


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_UpperCAmelCase     :					int       =logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase     :					Dict       ="""\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"""



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_=8	)->	Optional[Any]:
				lowerCAmelCase_	: Any      			=			height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				lowerCAmelCase_	: Tuple      			=			width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_=512	,				lowerCAmelCase_=512	)->	Optional[Any]:
				lowerCAmelCase_	: Union[str, Any]      			=			pil_image.resize((w, h)	,				resample=Image.BICUBIC	,				reducing_gap=1	)
				lowerCAmelCase_	: List[str]      			=			np.array(pil_image.convert('''RGB'''	)	)
				lowerCAmelCase_	: int      			=			arr.astype(np.floataa	) / 127.5 - 1
				lowerCAmelCase_	: Any      			=			np.transpose(SCREAMING_SNAKE_CASE_	,				[2, 0, 1]	)
				lowerCAmelCase_	: List[Any]      			=			torch.from_numpy(SCREAMING_SNAKE_CASE_	).unsqueeze(0	)
				return image







class 			snake_case__(							_UpperCAmelCase							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  )   ->      int:
										super().__init__()

										self.register_modules(
										    unet=lowercase__				,  scheduler=lowercase__				,  movq=lowercase__				,  )
										lowerCAmelCase_	: Dict      			=			2 ** (len(self.movq.config.block_out_channels							) - 1)




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      int:
										lowerCAmelCase_	: Union[str, Any]      			=			min(int(num_inference_steps * strength							)				,  lowercase__							)

										lowerCAmelCase_	: Dict      			=			max(num_inference_steps - init_timestep				,  0							)
										lowerCAmelCase_	: List[Any]      			=			self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase=None							)   ->      int:






										if not isinstance(lowercase__				,  (torch.Tensor, PIL.Image.Image, list)							):
														raise ValueError(
														    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase__							)}"""							)

										lowerCAmelCase_	: str      			=			image.to(device=lowercase__				,  dtype=lowercase__							)

										lowerCAmelCase_	: List[Any]      			=			batch_size * num_images_per_prompt

										if image.shape[1] == 4:
														lowerCAmelCase_	: Dict      			=			image

										else:
														if isinstance(lowercase__				,  lowercase__							) and len(lowercase__							) != batch_size:
																		raise ValueError(
																		    f"""You have passed a list of generators of length {len(lowercase__							)}, but requested an effective batch"""
																		    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

														elif isinstance(lowercase__				,  lowercase__							):
																		lowerCAmelCase_	: Union[str, Any]      			=			[
																		    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(lowercase__							)
																		]
																		lowerCAmelCase_	: Optional[Any]      			=			torch.cat(lowercase__				,  dim=0							)
														else:
																		lowerCAmelCase_	: List[str]      			=			self.movq.encode(lowercase__							).latent_dist.sample(lowercase__							)

														lowerCAmelCase_	: Any      			=			self.movq.config.scaling_factor * init_latents

										lowerCAmelCase_	: Tuple      			=			torch.cat([init_latents]				,  dim=0							)

										lowerCAmelCase_	: Union[str, Any]      			=			init_latents.shape
										lowerCAmelCase_	: Optional[Any]      			=			randn_tensor(lowercase__				,  generator=lowercase__				,  device=lowercase__				,  dtype=lowercase__							)

										# get latents
										lowerCAmelCase_	: str      			=			self.scheduler.add_noise(lowercase__				,  lowercase__				,  lowercase__							)

										lowerCAmelCase_	: Optional[int]      			=			init_latents

										return latents




						def   lowercase_	(			self				,  __lowercase=0							)   ->      List[str]:






