






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()
SCREAMING_SNAKE_CASE__   :      Any				=		logging.get_logger(__name__)

def   __lowercase       (			snake_case,	snake_case=False,	snake_case=False			):







				"""simple docstring"""






				__magic_name__		:Any   =   '''backbone.''' if is_semantic else ''''''

				__magic_name__		:Tuple   =   []
				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   __lowercase       (			snake_case,	snake_case,	snake_case=False,	snake_case=False			):







				"""simple docstring"""






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

								__magic_name__		:Optional[Any]   =   in_proj_weight[
								    : config.hidden_size, :
								]
								__magic_name__		:str   =   q_bias
								__magic_name__		:int   =   in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__magic_name__		:Optional[Any]   =   in_proj_weight[
								    -config.hidden_size :, :
								]
								__magic_name__		:Tuple   =   v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								__magic_name__		:Optional[Any]   =   state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''			)
								__magic_name__		:Optional[Any]   =   state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''			)

								__magic_name__		:List[Any]   =   gamma_a
								__magic_name__		:Optional[int]   =   gamma_a

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   dct.pop(snake_case			)
				__magic_name__		:Tuple   =   val

def   __lowercase       (			):







				"""simple docstring"""






				__magic_name__		:str   =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__magic_name__		:List[Any]   =   Image.open(requests.get(snake_case,	stream=snake_case			).raw			)
				return im

@torch.no_grad()
def   __lowercase       (			snake_case,	snake_case,	snake_case=False			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   False if '''rvlcdip''' in checkpoint_url else True
				__magic_name__		:int   =   BeitConfig(use_absolute_position_embeddings=snake_case,	use_mask_token=snake_case			)

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								__magic_name__		:Tuple   =   1_0_2_4
								__magic_name__		:int   =   4_0_9_6
								__magic_name__		:List[Any]   =   2_4
								__magic_name__		:Union[str, Any]   =   1_6

				# labels
				if "rvlcdip" in checkpoint_url:
								__magic_name__		:List[str]   =   1_6
								__magic_name__		:Dict   =   '''huggingface/label-files'''
								__magic_name__		:int   =   '''rvlcdip-id2label.json'''
								__magic_name__		:Optional[int]   =   json.load(open(hf_hub_download(snake_case,	snake_case,	repo_type='''dataset'''			),	'''r'''			)			)
								__magic_name__		:List[str]   =   {int(snake_case			): v for k, v in idalabel.items()}
								__magic_name__		:List[str]   =   idalabel
								__magic_name__		:Tuple   =   {v: k for k, v in idalabel.items()}

				# load state_dict of original model, remove and rename some keys
				__magic_name__		:Union[str, Any]   =   torch.hub.load_state_dict_from_url(snake_case,	map_location='''cpu'''			)['''model''']

				__magic_name__		:Dict   =   create_rename_keys(snake_case,	has_lm_head=snake_case			)
				for src, dest in rename_keys:
								rename_key(snake_case,	snake_case,	snake_case			)
				read_in_q_k_v(snake_case,	snake_case,	has_lm_head=snake_case			)

				# load HuggingFace model
				__magic_name__		:Union[str, Any]   =   BeitForMaskedImageModeling(snake_case			) if has_lm_head else BeitForImageClassification(snake_case			)
				model.eval()
				model.load_state_dict(snake_case			)

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

				__magic_name__		:Dict   =   image_processor(images=snake_case,	return_tensors='''pt'''			)
				__magic_name__		:str   =   encoding['''pixel_values''']

				__magic_name__		:List[str]   =   model(snake_case			)
				__magic_name__		:int   =   outputs.logits

				# verify logits
				__magic_name__		:Optional[int]   =   [1, 1_6] if '''rvlcdip''' in checkpoint_url else [1, 1_9_6, 8_1_9_2]
				assert logits.shape == torch.Size(snake_case			), "Shape of logits not as expected"

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

				if push_to_hub:
								if has_lm_head:
												__magic_name__		:Union[str, Any]   =   '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
								else:
												__magic_name__		:str   =   '''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(snake_case,	snake_case			),	organization='''nielsr''',	commit_message='''Add image processor''',	use_temp_dir=snake_case,	)
								model.push_to_hub(
								    repo_path_or_name=Path(snake_case,	snake_case			),	organization='''nielsr''',	commit_message='''Add model''',	use_temp_dir=snake_case,	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Dict				=		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""",
					)
					SCREAMING_SNAKE_CASE__   :      Optional[int]				=		parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def       _lowercase							(					):
 __A	:		List[Any]	  =      ArgumentParser(
     description=(
         'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
     )      )

 # Optional arguments for the launch helper
 parser.add_argument('--num_cores',					type=UpperCamelCase__,					default=1,					help='Number of TPU cores to use (1 or 8).'      )

 # positional
 parser.add_argument(
     'training_script',					type=UpperCamelCase__,					help=(
         'The full path to the single TPU training '
         'program/script to be launched in parallel, '
         'followed by all the arguments for the '
         'training script'
     ),					)

 # rest from the training program
 parser.add_argument('training_script_args',					nargs=UpperCamelCase__      )

 return parser.parse_args()


def       _lowercase							(					):
 __A	:		str	  =      parse_args()

 # Import training_script as a module.
 __A	:		Tuple	  =      Path(args.training_script      )
 sys.path.append(str(script_fpath.parent.resolve()      )      )
 __A	:		str	  =      script_fpath.stem
 __A	:		Dict	  =      importlib.import_module(UpperCamelCase__      )

 # Patch sys.argv
 __A	:		Optional[Any]	  =      [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores      )]

 xmp.spawn(mod._mp_fn,					args=(),					nprocs=args.num_cores      )


if __name__ == "__main__":
 main()






