




import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel






def 					snake_case					(							A						,   A=1			)							->							Optional[Any]:




	'''simple docstring'''


	if n_shave_prefix_segments >= 0:
		return ".".join(path.split('.'			)[n_shave_prefix_segments:]			)
	else:
		return ".".join(path.split('.'			)[:n_shave_prefix_segments]			)






def 					snake_case					(							A						,   A=0			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[Any]							   =							[]
	for old_item in old_list:
		lowerCamelCase_							:		Union[str, Any]							   =							old_item
		lowerCamelCase_							:		List[Any]							   =							new_item.replace('block.'						,   'resnets.'			)
		lowerCamelCase_							:		int							   =							new_item.replace('conv_shorcut'						,   'conv1'			)
		lowerCamelCase_							:		Optional[int]							   =							new_item.replace('in_shortcut'						,   'conv_shortcut'			)
		lowerCamelCase_							:		int							   =							new_item.replace('temb_proj'						,   'time_emb_proj'			)

		lowerCamelCase_							:		str							   =							shave_segments(A						,   n_shave_prefix_segments=A			)

		mapping.append({'old': old_item, 'new': new_item}			)

	return mapping






def 					snake_case					(							A						,   A=0						,   A=False			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							   =							[]
	for old_item in old_list:
		lowerCamelCase_							:		int							   =							old_item

		# In `model.mid`, the layer is called `attn`.
		if not in_mid:
			lowerCamelCase_							:		Dict							   =							new_item.replace('attn'						,   'attentions'			)
		lowerCamelCase_							:		List[str]							   =							new_item.replace('.k.'						,   '.key.'			)
		lowerCamelCase_							:		Tuple							   =							new_item.replace('.v.'						,   '.value.'			)
		lowerCamelCase_							:		List[str]							   =							new_item.replace('.q.'						,   '.query.'			)

		lowerCamelCase_							:		str							   =							new_item.replace('proj_out'						,   'proj_attn'			)
		lowerCamelCase_							:		Optional[Any]							   =							new_item.replace('norm'						,   'group_norm'			)

		lowerCamelCase_							:		Optional[Any]							   =							shave_segments(A						,   n_shave_prefix_segments=A			)
		mapping.append({'old': old_item, 'new': new_item}			)

	return mapping






def 					snake_case					(							A						,   A						,   A						,   A=None						,   A=None						,   A=None			)							->							Any:




	'''simple docstring'''


	assert isinstance(A						,   A			), "Paths should be a list of dicts containing 'old' and 'new' keys."

	if attention_paths_to_split is not None:
		if config is None:
			raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'			)

		for path, path_map in attention_paths_to_split.items():
			lowerCamelCase_							:		Tuple							   =							old_checkpoint[path]
			lowerCamelCase_							:		List[str]							   =							old_tensor.shape[0] // 3

			lowerCamelCase_							:		List[str]							   =							(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

			lowerCamelCase_							:		Optional[int]							   =							old_tensor.shape[0] // config.get('num_head_channels'						,   1			) // 3

			lowerCamelCase_							:		int							   =							old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Dict							   =							old_tensor.split(channels // num_heads						,   dim=1			)

			lowerCamelCase_							:		Optional[Any]							   =							query.reshape(A			).squeeze()
			lowerCamelCase_							:		Any							   =							key.reshape(A			).squeeze()
			lowerCamelCase_							:		List[Any]							   =							value.reshape(A			).squeeze()

	for path in paths:
		lowerCamelCase_							:		List[str]							   =							path['new']

		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
			continue

		lowerCamelCase_							:		Union[str, Any]							   =							new_path.replace('down.'						,   'down_blocks.'			)
		lowerCamelCase_							:		Any							   =							new_path.replace('up.'						,   'up_blocks.'			)

		if additional_replacements is not None:
			for replacement in additional_replacements:
				lowerCamelCase_							:		int							   =							new_path.replace(replacement['old']						,   replacement['new']			)

		if "attentions" in new_path:
			lowerCamelCase_							:		Any							   =							old_checkpoint[path['old']].squeeze()
		else:
			lowerCamelCase_							:		Optional[int]							   =							old_checkpoint[path['old']]






