



'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      __magic_name__						(   _UpperCamelCase       ):







							def __init__(							self :	Union[str, Any]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Dict = None     ,):
									super().__init__()
									self.register_modules(transformer=__a     ,vae=__a     ,scheduler=__a		)

									# create a imagenet -> id dictionary for easier use
									_a					:				Union[str, Any]				=							{}
									if idalabel is not None:
											for key, value in idalabel.items():
													for label in value.split(','		):
															_a					:				Union[str, Any]				=							int(__a		)
											_a					:				str				=							dict(sorted(self.labels.items()		)		)







							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	List[str]		):

									if not isinstance(__a     ,__a		):
											_a					:				int				=							list(__a		)

									for l in label:
											if l not in self.labels:
													raise ValueError(
													    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

									return [self.labels[l] for l in label]







							@torch.no_grad()
							def __call__(							self :	Tuple     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any] = 4.0     ,_UpperCAmelCase :	Tuple = None     ,_UpperCAmelCase :	Optional[int] = 50     ,_UpperCAmelCase :	Union[str, Any] = "pil"     ,_UpperCAmelCase :	List[str] = True     ,):
									_a					:				Optional[Any]				=							len(__a		)
									_a					:				Union[str, Any]				=							self.transformer.config.sample_size
									_a					:				Tuple				=							self.transformer.config.in_channels

									_a					:				str				=							randn_tensor(
									    shape=(batch_size, latent_channels, latent_size, latent_size)     ,generator=__a     ,device=self.device     ,dtype=self.transformer.dtype     ,)
									_a					:				str				=							torch.cat([latents] * 2		) if guidance_scale > 1 else latents

									_a					:				List[Any]				=							torch.tensor(__a     ,device=self.device		).reshape(-1		)
									_a					:				Tuple				=							torch.tensor([1000] * batch_size     ,device=self.device		)
									_a					:				int				=							torch.cat([class_labels, class_null]     ,0		) if guidance_scale > 1 else class_labels

									# set step values
									self.scheduler.set_timesteps(__a		)

									for t in self.progress_bar(self.scheduler.timesteps		):
											if guidance_scale > 1:
													_a					:				List[Any]				=							latent_model_input[: len(__a		) // 2]
													_a					:				Union[str, Any]				=							torch.cat([half, half]     ,dim=0		)
											_a					:				str				=							self.scheduler.scale_model_input(__a     ,__a		)

											_a					:				str				=							t
											if not torch.is_tensor(__a		):
													# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
													# This would be a good case for the `match` statement (Python 3.10+)
													_a					:				List[Any]				=							latent_model_input.device.type == 'mps'
													if isinstance(__a     ,__a		):
															_a					:				List[str]				=							torch.floataa if is_mps else torch.floataa
													else:
															_a					:				Dict				=							torch.intaa if is_mps else torch.intaa
													_a					:				Optional[Any]				=							torch.tensor([timesteps]     ,dtype=__a     ,device=latent_model_input.device		)
											elif len(timesteps.shape		) == 0:
													_a					:				Optional[int]				=							timesteps[None].to(latent_model_input.device		)
											# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
											_a					:				int				=							timesteps.expand(latent_model_input.shape[0]		)
											# predict noise model_output
											_a					:				Dict				=							self.transformer(
											    __a     ,timestep=__a     ,class_labels=__a		).sample

											# perform guidance
											if guidance_scale > 1:
													_a	, _a					:				Any				=							noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
													_a	, _a					:				Optional[Any]				=							torch.split(__a     ,len(__a		) // 2     ,dim=0		)

													_a					:				Optional[Any]				=							uncond_eps + guidance_scale * (cond_eps - uncond_eps)
													_a					:				Optional[Any]				=							torch.cat([half_eps, half_eps]     ,dim=0		)

													_a					:				str				=							torch.cat([eps, rest]     ,dim=1		)

											# learned sigma
											if self.transformer.config.out_channels // 2 == latent_channels:
													_a	, _a					:				Optional[Any]				=							torch.split(__a     ,__a     ,dim=1		)
											else:
													_a					:				Optional[Any]				=							noise_pred

											# compute previous image: x_t -> x_t-1
											_a					:				Dict				=							self.scheduler.step(__a     ,__a     ,__a		).prev_sample

									if guidance_scale > 1:
											_a	, _a					:				List[Any]				=							latent_model_input.chunk(2     ,dim=0		)
									else:
											_a					:				Any				=							latent_model_input

									_a					:				List[str]				=							1 / self.vae.config.scaling_factor * latents
									_a					:				int				=							self.vae.decode(__a		).sample

									_a					:				Optional[int]				=							(samples / 2 + 0.5).clamp(0     ,1		)

									# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
									_a					:				Dict				=							samples.cpu().permute(0     ,2     ,3     ,1		).float().numpy()

									if output_type == "pil":
											_a					:				int				=							self.numpy_to_pil(__a		)

									if not return_dict:
											return (samples,)

									return ImagePipelineOutput(images=__a		)









'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase						      =       logging.get_logger(__name__)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_=False  )      ->				Any:
		_a					:				Dict				=							'backbone.' if is_semantic else ''

		_a					:				Optional[int]				=							[]
		for i in range(config.num_hidden_layers  ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")  )
				rename_keys.append(
				    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")  )
				rename_keys.append(
				    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")  )
				rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")  )

