCase  =				_build_tf_to_pytorch_map(snake_case_      ,       snake_case_      ,       snake_case_ )

				for name, pointer in tf_to_pt_map.items():
								logger.info(f'''Importing {name}''' )
								if name not in tf_weights:
												logger.info(f'''{name} not in tf pre-trained weights, skipping''' )
												continue

								lowerCAmelCase  =				tf_weights[name]

								if "depthwise_weights" in name:
												logger.info("""Transposing depthwise""" )
												lowerCAmelCase  =				np.transpose(snake_case_      ,       (2, 3, 0, 1) )
								elif "weights" in name:
												logger.info("""Transposing""" )
												if len(pointer.shape ) == 2:  # copying into linear layer
																lowerCAmelCase  =				array.squeeze().transpose()
												else:
																lowerCAmelCase  =				np.transpose(snake_case_      ,       (3, 2, 0, 1) )

								if pointer.shape != array.shape:
												raise ValueError(f'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched''' )

								logger.info(f'''Initialize PyTorch weight {name} {array.shape}''' )
								lowerCAmelCase  =				torch.from_numpy(snake_case_ )

								tf_weights.pop(snake_case_      ,       snake_case_ )
								tf_weights.pop(name + """/RMSProp"""      ,       snake_case_ )
								tf_weights.pop(name + """/RMSProp_1"""      ,       snake_case_ )
								tf_weights.pop(name + """/ExponentialMovingAverage"""      ,       snake_case_ )

				logger.info(f'''Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}''' )
				return model





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : Optional[int] )     ->  Tuple:


				'''simple docstring'''






				lowerCAmelCase  =				features.shape[-2:]
				lowerCAmelCase  =				conv_layer.stride
				lowerCAmelCase  =				conv_layer.kernel_size

				if in_height % stride_height == 0:
								lowerCAmelCase  =				max(kernel_height - stride_height      ,       0 )
				else:
								lowerCAmelCase  =				max(kernel_height - (in_height % stride_height)      ,       0 )

				if in_width % stride_width == 0:
								lowerCAmelCase  =				max(kernel_width - stride_width      ,       0 )
				else:
								lowerCAmelCase  =				max(kernel_width - (in_width % stride_width)      ,       0 )

				lowerCAmelCase  =				pad_along_width // 2
				lowerCAmelCase  =				pad_along_width - pad_left
				lowerCAmelCase  =				pad_along_height // 2
				lowerCAmelCase  =				pad_along_height - pad_top

				lowerCAmelCase  =				(pad_left, pad_right, pad_top, pad_bottom)
				return nn.functional.pad(snake_case_      ,       snake_case_      ,       """constant"""      ,       0.0 )






class       SCREAMING_SNAKE_CASE__					(					nn.Module					):






						def __init__(							self				:			Optional[int]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			str = 1						,						lowerCAmelCase				:			Optional[int] = 1						,						lowerCAmelCase				:			Dict = False						,						lowerCAmelCase				:			Optional[Any] = True						,						lowerCAmelCase				:			Dict = True						,						):
										super().__init__()
										lowerCAmelCase  =				config

										if in_channels % groups != 0:
														raise ValueError(f'''Input channels ({in_channels}) are not divisible by {groups} groups.'''					)
										if out_channels % groups != 0:
														raise ValueError(f'''Output channels ({out_channels}) are not divisible by {groups} groups.'''					)

										lowerCAmelCase  =				0 if config.tf_padding else int((kernel_size - 1) / 2					)

										lowerCAmelCase  =				nn.Convad(
										    in_channels=_a						,						out_channels=_a						,						kernel_size=_a						,						stride=_a						,						padding=_a						,						groups=_a						,						bias=_a						,						padding_mode="""zeros"""						,						)

										if use_normalization:
														lowerCAmelCase  =				nn.BatchNormad(
														    num_features=_a						,						eps=config.layer_norm_eps						,						momentum=0.9997						,						affine=_a						,						track_running_stats=_a						,						)
										else:
														lowerCAmelCase  =				None

										if use_activation:
														if isinstance(_a						,						_a					):
																		lowerCAmelCase  =				ACTaFN[use_activation]
														elif isinstance(config.hidden_act						,						_a					):
																		lowerCAmelCase  =				ACTaFN[config.hidden_act]
														else:
																		lowerCAmelCase  =				config.hidden_act
										else:
														lowerCAmelCase  =				None






