
'''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()
UpperCAmelCase__ :			Union[str, Any]											=		logging.get_logger(__name__)







def 	A  (   UpperCamelCase_       :  int    ,						UpperCamelCase_       :  Optional[Any]=False    ,						UpperCamelCase_       :  List[Any]=False   )       ->							Dict:







			'''simple docstring'''



			lowerCAmelCase__										=				"backbone." if is_semantic else ""

			lowerCAmelCase__										=				[]
			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  (   UpperCamelCase_       :  List[Any]    ,						UpperCamelCase_       :  str    ,						UpperCamelCase_       :  Any=False    ,						UpperCamelCase_       :  Any=False   )       ->							int:







			'''simple docstring'''



			for i in range(config.num_hidden_layers   ):
						lowerCAmelCase__										=				"backbone." if is_semantic else ""
						# queries, keys and values
						lowerCAmelCase__										=				state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""   )
						lowerCAmelCase__										=				state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""   )
						lowerCAmelCase__										=				state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""   )

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

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

						lowerCAmelCase__										=				gamma_a
						lowerCAmelCase__										=				gamma_a







def 	A  (   UpperCamelCase_       :  List[Any]    ,						UpperCamelCase_       :  List[Any]    ,						UpperCamelCase_       :  Optional[Any]   )       ->							List[str]:







			'''simple docstring'''



			lowerCAmelCase__										=				dct.pop(UpperCamelCase_   )
			lowerCAmelCase__										=				val







def 	A  (   )       ->							Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__										=				"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase__										=				Image.open(requests.get(UpperCamelCase_    ,						stream=UpperCamelCase_   ).raw   )
			return im







@torch.no_grad()
def 	A  (   UpperCamelCase_       :  Any    ,						UpperCamelCase_       :  int    ,						UpperCamelCase_       :  int=False   )       ->							int:







			'''simple docstring'''



			lowerCAmelCase__										=				False if "rvlcdip" in checkpoint_url else True
			lowerCAmelCase__										=				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:
						lowerCAmelCase__										=				10_24
						lowerCAmelCase__										=				40_96
						lowerCAmelCase__										=				24
						lowerCAmelCase__										=				16

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

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

			lowerCAmelCase__										=				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
			lowerCAmelCase__										=				BeitForMaskedImageModeling(UpperCamelCase_   ) if has_lm_head else BeitForImageClassification(UpperCamelCase_   )
			model.eval()
			model.load_state_dict(UpperCamelCase_   )

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

			lowerCAmelCase__										=				image_processor(images=UpperCamelCase_    ,						return_tensors="pt"   )
			lowerCAmelCase__										=				encoding["pixel_values"]

			lowerCAmelCase__										=				model(UpperCamelCase_   )
			lowerCAmelCase__										=				outputs.logits

			# verify logits
			lowerCAmelCase__										=				[1, 16] if "rvlcdip" in checkpoint_url else [1, 1_96, 81_92]
			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:
									lowerCAmelCase__										=				"dit-base" if "base" in checkpoint_url else "dit-large"
						else:
									lowerCAmelCase__										=				"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__ :			Any											=		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__ :			List[Any]											=		parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








import argparse


_UpperCAmelCase						:  Union[str, Any]									=			"""docs/source/_static/js/custom.js"""





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							with open(UpperCamelCase__  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														snake_case_     							=       f.readlines()
							snake_case_     							=       0

							# First let's put the right version
							while not lines[index].startswith('const stableVersion =' ):
														index += 1
							snake_case_     							=       F'''const stableVersion = "v{version}"\n'''

							# Then update the dictionary
							while not lines[index].startswith('const versionMapping = {' ):
														index += 1

							# We go until the end
							while not lines[index].startswith('}' ):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

							with open(UpperCamelCase__  ,				'w'  ,				encoding='utf-8'  ,				newline='\n' ) as f:
														f.writelines(UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  int									=			argparse.ArgumentParser()
					parser.add_argument("""--version""", help="""Release version.""")
					_UpperCAmelCase						:  str									=			parser.parse_args()
					update_custom_js(args.version)




