
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()
UpperCamelCase_						:     Optional[int]	       =							logging.get_logger(__name__)



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Tuple=False ,			_UpperCAmelCase  :   Optional[int]=False  )   ->			Dict:






						'''simple docstring'''

						_lowercase       :	Dict           =   "backbone." if is_semantic else ""

						_lowercase       :	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 		UpperCamelCase     (       _UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   Dict ,			_UpperCAmelCase  :   Union[str, Any]=False ,			_UpperCAmelCase  :   Dict=False  )   ->			List[str]:






						'''simple docstring'''

						for i in range(config.num_hidden_layers  ):
												_lowercase       :	List[str]           =   "backbone." if is_semantic else ""
												# queries, keys and values
												_lowercase       :	str           =   state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""  )
												_lowercase       :	str           =   state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""  )
												_lowercase       :	Optional[int]           =   state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""  )

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

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												_lowercase       :	int           =   state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""  )
												_lowercase       :	Union[str, Any]           =   state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""  )

												_lowercase       :	Union[str, Any]           =   gamma_a
												_lowercase       :	Union[str, Any]           =   gamma_a



def 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   List[str] ,			_UpperCAmelCase  :   Dict  )   ->			Tuple:






						'''simple docstring'''

						_lowercase       :	Tuple           =   dct.pop(_UpperCAmelCase  )
						_lowercase       :	str           =   val



def 		UpperCamelCase     (       )   ->			Optional[int]:






						'''simple docstring'''

						_lowercase       :	Tuple           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
						_lowercase       :	int           =   Image.open(requests.get(_UpperCAmelCase ,			stream=_UpperCAmelCase  ).raw  )
						return im



@torch.no_grad()
def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   int ,			_UpperCAmelCase  :   Optional[int]=False  )   ->			Optional[int]:






						'''simple docstring'''

						_lowercase       :	Optional[Any]           =   False if "rvlcdip" in checkpoint_url else True
						_lowercase       :	int           =   BeitConfig(use_absolute_position_embeddings=_UpperCAmelCase ,			use_mask_token=_UpperCAmelCase  )

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

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

						# load state_dict of original model, remove and rename some keys
						_lowercase       :	List[str]           =   torch.hub.load_state_dict_from_url(_UpperCAmelCase ,			map_location="cpu"  )["model"]

						_lowercase       :	Any           =   create_rename_keys(_UpperCAmelCase ,			has_lm_head=_UpperCAmelCase  )
						for src, dest in rename_keys:
												rename_key(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )
						read_in_q_k_v(_UpperCAmelCase ,			_UpperCAmelCase ,			has_lm_head=_UpperCAmelCase  )

						# load HuggingFace model
						_lowercase       :	str           =   BeitForMaskedImageModeling(_UpperCAmelCase  ) if has_lm_head else BeitForImageClassification(_UpperCAmelCase  )
						model.eval()
						model.load_state_dict(_UpperCAmelCase  )

						# Check outputs on an image
						_lowercase       :	Optional[int]           =   BeitImageProcessor(
						    size=config.image_size ,			resample=PILImageResampling.BILINEAR ,			do_center_crop=_UpperCAmelCase  )
						_lowercase       :	Any           =   prepare_img()

						_lowercase       :	Dict           =   image_processor(images=_UpperCAmelCase ,			return_tensors="pt"  )
						_lowercase       :	int           =   encoding["pixel_values"]

						_lowercase       :	List[Any]           =   model(_UpperCAmelCase  )
						_lowercase       :	int           =   outputs.logits

						# verify logits
						_lowercase       :	Union[str, Any]           =   [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
						assert logits.shape == torch.Size(_UpperCAmelCase  ), "Shape of logits not as expected"

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

						if push_to_hub:
												if has_lm_head:
																		_lowercase       :	Tuple           =   "dit-base" if "base" in checkpoint_url else "dit-large"
												else:
																		_lowercase       :	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(_UpperCAmelCase ,			_UpperCAmelCase  ) ,			organization="nielsr" ,			commit_message="Add image processor" ,			use_temp_dir=_UpperCAmelCase ,			)
												model.push_to_hub(
												    repo_path_or_name=Path(_UpperCAmelCase ,			_UpperCAmelCase  ) ,			organization="nielsr" ,			commit_message="Add model" ,			use_temp_dir=_UpperCAmelCase ,			)


if __name__ == "__main__":
							UpperCamelCase_						:     str	       =							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""",
							)
							UpperCamelCase_						:     int	       =							parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:     Optional[int]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   "timm_backbone"






	def __init__(self				: Any      ,	snake_case				: List[Any]=None      ,	snake_case				: int=3      ,	snake_case				: Dict=True      ,	snake_case				: Union[str, Any]=True      ,	snake_case				: Any=None      ,	**snake_case				: int      ,	) ->			Optional[int]:
							super().__init__(**snake_case    )
							_lowercase       :	Dict           =   backbone
							_lowercase       :	Optional[Any]           =   num_channels
							_lowercase       :	Union[str, Any]           =   features_only
							_lowercase       :	Tuple           =   use_pretrained_backbone
							_lowercase       :	List[str]           =   True
							_lowercase       :	Tuple           =   out_indices if out_indices is not None else (-1,)




