





'''simple docstring'''






from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


A :			int							   =      logging.get_logger(__name__)

# General docstring
A :			List[str]							   =      'ResNetConfig'

# Base docstring
A :			Dict							   =      'microsoft/resnet-50'
A :			Any							   =      [1, 2_048, 7, 7]

# Image classification docstring
A :			Union[str, Any]							   =      'microsoft/resnet-50'
A :			Any							   =      'tiger cat'

A :			List[str]							   =      [
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class       lowerCamelCase       (    tf.keras.layers.Layer	):


					def __init__(       self						:	Optional[int]							,  __snake_case						:	Dict							,  __snake_case						:	Optional[Any] = 3							,  __snake_case						:	List[str] = 1							,  __snake_case						:	Union[str, Any] = "relu"							,  **__snake_case						:	List[Any]							):





											'''simple docstring'''





											super().__init__(**__lowercase							)
											_snake_case: Union[str, Any]	       =		kernel_size // 2
											_snake_case: Optional[int]	       =		tf.keras.layers.ConvaD(
											    __lowercase							,  kernel_size=__lowercase							,  strides=__lowercase							,  padding='valid'							,  use_bias=__lowercase							,  name='convolution'							)
											# Use same default momentum and epsilon as PyTorch equivalent
											_snake_case: Dict	       =		tf.keras.layers.BatchNormalization(epsilon=1e-5							,  momentum=0.9							,  name='normalization'							)
											_snake_case: List[Any]	       =		ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear'							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  __snake_case						:	int							):





											'''simple docstring'''





											_snake_case: Tuple	       =		(self.pad_value, self.pad_value)
											_snake_case: List[str]	       =		tf.pad(__lowercase							,  [(0, 0), height_pad, width_pad, (0, 0)]							)
											_snake_case: List[str]	       =		self.conv(__lowercase							)
											return hidden_state




					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	Optional[Any]							,  __snake_case						:	str = False							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.convolution(__lowercase							)
											_snake_case: Optional[int]	       =		self.normalization(__lowercase							,  training=__lowercase							)
											_snake_case: int	       =		self.activation(__lowercase							)
											return hidden_state







class       lowerCamelCase       (    tf.keras.layers.Layer	):


					def __init__(       self						:	List[Any]							,  __snake_case						:	Dict							,  **__snake_case						:	List[Any]							):





											'''simple docstring'''





											super().__init__(**__lowercase							)
											_snake_case: int	       =		TFResNetConvLayer(
											    config.embedding_size							,  kernel_size=7							,  stride=2							,  activation=config.hidden_act							,  name='embedder'							,  )
											_snake_case: Dict	       =		tf.keras.layers.MaxPoolaD(pool_size=3							,  strides=2							,  padding='valid'							,  name='pooler'							)
											_snake_case: int	       =		config.num_channels




					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  __snake_case						:	int							,  __snake_case						:	Union[str, Any] = False							):





											'''simple docstring'''





											_snake_case: List[str]	       =		shape_list(__lowercase							)
											if tf.executing_eagerly() and num_channels != self.num_channels:
																	raise ValueError(
																	    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'							)
											_snake_case: str	       =		pixel_values
											_snake_case: Any	       =		self.embedder(__lowercase							)
											_snake_case: Optional[int]	       =		tf.pad(__lowercase							,  [[0, 0], [1, 1], [1, 1], [0, 0]]							)
											_snake_case: List[str]	       =		self.pooler(__lowercase							)
											return hidden_state







class       lowerCamelCase       (    tf.keras.layers.Layer	):


					def __init__(       self						:	List[str]							,  __snake_case						:	Tuple							,  __snake_case						:	Tuple = 2							,  **__snake_case						:	List[Any]							):