										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError('''Please install accelerate via `pip install accelerate`'''							)

										lowerCAmelCase_	: List[Any]      			=			torch.device(f"""cuda:{gpu_id}"""							)

										lowerCAmelCase_	: Optional[Any]      			=			[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(lowercase__				,  lowercase__							)




						def   lowercase_	(			self				,  __lowercase=0							)   ->      str:






										if is_accelerate_available() and is_accelerate_version('''>='''				,  '''0.17.0.dev0'''							):
														from accelerate import cpu_offload_with_hook
										else:
														raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''							)

										lowerCAmelCase_	: Dict      			=			torch.device(f"""cuda:{gpu_id}"""							)

										if self.device.type != "cpu":
														self.to('''cpu'''				,  silence_dtype_warnings=lowercase__							)
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										lowerCAmelCase_	: int      			=			None
										for cpu_offloaded_model in [self.unet, self.movq]:
														lowerCAmelCase_	: str      			=			cpu_offload_with_hook(lowercase__				,  lowercase__				,  prev_module_hook=lowercase__							)

										# We'll offload the last model manually.
										lowerCAmelCase_	: Any      			=			hook




						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def   lowercase_	(			self							)   ->      Dict:






										if not hasattr(self.unet				,  '''_hf_hook'''							):
														return self.device
										for module in self.unet.modules():
														if (
														    hasattr(lowercase__				,  '''_hf_hook'''							)
														    and hasattr(module._hf_hook				,  '''execution_device'''							)
														    and module._hf_hook.execution_device is not None
														):
																		return torch.device(module._hf_hook.execution_device							)
										return self.device




						@torch.no_grad()
						@replace_example_docstring(lowercase__							)
						def __call__(			self				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase = 5_1_2				,  __lowercase = 5_1_2				,  __lowercase = 1_0_0				,  __lowercase = 4.0				,  __lowercase = 0.3				,  __lowercase = 1				,  __lowercase = None				,  __lowercase = "pil"				,  __lowercase = True				,  )   ->      Union[str, Any]:
										lowerCAmelCase_	: int      			=			self._execution_device

										lowerCAmelCase_	: str      			=			guidance_scale > 1.0

										if isinstance(lowercase__				,  lowercase__							):
														lowerCAmelCase_	: int      			=			torch.cat(lowercase__				,  dim=0							)
										if isinstance(lowercase__				,  lowercase__							):
														lowerCAmelCase_	: Union[str, Any]      			=			torch.cat(lowercase__				,  dim=0							)
										if isinstance(lowercase__				,  lowercase__							):
														lowerCAmelCase_	: Optional[int]      			=			torch.cat(lowercase__				,  dim=0							)

										lowerCAmelCase_	: List[Any]      			=			image_embeds.shape[0]

										if do_classifier_free_guidance:
														lowerCAmelCase_	: Optional[int]      			=			image_embeds.repeat_interleave(lowercase__				,  dim=0							)
														lowerCAmelCase_	: Optional[int]      			=			negative_image_embeds.repeat_interleave(lowercase__				,  dim=0							)
														lowerCAmelCase_	: Optional[int]      			=			hint.repeat_interleave(lowercase__				,  dim=0							)

										lowerCAmelCase_	: Union[str, Any]      			=			torch.cat([negative_image_embeds, image_embeds]				,  dim=0							).to(dtype=self.unet.dtype				,  device=lowercase__							)
										lowerCAmelCase_	: List[Any]      			=			torch.cat([hint, hint]				,  dim=0							).to(dtype=self.unet.dtype				,  device=lowercase__							)

										if not isinstance(lowercase__				,  lowercase__							):
														lowerCAmelCase_	: List[Any]      			=			[image]
										if not all(isinstance(lowercase__				,  (PIL.Image.Image, torch.Tensor)							) for i in image							):
														raise ValueError(
														    f"""Input is in incorrect format: {[type(lowercase__							) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""							)

										lowerCAmelCase_	: Optional[Any]      			=			torch.cat([prepare_image(lowercase__				,  lowercase__				,  lowercase__							) for i in image]				,  dim=0							)
										lowerCAmelCase_	: Dict      			=			image.to(dtype=image_embeds.dtype				,  device=lowercase__							)

