




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 		__A		(	__lowerCamelCase							,			__lowerCamelCase=False							,			__lowerCamelCase=False    )      ->       List[Any]:
	a										=							"""backbone.""" if is_semantic else """"""

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

		a										=							in_proj_weight[
		    : config.hidden_size, :
		]
		a										=							q_bias
		a										=							in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		a										=							in_proj_weight[
		    -config.hidden_size :, :
		]
		a										=							v_bias

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

		a										=							gamma_a
		a										=							gamma_a





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[int]:
	a										=							dct.pop(__lowerCamelCase    )
	a										=							val





def 		__A		(	)      ->       List[str]:
	a										=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	a										=							Image.open(requests.get(__lowerCamelCase							,			stream=__lowerCamelCase    ).raw    )
	return im





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=False    )      ->       int:
	a										=							False if """rvlcdip""" in checkpoint_url else True
	a										=							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										=							1024
		a										=							4096
		a										=							24
		a										=							16

	# labels
	if "rvlcdip" in checkpoint_url:
		a										=							16
		a										=							"""huggingface/label-files"""
		a										=							"""rvlcdip-id2label.json"""
		a										=							json.load(open(hf_hub_download(__lowerCamelCase							,			__lowerCamelCase							,			repo_type="""dataset"""    )							,			"""r"""    )    )
		a										=							{int(__lowerCamelCase    ): v for k, v in idalabel.items()}
		a										=							idalabel
		a										=							{v: k for k, v in idalabel.items()}

	# load state_dict of original model, remove and rename some keys
	a										=							torch.hub.load_state_dict_from_url(__lowerCamelCase							,			map_location="""cpu"""    )["""model"""]

	a										=							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										=							BeitForMaskedImageModeling(__lowerCamelCase    ) if has_lm_head else BeitForImageClassification(__lowerCamelCase    )
	model.eval()
	model.load_state_dict(__lowerCamelCase    )

	# Check outputs on an image
	a										=							BeitImageProcessor(
	    size=config.image_size							,			resample=PILImageResampling.BILINEAR							,			do_center_crop=__lowerCamelCase    )
	a										=							prepare_img()

	a										=							image_processor(images=__lowerCamelCase							,			return_tensors="""pt"""    )
	a										=							encoding["""pixel_values"""]

	a										=							model(__lowerCamelCase    )
	a										=							outputs.logits

	# verify logits
	a										=							[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										=							"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
		else:
			a										=							"""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__":
		__UpperCamelCase					:       int    					=      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					:       Dict    					=      parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def 		__A		(	)      ->       Any:
	a										=							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=__lowerCamelCase							,			default=1							,			help="""Number of TPU cores to use (1 or 8)."""    )

	# positional
	parser.add_argument(
	    """training_script"""							,			type=__lowerCamelCase							,			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=__lowerCamelCase    )

	return parser.parse_args()





def 		__A		(	)      ->       Union[str, Any]:
	a										=							parse_args()

	# Import training_script as a module.
	a										=							Path(args.training_script    )
	sys.path.append(str(script_fpath.parent.resolve()    )    )
	a										=							script_fpath.stem
	a										=							importlib.import_module(__lowerCamelCase    )

	# Patch sys.argv
	a										=							[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()