											'''simple docstring'''





											super().__init__(**__lowercase							)
											_snake_case: List[Any]	       =		tf.keras.layers.ConvaD(
											    __lowercase							,  kernel_size=1							,  strides=__lowercase							,  use_bias=__lowercase							,  name='convolution'							)
											# Use same default momentum and epsilon as PyTorch equivalent
											_snake_case: Optional[Any]	       =		tf.keras.layers.BatchNormalization(epsilon=1e-5							,  momentum=0.9							,  name='normalization'							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	str							,  __snake_case						:	int = False							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		x
											_snake_case: str	       =		self.convolution(__lowercase							)
											_snake_case: int	       =		self.normalization(__lowercase							,  training=__lowercase							)
											return hidden_state







class       lowerCamelCase       (    tf.keras.layers.Layer	):


					def __init__(       self						:	Optional[int]							,  __snake_case						:	int							,  __snake_case						:	Tuple							,  __snake_case						:	Tuple = 1							,  __snake_case						:	List[Any] = "relu"							,  **__snake_case						:	Any							):





											'''simple docstring'''





											super().__init__(**__lowercase							)
											_snake_case: int	       =		in_channels != out_channels or stride != 1
											_snake_case: str	       =		TFResNetConvLayer(__lowercase							,  stride=__lowercase							,  name='layer.0'							)
											_snake_case: str	       =		TFResNetConvLayer(__lowercase							,  activation=__lowercase							,  name='layer.1'							)
											_snake_case: Any	       =		(
											    TFResNetShortCut(__lowercase							,  stride=__lowercase							,  name='shortcut'							)
											    if should_apply_shortcut
											    else tf.keras.layers.Activation('linear'							,  name='shortcut'							)
											)
											_snake_case: Optional[Any]	       =		ACTaFN[activation]




					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	List[str]							,  __snake_case						:	Optional[Any] = False							):





											'''simple docstring'''





											_snake_case: Dict	       =		hidden_state
											_snake_case: Union[str, Any]	       =		self.conva(__lowercase							,  training=__lowercase							)
											_snake_case: Tuple	       =		self.conva(__lowercase							,  training=__lowercase							)
											_snake_case: Dict	       =		self.shortcut(__lowercase							,  training=__lowercase							)
											hidden_state += residual
											_snake_case: List[Any]	       =		self.activation(__lowercase							)
											return hidden_state







class       lowerCamelCase       (    tf.keras.layers.Layer	):


					def __init__(       self						:	Optional[int]							,  __snake_case						:	Tuple							,  __snake_case						:	List[Any]							,  __snake_case						:	Union[str, Any] = 1							,  __snake_case						:	Tuple = "relu"							,  __snake_case						:	int = 4							,  **__snake_case						:	Union[str, Any]							,  ):





											'''simple docstring'''





											super().__init__(**__lowercase							)
											_snake_case: Dict	       =		in_channels != out_channels or stride != 1
											_snake_case: Union[str, Any]	       =		out_channels // reduction
											_snake_case: Optional[Any]	       =		TFResNetConvLayer(__lowercase							,  kernel_size=1							,  name='layer.0'							)
											_snake_case: int	       =		TFResNetConvLayer(__lowercase							,  stride=__lowercase							,  name='layer.1'							)
											_snake_case: Dict	       =		TFResNetConvLayer(__lowercase							,  kernel_size=1							,  activation=__lowercase							,  name='layer.2'							)
											_snake_case: Union[str, Any]	       =		(
											    TFResNetShortCut(__lowercase							,  stride=__lowercase							,  name='shortcut'							)
											    if should_apply_shortcut
											    else tf.keras.layers.Activation('linear'							,  name='shortcut'							)
											)
											_snake_case: List[str]	       =		ACTaFN[activation]




					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	str							,  __snake_case						:	Union[str, Any] = False							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		hidden_state
											_snake_case: Optional[int]	       =		self.conva(__lowercase							,  training=__lowercase							)
											_snake_case: Tuple	       =		self.conva(__lowercase							,  training=__lowercase							)
											_snake_case: List[Any]	       =		self.conva(__lowercase							,  training=__lowercase							)
											_snake_case: List[str]	       =		self.shortcut(__lowercase							,  training=__lowercase							)
											hidden_state += residual
											_snake_case: Dict	       =		self.activation(__lowercase							)
											return hidden_state







class       lowerCamelCase       (    tf.keras.layers.Layer	):


					def __init__(       self						:	List[Any]							,  __snake_case						:	List[str]							,  __snake_case						:	str							,  __snake_case						:	Tuple							,  __snake_case						:	Dict = 2							,  __snake_case						:	List[str] = 2							,  **__snake_case						:	List[Any]							):