										lowerCAmelCase_	: List[Any]      			=			self.movq.encode(lowercase__							)["latents"]
										lowerCAmelCase_	: Any      			=			latents.repeat_interleave(lowercase__				,  dim=0							)
										self.scheduler.set_timesteps(lowercase__				,  device=lowercase__							)
										lowerCAmelCase_	: str      			=			self.get_timesteps(lowercase__				,  lowercase__				,  lowercase__							)
										lowerCAmelCase_	: str      			=			timesteps[:1].repeat(batch_size * num_images_per_prompt							)
										lowerCAmelCase_	: Optional[Any]      			=			downscale_height_and_width(lowercase__				,  lowercase__				,  self.movq_scale_factor							)
										lowerCAmelCase_	: str      			=			self.prepare_latents(
										    lowercase__				,  lowercase__				,  lowercase__				,  lowercase__				,  image_embeds.dtype				,  lowercase__				,  lowercase__							)
										for i, t in enumerate(self.progress_bar(lowercase__							)							):
														# expand the latents if we are doing classifier free guidance
														lowerCAmelCase_	: Tuple      			=			torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

														lowerCAmelCase_	: List[Any]      			=			{"image_embeds": image_embeds, "hint": hint}
														lowerCAmelCase_	: Dict      			=			self.unet(
														    sample=lowercase__				,  timestep=lowercase__				,  encoder_hidden_states=lowercase__				,  added_cond_kwargs=lowercase__				,  return_dict=lowercase__				,  )[0]

														if do_classifier_free_guidance:
																		lowerCAmelCase_	: str      			=			noise_pred.split(latents.shape[1]				,  dim=1							)
																		lowerCAmelCase_	: Any      			=			noise_pred.chunk(2							)
																		lowerCAmelCase_	: List[Any]      			=			variance_pred.chunk(2							)
																		lowerCAmelCase_	: Tuple      			=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																		lowerCAmelCase_	: Dict      			=			torch.cat([noise_pred, variance_pred_text]				,  dim=1							)

														if not (
														    hasattr(self.scheduler.config				,  '''variance_type'''							)
														    and self.scheduler.config.variance_type in ["learned", "learned_range"]
														):
																		lowerCAmelCase_	: Dict      			=			noise_pred.split(latents.shape[1]				,  dim=1							)

														# compute the previous noisy sample x_t -> x_t-1

														lowerCAmelCase_	: str      			=			self.scheduler.step(
														    lowercase__				,  lowercase__				,  lowercase__				,  generator=lowercase__				,  )[0]

										# post-processing
										lowerCAmelCase_	: int      			=			self.movq.decode(lowercase__				,  force_not_quantize=lowercase__							)["sample"]

										if output_type not in ["pt", "np", "pil"]:
														raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""							)

										if output_type in ["np", "pil"]:
														lowerCAmelCase_	: str      			=			image * 0.5 + 0.5
														lowerCAmelCase_	: Union[str, Any]      			=			image.clamp(0				,  1							)
														lowerCAmelCase_	: Dict      			=			image.cpu().permute(0				,  2				,  3				,  1							).float().numpy()

										if output_type == "pil":
														lowerCAmelCase_	: Optional[Any]      			=			self.numpy_to_pil(lowercase__							)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=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     :					List[Any]       ="""src/diffusers"""
_UpperCAmelCase     :					Dict       ="""docs/source/en"""
_UpperCAmelCase     :					List[str]       ="""."""



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

				lowerCAmelCase_	: List[Any]      			=			start_index
				while not lines[end_index].startswith(lowerCAmelCase_	):
								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     :					int       ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase     :					Any       =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase     :					Optional[int]       =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     :					Union[str, Any]       =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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



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



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



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

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

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

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

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

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



def      lowerCAmelCase	(						lowerCAmelCase_=False	)->	str:
				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			_find_text_in_file(
				    filename=os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				start_prompt='''<!--This table is updated automatically from the auto modules'''	,				end_prompt='''<!-- End table-->'''	,				)
				lowerCAmelCase_	: int      			=			get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase_	,				'''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     :					Optional[Any]       =argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_UpperCAmelCase     :					int       =parser.parse_args()

						check_model_table(args.fix_and_overwrite)