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




	'''simple docstring'''


	lowerCamelCase_							:		Dict							   =							{}

	lowerCamelCase_							:		Optional[Any]							   =							checkpoint['temb.dense.0.weight']
	lowerCamelCase_							:		Union[str, Any]							   =							checkpoint['temb.dense.0.bias']
	lowerCamelCase_							:		Tuple							   =							checkpoint['temb.dense.1.weight']
	lowerCamelCase_							:		str							   =							checkpoint['temb.dense.1.bias']

	lowerCamelCase_							:		str							   =							checkpoint['norm_out.weight']
	lowerCamelCase_							:		Union[str, Any]							   =							checkpoint['norm_out.bias']

	lowerCamelCase_							:		Optional[Any]							   =							checkpoint['conv_in.weight']
	lowerCamelCase_							:		int							   =							checkpoint['conv_in.bias']
	lowerCamelCase_							:		str							   =							checkpoint['conv_out.weight']
	lowerCamelCase_							:		List[Any]							   =							checkpoint['conv_out.bias']

	lowerCamelCase_							:		str							   =							len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'down' in layer}			)
	lowerCamelCase_							:		int							   =							{
	    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(A			)
	}

	lowerCamelCase_							:		Dict							   =							len({'.'.join(layer.split('.'			)[:2]			) for layer in checkpoint if 'up' in layer}			)
	lowerCamelCase_							:		Optional[Any]							   =							{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(A			)}

	for i in range(A			):
		lowerCamelCase_							:		Dict							   =							(i - 1) // (config['layers_per_block'] + 1)

		if any('downsample' in layer for layer in down_blocks[i]			):
			lowerCamelCase_							:		Union[str, Any]							   =							checkpoint[
			    f"down.{i}.downsample.op.weight"
			]
			lowerCamelCase_							:		Union[str, Any]							   =							checkpoint[f"down.{i}.downsample.op.bias"]
		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
		#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

		if any('block' in layer for layer in down_blocks[i]			):
			lowerCamelCase_							:		Optional[int]							   =							len(
			    {'.'.join(shave_segments(A						,   2			).split('.'			)[:2]			) for layer in down_blocks[i] if 'block' in layer}			)
			lowerCamelCase_							:		Optional[int]							   =							{
			    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
			    for layer_id in range(A			)
			}

			if num_blocks > 0:
				for j in range(config['layers_per_block']			):
					lowerCamelCase_							:		List[Any]							   =							renew_resnet_paths(blocks[j]			)
					assign_to_checkpoint(A						,   A						,   A			)

		if any('attn' in layer for layer in down_blocks[i]			):
			lowerCamelCase_							:		Tuple							   =							len(
			    {'.'.join(shave_segments(A						,   2			).split('.'			)[:2]			) for layer in down_blocks[i] if 'attn' in layer}			)
			lowerCamelCase_							:		str							   =							{
			    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
			    for layer_id in range(A			)
			}

			if num_attn > 0:
				for j in range(config['layers_per_block']			):
					lowerCamelCase_							:		str							   =							renew_attention_paths(attns[j]			)
					assign_to_checkpoint(A						,   A						,   A						,   config=A			)

	lowerCamelCase_							:		Optional[Any]							   =							[key for key in checkpoint if 'mid.block_1' in key]
	lowerCamelCase_							:		Tuple							   =							[key for key in checkpoint if 'mid.block_2' in key]
	lowerCamelCase_							:		List[Any]							   =							[key for key in checkpoint if 'mid.attn_1' in key]

	# Mid new 2
	lowerCamelCase_							:		List[Any]							   =							renew_resnet_paths(A			)
	assign_to_checkpoint(
	    A						,   A						,   A						,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]						,   )

	lowerCamelCase_							:		Optional[int]							   =							renew_resnet_paths(A			)
	assign_to_checkpoint(
	    A						,   A						,   A						,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]						,   )

	lowerCamelCase_							:		Any							   =							renew_attention_paths(A						,   in_mid=A			)
	assign_to_checkpoint(
	    A						,   A						,   A						,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]						,   )

	for i in range(A			):
		lowerCamelCase_							:		Dict							   =							num_up_blocks - 1 - i

		if any('upsample' in layer for layer in up_blocks[i]			):
			lowerCamelCase_							:		int							   =							checkpoint[
			    f"up.{i}.upsample.conv.weight"
			]
			lowerCamelCase_							:		Optional[int]							   =							checkpoint[f"up.{i}.upsample.conv.bias"]

		if any('block' in layer for layer in up_blocks[i]			):
			lowerCamelCase_							:		List[Any]							   =							len(
			    {'.'.join(shave_segments(A						,   2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'block' in layer}			)
			lowerCamelCase_							:		Dict							   =							{
			    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(A			)
			}

			if num_blocks > 0:
				for j in range(config['layers_per_block'] + 1			):
					lowerCamelCase_							:		List[str]							   =							{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
					lowerCamelCase_							:		Any							   =							renew_resnet_paths(blocks[j]			)
					assign_to_checkpoint(A						,   A						,   A						,   additional_replacements=[replace_indices]			)