											'''simple docstring'''





											super().__init__(**__lowercase							)

											_snake_case: Tuple	       =		TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

											_snake_case: Tuple	       =		[layer(__lowercase							,  __lowercase							,  stride=__lowercase							,  activation=config.hidden_act							,  name='layers.0'							)]
											layers += [
											    layer(__lowercase							,  __lowercase							,  activation=config.hidden_act							,  name=f'''layers.{i + 1}'''							)
											    for i in range(depth - 1							)
											]
											_snake_case: List[str]	       =		layers




					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	str = False							):





											'''simple docstring'''





											for layer in self.stage_layers:
																	_snake_case: List[str]	       =		layer(__lowercase							,  training=__lowercase							)
											return hidden_state







class       lowerCamelCase       (    tf.keras.layers.Layer	):


					def __init__(       self						:	Tuple							,  __snake_case						:	List[str]							,  **__snake_case						:	Optional[int]							):





											'''simple docstring'''





											super().__init__(**__lowercase							)
											# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
											_snake_case: str	       =		[
											    TFResNetStage(
											        __lowercase							,  config.embedding_size							,  config.hidden_sizes[0]							,  stride=2 if config.downsample_in_first_stage else 1							,  depth=config.depths[0]							,  name='stages.0'							,  )
											]
											for i, (in_channels, out_channels, depth) in enumerate(
											    zip(config.hidden_sizes							,  config.hidden_sizes[1:]							,  config.depths[1:]							)							):
																	self.stages.append(TFResNetStage(__lowercase							,  __lowercase							,  __lowercase							,  depth=__lowercase							,  name=f'''stages.{i + 1}'''							)							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	List[str]							,  __snake_case						:	List[str] = False							,  __snake_case						:	Dict = True							,  __snake_case						:	int = False							,  ):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		() if output_hidden_states else None

											for stage_module in self.stages:
																	if output_hidden_states:
																							_snake_case: List[str]	       =		hidden_states + (hidden_state,)

																	_snake_case: int	       =		stage_module(__lowercase							,  training=__lowercase							)

											if output_hidden_states:
																	_snake_case: List[str]	       =		hidden_states + (hidden_state,)

											if not return_dict:
																	return tuple(v for v in [hidden_state, hidden_states] if v is not None							)

											return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowercase							,  hidden_states=__lowercase							)







class       lowerCamelCase       (    _A	):
					_SCREAMING_SNAKE_CASE				 =     ResNetConfig
					_SCREAMING_SNAKE_CASE				 =     "resnet"
					_SCREAMING_SNAKE_CASE				 =     "pixel_values"


					@property
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)							,  dtype=tf.floataa							)}





A :			Any							   =      r'\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'


A :			Union[str, Any]							   =      r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\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'







@keras_serializable
class       lowerCamelCase       (    tf.keras.layers.Layer	):
					_SCREAMING_SNAKE_CASE				 =     ResNetConfig


					def __init__(       self						:	List[Any]							,  __snake_case						:	Any							,  **__snake_case						:	int							):





											'''simple docstring'''





											super().__init__(**__lowercase							)
											_snake_case: List[str]	       =		config
											_snake_case: Any	       =		TFResNetEmbeddings(__lowercase							,  name='embedder'							)
											_snake_case: int	       =		TFResNetEncoder(__lowercase							,  name='encoder'							)
											_snake_case: List[str]	       =		tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase							)




					@unpack_inputs
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Optional[int] = None							,  __snake_case						:	Union[str, Any] = None							,  __snake_case						:	str = False							,  ):





											'''simple docstring'''





											_snake_case: Any	       =		(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											_snake_case: str	       =		return_dict if return_dict is not None else self.config.use_return_dict