						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			Optional[Any]					):
										if self.config.tf_padding:
														lowerCAmelCase  =				apply_tf_padding(_a						,						self.convolution					)
										lowerCAmelCase  =				self.convolution(_a					)
										if self.normalization is not None:
														lowerCAmelCase  =				self.normalization(_a					)
										if self.activation is not None:
														lowerCAmelCase  =				self.activation(_a					)
										return features






class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase__					):
						_a    						=						MobileNetVaConfig
						_a    						=						load_tf_weights_in_mobilenet_va
						_a    						=						'mobilenet_v1'
						_a    						=						'pixel_values'
						_a    						=						False






						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Dict					):
										if isinstance(_a						,						(nn.Linear, nn.Convad)					):
														module.weight.data.normal_(mean=0.0						,						std=self.config.initializer_range					)
														if module.bias is not None:
																		module.bias.data.zero_()
										elif isinstance(_a						,						nn.BatchNormad					):
														module.bias.data.zero_()
														module.weight.data.fill_(1.0					)





a      =							R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

a      =							R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'






@add_start_docstrings(
    'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'						,				UpperCamelCase__						,				)
class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase__					):






						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			List[Any] = True					):
										super().__init__(_a					)
										lowerCAmelCase  =				config

										lowerCAmelCase  =				32
										lowerCAmelCase  =				max(int(depth * config.depth_multiplier					)						,						config.min_depth					)

										lowerCAmelCase  =				MobileNetVaConvLayer(
										    _a						,						in_channels=config.num_channels						,						out_channels=_a						,						kernel_size=3						,						stride=2						,						)

										lowerCAmelCase  =				[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

										lowerCAmelCase  =				nn.ModuleList()
										for i in range(13					):
														lowerCAmelCase  =				out_channels

														if strides[i] == 2 or i == 0:
																		depth *= 2
																		lowerCAmelCase  =				max(int(depth * config.depth_multiplier					)						,						config.min_depth					)

														self.layer.append(
														    MobileNetVaConvLayer(
														        _a						,						in_channels=_a						,						out_channels=_a						,						kernel_size=3						,						stride=strides[i]						,						groups=_a						,						)					)

														self.layer.append(
														    MobileNetVaConvLayer(
														        _a						,						in_channels=_a						,						out_channels=_a						,						kernel_size=1						,						)					)

										lowerCAmelCase  =				nn.AdaptiveAvgPoolad((1, 1)					) if add_pooling_layer else None

										# Initialize weights and apply final processing
										self.post_init()






						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			List[Any]					):
										raise NotImplementedError






						@add_start_docstrings_to_model_forward(_a					)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC						,						output_type=_a						,						config_class=_CONFIG_FOR_DOC						,						modality="""vision"""						,						expected_output=_EXPECTED_OUTPUT_SHAPE						,						)
						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			List[str] = None						,						lowerCAmelCase				:			str = None						,						lowerCAmelCase				:			Union[str, Any] = None						,						):
										lowerCAmelCase  =				(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										lowerCAmelCase  =				return_dict if return_dict is not None else self.config.use_return_dict

										if pixel_values is None:
														raise ValueError("""You have to specify pixel_values"""					)

										lowerCAmelCase  =				self.conv_stem(_a					)

										lowerCAmelCase  =				() if output_hidden_states else None

										for i, layer_module in enumerate(self.layer					):
														lowerCAmelCase  =				layer_module(_a					)

														if output_hidden_states:
																		lowerCAmelCase  =				all_hidden_states + (hidden_states,)

										lowerCAmelCase  =				hidden_states

										if self.pooler is not None:
														lowerCAmelCase  =				torch.flatten(self.pooler(_a					)						,						start_dim=1					)
										else:
														lowerCAmelCase  =				None

										if not return_dict:
														return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None					)

										return BaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=_a						,						pooler_output=_a						,						hidden_states=_a						,						)






@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '						,				UpperCamelCase__						,				)
class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase__					):






						def __init__(							self				:			str						,						lowerCAmelCase				:			int					):
										super().__init__(_a					)

										lowerCAmelCase  =				config.num_labels
										lowerCAmelCase  =				MobileNetVaModel(_a					)

										lowerCAmelCase  =				self.mobilenet_va.layer[-1].convolution.out_channels

										# Classifier head
										lowerCAmelCase  =				nn.Dropout(config.classifier_dropout_prob						,						inplace=_a					)
										lowerCAmelCase  =				nn.Linear(_a						,						config.num_labels					) if config.num_labels > 0 else nn.Identity()

