



'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   torch.FloatTensor

class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase       ):







							@register_to_config
							def __init__(							self :	List[str]     ,_UpperCAmelCase :	int = 65536     ,_UpperCAmelCase :	Optional[int] = None     ,_UpperCAmelCase :	int = 2     ,_UpperCAmelCase :	int = 2     ,_UpperCAmelCase :	int = 0     ,_UpperCAmelCase :	str = "fourier"     ,_UpperCAmelCase :	bool = True     ,_UpperCAmelCase :	bool = False     ,_UpperCAmelCase :	float = 0.0     ,_UpperCAmelCase :	Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")     ,_UpperCAmelCase :	Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")     ,_UpperCAmelCase :	Tuple[str] = "UNetMidBlock1D"     ,_UpperCAmelCase :	str = None     ,_UpperCAmelCase :	Tuple[int] = (32, 32, 64)     ,_UpperCAmelCase :	str = None     ,_UpperCAmelCase :	int = 8     ,_UpperCAmelCase :	int = 1     ,_UpperCAmelCase :	bool = False     ,):
									super().__init__()
									_a					:				List[str]				=							sample_size

									# time
									if time_embedding_type == "fourier":
											_a					:				str				=							GaussianFourierProjection(
											    embedding_size=8     ,set_W_to_weight=_UpperCAmelCase     ,log=_UpperCAmelCase     ,flip_sin_to_cos=_UpperCAmelCase		)
											_a					:				Any				=							2 * block_out_channels[0]
									elif time_embedding_type == "positional":
											_a					:				List[Any]				=							Timesteps(
											    block_out_channels[0]     ,flip_sin_to_cos=_UpperCAmelCase     ,downscale_freq_shift=_UpperCAmelCase		)
											_a					:				str				=							block_out_channels[0]

									if use_timestep_embedding:
											_a					:				Optional[int]				=							block_out_channels[0] * 4
											_a					:				Optional[Any]				=							TimestepEmbedding(
											    in_channels=_UpperCAmelCase     ,time_embed_dim=_UpperCAmelCase     ,act_fn=_UpperCAmelCase     ,out_dim=block_out_channels[0]     ,)

									_a					:				Optional[Any]				=							nn.ModuleList([]		)
									_a					:				int				=							None
									_a					:				Any				=							nn.ModuleList([]		)
									_a					:				Union[str, Any]				=							None

									# down
									_a					:				List[str]				=							in_channels
									for i, down_block_type in enumerate(_UpperCAmelCase		):
											_a					:				Tuple				=							output_channel
											_a					:				List[str]				=							block_out_channels[i]

											if i == 0:
													input_channel += extra_in_channels

											_a					:				Optional[Any]				=							i == len(_UpperCAmelCase		) - 1

											_a					:				List[str]				=							get_down_block(
											    _UpperCAmelCase     ,num_layers=_UpperCAmelCase     ,in_channels=_UpperCAmelCase     ,out_channels=_UpperCAmelCase     ,temb_channels=block_out_channels[0]     ,add_downsample=not is_final_block or downsample_each_block     ,)
											self.down_blocks.append(_UpperCAmelCase		)

									# mid
									_a					:				Union[str, Any]				=							get_mid_block(
									    _UpperCAmelCase     ,in_channels=block_out_channels[-1]     ,mid_channels=block_out_channels[-1]     ,out_channels=block_out_channels[-1]     ,embed_dim=block_out_channels[0]     ,num_layers=_UpperCAmelCase     ,add_downsample=_UpperCAmelCase     ,)

									# up
									_a					:				Any				=							list(reversed(_UpperCAmelCase		)		)
									_a					:				List[str]				=							reversed_block_out_channels[0]
									if out_block_type is None:
											_a					:				Tuple				=							out_channels
									else:
											_a					:				Union[str, Any]				=							block_out_channels[0]

									for i, up_block_type in enumerate(_UpperCAmelCase		):
											_a					:				List[str]				=							output_channel
											_a					:				str				=							(
											    reversed_block_out_channels[i + 1] if i < len(_UpperCAmelCase		) - 1 else final_upsample_channels
											)

											_a					:				Tuple				=							i == len(_UpperCAmelCase		) - 1

											_a					:				Optional[Any]				=							get_up_block(
											    _UpperCAmelCase     ,num_layers=_UpperCAmelCase     ,in_channels=_UpperCAmelCase     ,out_channels=_UpperCAmelCase     ,temb_channels=block_out_channels[0]     ,add_upsample=not is_final_block     ,)
											self.up_blocks.append(_UpperCAmelCase		)
											_a					:				Tuple				=							output_channel

									# out
									_a					:				List[str]				=							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,32		)
									_a					:				Union[str, Any]				=							get_out_block(
									    out_block_type=_UpperCAmelCase     ,num_groups_out=_UpperCAmelCase     ,embed_dim=block_out_channels[0]     ,out_channels=_UpperCAmelCase     ,act_fn=_UpperCAmelCase     ,fc_dim=block_out_channels[-1] // 4     ,)







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	torch.FloatTensor     ,_UpperCAmelCase :	Union[torch.Tensor, float, int]     ,_UpperCAmelCase :	bool = True     ,):
									_a					:				Dict				=							timestep
									if not torch.is_tensor(_UpperCAmelCase		):
											_a					:				int				=							torch.tensor([timesteps]     ,dtype=torch.long     ,device=sample.device		)
									elif torch.is_tensor(_UpperCAmelCase		) and len(timesteps.shape		) == 0:
											_a					:				Union[str, Any]				=							timesteps[None].to(sample.device		)

									_a					:				Dict				=							self.time_proj(_UpperCAmelCase		)
									if self.config.use_timestep_embedding:
											_a					:				List[Any]				=							self.time_mlp(_UpperCAmelCase		)
									else:
											_a					:				Optional[int]				=							timestep_embed[..., None]
											_a					:				Optional[Any]				=							timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
											_a					:				int				=							timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

									# 2. down
									_a					:				str				=							()
									for downsample_block in self.down_blocks:
											_a					:				str				=							downsample_block(hidden_states=_UpperCAmelCase     ,temb=_UpperCAmelCase		)
											down_block_res_samples += res_samples

									# 3. mid
									if self.mid_block:
											_a					:				Optional[Any]				=							self.mid_block(_UpperCAmelCase     ,_UpperCAmelCase		)

									# 4. up
									for i, upsample_block in enumerate(self.up_blocks		):
											_a					:				Tuple				=							down_block_res_samples[-1:]
											_a					:				Optional[int]				=							down_block_res_samples[:-1]
											_a					:				Any				=							upsample_block(_UpperCAmelCase     ,res_hidden_states_tuple=_UpperCAmelCase     ,temb=_UpperCAmelCase		)

									# 5. post-process
									if self.out_block:
											_a					:				Optional[Any]				=							self.out_block(_UpperCAmelCase     ,_UpperCAmelCase		)

									if not return_dict:
											return (sample,)

									return UNetaDOutput(sample=_UpperCAmelCase		)









'''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)