											# TF 2.0 image layers can't use NCHW format when running on CPU.
											# We transpose to NHWC format and then transpose back after the full forward pass.
											# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
											_snake_case: Any	       =		tf.transpose(__lowercase							,  perm=[0, 2, 3, 1]							)
											_snake_case: Any	       =		self.embedder(__lowercase							,  training=__lowercase							)

											_snake_case: int	       =		self.encoder(
											    __lowercase							,  output_hidden_states=__lowercase							,  return_dict=__lowercase							,  training=__lowercase							)

											_snake_case: Union[str, Any]	       =		encoder_outputs[0]

											_snake_case: Any	       =		self.pooler(__lowercase							)

											# Transpose all the outputs to the NCHW format
											# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
											_snake_case: List[str]	       =		tf.transpose(__lowercase							,  (0, 3, 1, 2)							)
											_snake_case: Optional[Any]	       =		tf.transpose(__lowercase							,  (0, 3, 1, 2)							)
											_snake_case: Union[str, Any]	       =		()
											for hidden_state in encoder_outputs[1:]:
																	_snake_case: str	       =		hidden_states + tuple(tf.transpose(__lowercase							,  (0, 3, 1, 2)							) for h in hidden_state							)

											if not return_dict:
																	return (last_hidden_state, pooled_output) + hidden_states

											_snake_case: Dict	       =		hidden_states if output_hidden_states else None

											return TFBaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=__lowercase							,  pooler_output=__lowercase							,  hidden_states=__lowercase							,  )







@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."  ,				_A  ,				)
class       lowerCamelCase       (    _A	):


					def __init__(       self						:	Tuple							,  __snake_case						:	Optional[int]							,  **__snake_case						:	List[Any]							):





											'''simple docstring'''





											super().__init__(__lowercase							,  **__lowercase							)
											_snake_case: int	       =		TFResNetMainLayer(config=__lowercase							,  name='resnet'							)




					@add_start_docstrings_to_model_forward(__lowercase							)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC							,  output_type=__lowercase							,  config_class=_CONFIG_FOR_DOC							,  modality='vision'							,  expected_output=_EXPECTED_OUTPUT_SHAPE							,  )
					@unpack_inputs
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							,  __snake_case						:	int							,  __snake_case						:	Optional[Any] = None							,  __snake_case						:	List[str] = None							,  __snake_case						:	List[str] = False							,  ):





											'''simple docstring'''





											_snake_case: str	       =		(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											_snake_case: List[Any]	       =		return_dict if return_dict is not None else self.config.use_return_dict

											_snake_case: Optional[int]	       =		self.resnet(
											    pixel_values=__lowercase							,  output_hidden_states=__lowercase							,  return_dict=__lowercase							,  training=__lowercase							,  )
											return resnet_outputs







@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "  ,				_A  ,				)
class       lowerCamelCase       (    _A  ,				_A	):


					def __init__(       self						:	Any							,  __snake_case						:	Optional[int]							,  **__snake_case						:	List[Any]							):





											'''simple docstring'''





											super().__init__(__lowercase							,  **__lowercase							)
											_snake_case: List[str]	       =		config.num_labels
											_snake_case: List[Any]	       =		TFResNetMainLayer(__lowercase							,  name='resnet'							)
											# classification head
											_snake_case: str	       =		(
											    tf.keras.layers.Dense(config.num_labels							,  name='classifier.1'							)
											    if config.num_labels > 0
											    else tf.keras.layers.Activation('linear'							,  name='classifier.1'							)
											)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	int							):





											'''simple docstring'''





											_snake_case: Any	       =		tf.keras.layers.Flatten()(__lowercase							)
											_snake_case: Tuple	       =		self.classifier_layer(__lowercase							)
											return logits




					@add_start_docstrings_to_model_forward(__lowercase							)
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT							,  output_type=__lowercase							,  config_class=_CONFIG_FOR_DOC							,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,  )
					@unpack_inputs
					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	int = None							,  __snake_case						:	Dict = None							,  __snake_case						:	Optional[Any] = None							,  __snake_case						:	Dict = None							,  __snake_case						:	Tuple = False							,  ):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		return_dict if return_dict is not None else self.config.use_return_dict

