






"""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 				lowercase__		(					lowerCamelCase,						lowerCamelCase=False,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE   :		str       		=    'backbone.' if is_semantic else ''

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

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

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

								_SCREAMING_SNAKE_CASE   :		Dict       		=    gamma_a
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    gamma_a





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Any       		=    dct.pop(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    val





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'http://images.cocodataset.org/val2017/000000039769.jpg'
				_SCREAMING_SNAKE_CASE   :		int       		=    Image.open(requests.get(lowerCamelCase,						stream=lowerCamelCase       ).raw       )
				return im





@torch.no_grad()
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    False if 'rvlcdip' in checkpoint_url else True
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    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:
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    1_024
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    4_096
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    24
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    16

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

				# load state_dict of original model, remove and rename some keys
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    torch.hub.load_state_dict_from_url(lowerCamelCase,						map_location='cpu'       )['model']

				_SCREAMING_SNAKE_CASE   :		Tuple       		=    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
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    BeitForMaskedImageModeling(lowerCamelCase       ) if has_lm_head else BeitForImageClassification(lowerCamelCase       )
				model.eval()
				model.load_state_dict(lowerCamelCase       )

				# Check outputs on an image
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    BeitImageProcessor(
				    size=config.image_size,						resample=PILImageResampling.BILINEAR,						do_center_crop=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    prepare_img()

				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    image_processor(images=lowerCamelCase,						return_tensors='pt'       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    encoding['pixel_values']

				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    model(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    outputs.logits

				# verify logits
				_SCREAMING_SNAKE_CASE   :		Dict       		=    [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8_192]
				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:
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'dit-base' if 'base' in checkpoint_url else 'dit-large'
								else:
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    '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)









"""simple docstring"""
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				return numa ^ numa < 0


if __name__ == "__main__":
			import doctest

			doctest.testmod()