		if any('attn' in layer for layer in up_blocks[i]			):
			lowerCamelCase_							:		int							   =							len(
			    {'.'.join(shave_segments(A						,   2			).split('.'			)[:2]			) for layer in up_blocks[i] if 'attn' in layer}			)
			lowerCamelCase_							:		Optional[int]							   =							{
			    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(A			)
			}

			if num_attn > 0:
				for j in range(config['layers_per_block'] + 1			):
					lowerCamelCase_							:		List[str]							   =							{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
					lowerCamelCase_							:		str							   =							renew_attention_paths(attns[j]			)
					assign_to_checkpoint(A						,   A						,   A						,   additional_replacements=[replace_indices]			)

	lowerCamelCase_							:		Optional[Any]							   =							{k.replace('mid_new_2'						,   'mid_block'			): v for k, v in new_checkpoint.items()}
	return new_checkpoint






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




	'''simple docstring'''


	lowerCamelCase_							:		Optional[int]							   =							{}

	lowerCamelCase_							:		Tuple							   =							checkpoint['encoder.norm_out.weight']
	lowerCamelCase_							:		Optional[int]							   =							checkpoint['encoder.norm_out.bias']

	lowerCamelCase_							:		Dict							   =							checkpoint['encoder.conv_in.weight']
	lowerCamelCase_							:		Optional[Any]							   =							checkpoint['encoder.conv_in.bias']
	lowerCamelCase_							:		List[Any]							   =							checkpoint['encoder.conv_out.weight']
	lowerCamelCase_							:		Any							   =							checkpoint['encoder.conv_out.bias']

	lowerCamelCase_							:		Dict							   =							checkpoint['decoder.norm_out.weight']
	lowerCamelCase_							:		Any							   =							checkpoint['decoder.norm_out.bias']

	lowerCamelCase_							:		Optional[int]							   =							checkpoint['decoder.conv_in.weight']
	lowerCamelCase_							:		Dict							   =							checkpoint['decoder.conv_in.bias']
	lowerCamelCase_							:		str							   =							checkpoint['decoder.conv_out.weight']
	lowerCamelCase_							:		List[Any]							   =							checkpoint['decoder.conv_out.bias']

	lowerCamelCase_							:		Dict							   =							len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'down' in layer}			)
	lowerCamelCase_							:		List[Any]							   =							{
	    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(A			)
	}

	lowerCamelCase_							:		List[str]							   =							len({'.'.join(layer.split('.'			)[:3]			) for layer in checkpoint if 'up' in layer}			)
	lowerCamelCase_							:		Dict							   =							{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(A			)}

	for i in range(A			):
		lowerCamelCase_							:		Optional[Any]							   =							(i - 1) // (config['layers_per_block'] + 1)

		if any('downsample' in layer for layer in down_blocks[i]			):
			lowerCamelCase_							:		Optional[Any]							   =							checkpoint[
			    f"encoder.down.{i}.downsample.conv.weight"
			]
			lowerCamelCase_							:		Optional[int]							   =							checkpoint[
			    f"encoder.down.{i}.downsample.conv.bias"
			]

		if any('block' in layer for layer in down_blocks[i]			):
			lowerCamelCase_							:		List[Any]							   =							len(
			    {'.'.join(shave_segments(A						,   3			).split('.'			)[:3]			) for layer in down_blocks[i] if 'block' in layer}			)
			lowerCamelCase_							:		str							   =							{
			    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
			    for layer_id in range(A			)
			}

			if num_blocks > 0:
				for j in range(config['layers_per_block']			):
					lowerCamelCase_							:		str							   =							renew_resnet_paths(blocks[j]			)
					assign_to_checkpoint(A						,   A						,   A			)

		if any('attn' in layer for layer in down_blocks[i]			):
			lowerCamelCase_							:		str							   =							len(
			    {'.'.join(shave_segments(A						,   3			).split('.'			)[:3]			) for layer in down_blocks[i] if 'attn' in layer}			)
			lowerCamelCase_							:		Union[str, Any]							   =							{
			    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
			    for layer_id in range(A			)
			}

			if num_attn > 0:
				for j in range(config['layers_per_block']			):
					lowerCamelCase_							:		Union[str, Any]							   =							renew_attention_paths(attns[j]			)
					assign_to_checkpoint(A						,   A						,   A						,   config=A			)

	lowerCamelCase_							:		str							   =							[key for key in checkpoint if 'mid.block_1' in key]
	lowerCamelCase_							:		List[str]							   =							[key for key in checkpoint if 'mid.block_2' in key]
	lowerCamelCase_							:		Any							   =							[key for key in checkpoint if 'mid.attn_1' in key]