											_snake_case: Tuple	       =		self.resnet(
											    __lowercase							,  output_hidden_states=__lowercase							,  return_dict=__lowercase							,  training=__lowercase							)

											_snake_case: Any	       =		outputs.pooler_output if return_dict else outputs[1]

											_snake_case: Optional[int]	       =		self.classifier(__lowercase							)

											_snake_case: Optional[int]	       =		None if labels is None else self.hf_compute_loss(__lowercase							,  __lowercase							)

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

											return TFImageClassifierOutputWithNoAttention(loss=__lowercase							,  logits=__lowercase							,  hidden_states=outputs.hidden_states							)












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


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		with open(SCREAMING_SNAKE_CASE_				,							'''r'''				,							encoding='''utf-8'''				,							newline='''\n'''		) as f:
				lowerCamelCase :			List[str]    		=f.readlines()
		# Find the start prompt.
		lowerCamelCase :			List[Any]    		=0
		while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_		):
				start_index += 1
		start_index += 1

		lowerCamelCase :			Dict    		=start_index
		while not lines[end_index].startswith(SCREAMING_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 |
snake_case_				=      '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
snake_case_				=      re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
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.
snake_case_				=      re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Tuple:
		lowerCamelCase :			Optional[Any]    		=re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''				,							SCREAMING_SNAKE_CASE_		)
		return [m.group(0		) for m in matches]


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		lowerCamelCase :			Dict    		=2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Union[str, Any]    		=(width - text_length) // 2
		lowerCamelCase :			List[str]    		=width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent


def        A__						(							) ->  Union[str, Any]:
		lowerCamelCase :			List[str]    		=diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		lowerCamelCase :			List[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 :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			str    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Any    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Optional[int]    		=collections.defaultdict(SCREAMING_SNAKE_CASE_		)

		# Let's lookup through all diffusers object (once).
		for attr_name in dir(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase :			Union[str, Any]    		=None
				if attr_name.endswith('''Tokenizer'''		):
						lowerCamelCase :			List[str]    		=slow_tokenizers
						lowerCamelCase :			List[Any]    		=attr_name[:-9]
				elif attr_name.endswith('''TokenizerFast'''		):
						lowerCamelCase :			Any    		=fast_tokenizers
						lowerCamelCase :			Optional[Any]    		=attr_name[:-1_3]
				elif _re_tf_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			Optional[Any]    		=tf_models
						lowerCamelCase :			Any    		=_re_tf_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_flax_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[str]    		=flax_models
						lowerCamelCase :			int    		=_re_flax_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]
				elif _re_pt_models.match(SCREAMING_SNAKE_CASE_		) is not None:
						lowerCamelCase :			List[Any]    		=pt_models
						lowerCamelCase :			List[Any]    		=_re_pt_models.match(SCREAMING_SNAKE_CASE_		).groups()[0]

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

    # Let's build that table!
		lowerCamelCase :			Optional[int]    		=list(model_name_to_config.keys()		)
		model_names.sort(key=str.lower		)
		lowerCamelCase :			Union[str, Any]    		=['''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 :			Tuple    		=[len(SCREAMING_SNAKE_CASE_		) + 2 for c in columns]
		lowerCamelCase :			Dict    		=max([len(SCREAMING_SNAKE_CASE_		) for name in model_names]		) + 2

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

		lowerCamelCase :			Optional[Any]    		={True: '''✅''', False: '''❌'''}
		for name in model_names:
				lowerCamelCase :			str    		=model_name_to_prefix[name]
				lowerCamelCase :			Dict    		=[
				    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(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) for l, w in zip(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)]		) + "|\n"
		return table


def        A__						(							SCREAMING_SNAKE_CASE_=False		) ->  int:
		lowerCamelCase		, lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Tuple    		=_find_text_in_file(
		    filename=os.path.join(SCREAMING_SNAKE_CASE_				,							'''index.mdx'''		)				,							start_prompt='''<!--This table is updated automatically from the auto modules'''				,							end_prompt='''<!-- End table-->'''				,							)
		lowerCamelCase :			List[Any]    		=get_model_table_from_auto_modules()

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

				check_model_table(args.fix_and_overwrite)

