




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




def     __magic_name__       (				A	:      Optional[Any],						A	:      str=False,						A	:      List[Any]=False     ):






			'''simple docstring'''







			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     __magic_name__       (				A	:      str,						A	:      List[Any],						A	:      Union[str, Any]=False,						A	:      str=False     ):






			'''simple docstring'''







			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     __magic_name__       (				A	:      Dict,						A	:      Union[str, Any],						A	:      List[Any]     ):






			'''simple docstring'''







			a					=    dct.pop(A     )
			a					=    val




def     __magic_name__       (				):






			'''simple docstring'''







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




@torch.no_grad()
def     __magic_name__       (				A	:      Optional[int],						A	:      List[Any],						A	:      Optional[int]=False     ):






			'''simple docstring'''







			a					=    False if "rvlcdip" in checkpoint_url else True
			a					=    BeitConfig(use_absolute_position_embeddings=A,						use_mask_token=A     )

			# 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(A,						A,						repo_type="dataset"     ),						"r"     )     )
						a					=    {int(A     ): 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(A,						map_location="cpu"     )["model"]

			a					=    create_rename_keys(A,						has_lm_head=A     )
			for src, dest in rename_keys:
						rename_key(A,						A,						A     )
			read_in_q_k_v(A,						A,						has_lm_head=A     )

			# load HuggingFace model
			a					=    BeitForMaskedImageModeling(A     ) if has_lm_head else BeitForImageClassification(A     )
			model.eval()
			model.load_state_dict(A     )

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

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

			a					=    model(A     )
			a					=    outputs.logits

			# verify logits
			a					=    [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
			assert logits.shape == torch.Size(A     ), "Shape of logits not as expected"

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

			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(A,						A     ),						organization="nielsr",						commit_message="Add image processor",						use_temp_dir=A,						)
						model.push_to_hub(
						    repo_path_or_name=Path(A,						A     ),						organization="nielsr",						commit_message="Add model",						use_temp_dir=A,						)


if __name__ == "__main__":
		__lowerCAmelCase	:       List[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',
		)
		__lowerCAmelCase	:       int     =	parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