										# Initialize weights and apply final processing
										self.post_init()






						@add_start_docstrings_to_model_forward(_a					)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT						,						output_type=_a						,						config_class=_CONFIG_FOR_DOC						,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,						)
						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			str = None						,						lowerCAmelCase				:			Union[str, Any] = None						,						lowerCAmelCase				:			List[Any] = None						,						lowerCAmelCase				:			str = None						,						):
										lowerCAmelCase  =				return_dict if return_dict is not None else self.config.use_return_dict

										lowerCAmelCase  =				self.mobilenet_va(_a						,						output_hidden_states=_a						,						return_dict=_a					)

										lowerCAmelCase  =				outputs.pooler_output if return_dict else outputs[1]

										lowerCAmelCase  =				self.classifier(self.dropout(_a					)					)

										lowerCAmelCase  =				None
										if labels is not None:
														if self.config.problem_type is None:
																		if self.num_labels == 1:
																						lowerCAmelCase  =				"""regression"""
																		elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																						lowerCAmelCase  =				"""single_label_classification"""
																		else:
																						lowerCAmelCase  =				"""multi_label_classification"""

														if self.config.problem_type == "regression":
																		lowerCAmelCase  =				MSELoss()
																		if self.num_labels == 1:
																						lowerCAmelCase  =				loss_fct(logits.squeeze()						,						labels.squeeze()					)
																		else:
																						lowerCAmelCase  =				loss_fct(_a						,						_a					)
														elif self.config.problem_type == "single_label_classification":
																		lowerCAmelCase  =				CrossEntropyLoss()
																		lowerCAmelCase  =				loss_fct(logits.view(-1						,						self.num_labels					)						,						labels.view(-1					)					)
														elif self.config.problem_type == "multi_label_classification":
																		lowerCAmelCase  =				BCEWithLogitsLoss()
																		lowerCAmelCase  =				loss_fct(_a						,						_a					)

										if not return_dict:
														lowerCAmelCase  =				(logits,) + outputs[2:]
														return ((loss,) + output) if loss is not None else output

										return ImageClassifierOutputWithNoAttention(
										    loss=_a						,						logits=_a						,						hidden_states=outputs.hidden_states						,						)











"""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
a      =							'src/transformers'
a      =							'docs/source/en'
a      =							'.'





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Optional[int]      ,       snake_case__   : Tuple )     ->  Any:


				'''simple docstring'''






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

				lowerCAmelCase  =				start_index
				while not lines[end_index].startswith(snake_case__ ):
								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 |
a      =							'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
a      =							re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a      =							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.
a      =							re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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





def 							lowercase					(snake_case__   : int )     ->  Any:


				'''simple docstring'''






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





def 							lowercase					(snake_case__   : str      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				2 if text == """✅""" or text == """❌""" else len(snake_case__ )
				lowerCAmelCase  =				(width - text_length) // 2
				lowerCAmelCase  =				width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase  =				{
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				lowerCAmelCase  =				{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.
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )
				lowerCAmelCase  =				collections.defaultdict(snake_case__ )

				# Let's lookup through all transformers object (once).
				for attr_name in dir(snake_case__ ):
								lowerCAmelCase  =				None
								if attr_name.endswith("""Tokenizer""" ):
												lowerCAmelCase  =				slow_tokenizers
												lowerCAmelCase  =				attr_name[:-9]
								elif attr_name.endswith("""TokenizerFast""" ):
												lowerCAmelCase  =				fast_tokenizers
												lowerCAmelCase  =				attr_name[:-13]
								elif _re_tf_models.match(snake_case__ ) is not None:
												lowerCAmelCase  =				tf_models
												lowerCAmelCase  =				_re_tf_models.match(snake_case__ ).groups()[0]
								elif _re_flax_models.match(snake_case__ ) is not None:
												lowerCAmelCase  =				flax_models
												lowerCAmelCase  =				_re_flax_models.match(snake_case__ ).groups()[0]
								elif _re_pt_models.match(snake_case__ ) is not None:
												lowerCAmelCase  =				pt_models
												lowerCAmelCase  =				_re_pt_models.match(snake_case__ ).groups()[0]

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

    # Let's build that table!
				lowerCAmelCase  =				list(model_name_to_config.keys() )
				model_names.sort(key=str.lower )
				lowerCAmelCase  =				["""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  =				[len(snake_case__ ) + 2 for c in columns]
				lowerCAmelCase  =				max([len(snake_case__ ) for name in model_names] ) + 2

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

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





def 							lowercase					(snake_case__   : Dict=False )     ->  Any:


				'''simple docstring'''






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

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(snake_case__      ,       """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__":
		a      =							argparse.ArgumentParser()
		parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
		a      =							parser.parse_args()

		check_model_table(args.fix_and_overwrite)