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        (f"""{prefix}cls_token""", 'beit.embeddings.cls_token'),
		        (f"""{prefix}patch_embed.proj.weight""", 'beit.embeddings.patch_embeddings.projection.weight'),
		        (f"""{prefix}patch_embed.proj.bias""", 'beit.embeddings.patch_embeddings.projection.bias'),
		        (f"""{prefix}pos_embed""", 'beit.embeddings.position_embeddings'),
		    ]  )

		if has_lm_head:
				# mask token + layernorm
				rename_keys.extend(
				    [
				        ('mask_token', 'beit.embeddings.mask_token'),
				        ('norm.weight', 'layernorm.weight'),
				        ('norm.bias', 'layernorm.bias'),
				    ]  )
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
				        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
				        ('head.weight', 'classifier.weight'),
				        ('head.bias', 'classifier.bias'),
				    ]  )

		return rename_keys



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_=False  )      ->				int:
		for i in range(config.num_hidden_layers  ):
				_a					:				Union[str, Any]				=							'backbone.' if is_semantic else ''
				# queries, keys and values
				_a					:				Any				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""  )
				_a					:				str				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""  )
				_a					:				str				=							state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""  )

				_a					:				int				=							in_proj_weight[
				    : config.hidden_size, :
				]
				_a					:				str				=							q_bias
				_a					:				List[Any]				=							in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_a					:				List[str]				=							in_proj_weight[
				    -config.hidden_size :, :
				]
				_a					:				Any				=							v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				_a					:				int				=							state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""  )
				_a					:				Tuple				=							state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""  )

				_a					:				Tuple				=							gamma_a
				_a					:				Optional[Any]				=							gamma_a



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Dict:
		_a					:				List[Any]				=							dct.pop(lowerCAmelCase_  )
		_a					:				List[Any]				=							val



def 					__lowerCamelCase					(				)      ->				Dict:
		_a					:				Optional[Any]				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a					:				Dict				=							Image.open(requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_  ).raw  )
		return im



@torch.no_grad()
def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Union[str, Any]:
		_a					:				Optional[Any]				=							False if 'rvlcdip' in checkpoint_url else True
		_a					:				List[Any]				=							BeitConfig(use_absolute_position_embeddings=lowerCAmelCase_						,      use_mask_token=lowerCAmelCase_  )

		# size of the architecture
		if "large" in checkpoint_url or "dit-l" in checkpoint_url:
				_a					:				Optional[Any]				=							1024
				_a					:				Tuple				=							4096
				_a					:				Any				=							24
				_a					:				Optional[int]				=							16

		# labels
		if "rvlcdip" in checkpoint_url:
				_a					:				Any				=							16
				_a					:				Tuple				=							'huggingface/label-files'
				_a					:				List[Any]				=							'rvlcdip-id2label.json'
				_a					:				Optional[int]				=							json.load(open(hf_hub_download(lowerCAmelCase_						,      lowerCAmelCase_						,      repo_type='dataset'  )						,      'r'  )  )
				_a					:				int				=							{int(lowerCAmelCase_  ): v for k, v in idalabel.items()}
				_a					:				int				=							idalabel
				_a					:				int				=							{v: k for k, v in idalabel.items()}

		# load state_dict of original model, remove and rename some keys
		_a					:				List[Any]				=							torch.hub.load_state_dict_from_url(lowerCAmelCase_						,      map_location='cpu'  )['model']

		_a					:				Dict				=							create_rename_keys(lowerCAmelCase_						,      has_lm_head=lowerCAmelCase_  )
		for src, dest in rename_keys:
				rename_key(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		read_in_q_k_v(lowerCAmelCase_						,      lowerCAmelCase_						,      has_lm_head=lowerCAmelCase_  )

		# load HuggingFace model
		_a					:				Dict				=							BeitForMaskedImageModeling(lowerCAmelCase_  ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_  )
		model.eval()
		model.load_state_dict(lowerCAmelCase_  )

		# Check outputs on an image
		_a					:				Dict				=							BeitImageProcessor(
		    size=config.image_size						,      resample=PILImageResampling.BILINEAR						,      do_center_crop=lowerCAmelCase_  )
		_a					:				Dict				=							prepare_img()

		_a					:				str				=							image_processor(images=lowerCAmelCase_						,      return_tensors='pt'  )
		_a					:				Optional[Any]				=							encoding['pixel_values']

		_a					:				Optional[Any]				=							model(lowerCAmelCase_  )
		_a					:				Optional[Any]				=							outputs.logits

		# verify logits
		_a					:				int				=							[1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8192]
		assert logits.shape == torch.Size(lowerCAmelCase_  ), "Shape of logits not as expected"

		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		print(f"""Saving model to {pytorch_dump_folder_path}"""  )
		model.save_pretrained(lowerCAmelCase_  )
		print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
		image_processor.save_pretrained(lowerCAmelCase_  )

		if push_to_hub:
				if has_lm_head:
						_a					:				Tuple				=							'dit-base' if 'base' in checkpoint_url else 'dit-large'
				else:
						_a					:				int				=							'dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
				image_processor.push_to_hub(
				    repo_path_or_name=Path(lowerCAmelCase_						,      lowerCAmelCase_  )						,      organization='nielsr'						,      commit_message='Add image processor'						,      use_temp_dir=lowerCAmelCase_						,      )
				model.push_to_hub(
				    repo_path_or_name=Path(lowerCAmelCase_						,      lowerCAmelCase_  )						,      organization='nielsr'						,      commit_message='Add model'						,      use_temp_dir=lowerCAmelCase_						,      )


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

							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
							    type=str,
							    help='''URL to the original PyTorch checkpoint (.pth file).''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
							)
							parser.add_argument(
							    '''--push_to_hub''',
							    action='''store_true''',
							)
							__lowerCAmelCase						      =       parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