	# Mid new 2
	lowerCamelCase_							:		Dict							   =							renew_resnet_paths(A			)
	assign_to_checkpoint(
	    A						,   A						,   A						,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]						,   )

	lowerCamelCase_							:		Optional[Any]							   =							renew_resnet_paths(A			)
	assign_to_checkpoint(
	    A						,   A						,   A						,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]						,   )

	lowerCamelCase_							:		Optional[int]							   =							renew_attention_paths(A						,   in_mid=A			)
	assign_to_checkpoint(
	    A						,   A						,   A						,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]						,   )

	for i in range(A			):
		lowerCamelCase_							:		int							   =							num_up_blocks - 1 - i

		if any('upsample' in layer for layer in up_blocks[i]			):
			lowerCamelCase_							:		List[Any]							   =							checkpoint[
			    f"decoder.up.{i}.upsample.conv.weight"
			]
			lowerCamelCase_							:		Union[str, Any]							   =							checkpoint[
			    f"decoder.up.{i}.upsample.conv.bias"
			]

		if any('block' in layer for layer in up_blocks[i]			):
			lowerCamelCase_							:		Tuple							   =							len(
			    {'.'.join(shave_segments(A						,   3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'block' in layer}			)
			lowerCamelCase_							:		Dict							   =							{
			    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(A			)
			}

			if num_blocks > 0:
				for j in range(config['layers_per_block'] + 1			):
					lowerCamelCase_							:		int							   =							{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
					lowerCamelCase_							:		List[Any]							   =							renew_resnet_paths(blocks[j]			)
					assign_to_checkpoint(A						,   A						,   A						,   additional_replacements=[replace_indices]			)

		if any('attn' in layer for layer in up_blocks[i]			):
			lowerCamelCase_							:		int							   =							len(
			    {'.'.join(shave_segments(A						,   3			).split('.'			)[:3]			) for layer in up_blocks[i] if 'attn' in layer}			)
			lowerCamelCase_							:		int							   =							{
			    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(A			)
			}

			if num_attn > 0:
				for j in range(config['layers_per_block'] + 1			):
					lowerCamelCase_							:		Dict							   =							{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
					lowerCamelCase_							:		List[str]							   =							renew_attention_paths(attns[j]			)
					assign_to_checkpoint(A						,   A						,   A						,   additional_replacements=[replace_indices]			)

	lowerCamelCase_							:		Optional[Any]							   =							{k.replace('mid_new_2'						,   'mid_block'			): v for k, v in new_checkpoint.items()}
	lowerCamelCase_							:		int							   =							checkpoint['quant_conv.weight']
	lowerCamelCase_							:		Any							   =							checkpoint['quant_conv.bias']
	if "quantize.embedding.weight" in checkpoint:
		lowerCamelCase_							:		int							   =							checkpoint['quantize.embedding.weight']
	lowerCamelCase_							:		Any							   =							checkpoint['post_quant_conv.weight']
	lowerCamelCase_							:		List[str]							   =							checkpoint['post_quant_conv.bias']

	return new_checkpoint


if __name__ == "__main__":
							UpperCamelCase__              =						argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)

							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The config json file corresponding to the architecture.',
							)

							parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

							UpperCamelCase__              =						parser.parse_args()
							UpperCamelCase__              =						torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														UpperCamelCase__              =						json.loads(f.read())

							# unet case
							UpperCamelCase__              =						{key.split('.')[0] for key in checkpoint.keys()}
							if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
														UpperCamelCase__              =						convert_vq_autoenc_checkpoint(checkpoint, config)
							else:
														UpperCamelCase__              =						convert_ddpm_checkpoint(checkpoint, config)

							if "ddpm" in config:
														del config["ddpm"]

							if config["_class_name"] == "VQModel":
														UpperCamelCase__              =						VQModel(**config)
														model.load_state_dict(converted_checkpoint)
														model.save_pretrained(args.dump_path)
							elif config["_class_name"] == "AutoencoderKL":
														UpperCamelCase__              =						AutoencoderKL(**config)
														model.load_state_dict(converted_checkpoint)
														model.save_pretrained(args.dump_path)
							else:
														UpperCamelCase__              =						UNetaDModel(**config)
														model.load_state_dict(converted_checkpoint)

														UpperCamelCase__              =						DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

														UpperCamelCase__              =						DDPMPipeline(unet=model, scheduler=scheduler)
														pipe.save_pretrained(args.dump_path)






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)
