




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__					    =    logging.get_logger(__name__)







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       Tuple=False   ,   SCREAMING_SNAKE_CASE_							:       List[str]=False  ):
					__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   (		SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       Optional[int]   ,   SCREAMING_SNAKE_CASE_							:       Any=False   ,   SCREAMING_SNAKE_CASE_							:       Tuple=False  ):
					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   (		SCREAMING_SNAKE_CASE_							:       int   ,   SCREAMING_SNAKE_CASE_							:       Any   ,   SCREAMING_SNAKE_CASE_							:       Tuple  ):
					__lowerCAmelCase            = dct.pop(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = val







def 			_a   (		):
					__lowerCAmelCase            = "http://images.cocodataset.org/val2017/000000039769.jpg"
					__lowerCAmelCase            = Image.open(requests.get(SCREAMING_SNAKE_CASE_   ,   stream=SCREAMING_SNAKE_CASE_  ).raw  )
					return im







@torch.no_grad()
def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[str]   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       int=False  ):
					__lowerCAmelCase            = False if "rvlcdip" in checkpoint_url else True
					__lowerCAmelCase            = BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_   ,   use_mask_token=SCREAMING_SNAKE_CASE_  )

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

					__lowerCAmelCase            = create_rename_keys(SCREAMING_SNAKE_CASE_   ,   has_lm_head=SCREAMING_SNAKE_CASE_  )
					for src, dest in rename_keys:
										rename_key(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
					read_in_q_k_v(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   has_lm_head=SCREAMING_SNAKE_CASE_  )

					# load HuggingFace model
					__lowerCAmelCase            = BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_  ) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_  )
					model.eval()
					model.load_state_dict(SCREAMING_SNAKE_CASE_  )

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

					__lowerCAmelCase            = image_processor(images=SCREAMING_SNAKE_CASE_   ,   return_tensors="pt"  )
					__lowerCAmelCase            = encoding["pixel_values"]

					__lowerCAmelCase            = model(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = outputs.logits

					# verify logits
					__lowerCAmelCase            = [1, 16] if "rvlcdip" in checkpoint_url else [1, 1_96, 81_92]
					assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_  ), "Shape of logits not as expected"

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

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


if __name__ == "__main__":
					UpperCamelCase__					    =    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__					    =    parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):
					if divisor % 5 == 0 or divisor % 2 == 0:
										return 0
					__lowerCAmelCase            = 1
					__lowerCAmelCase            = 1
					while repunit:
										__lowerCAmelCase            = (10 * repunit + 1) % divisor
										repunit_index += 1
					return repunit_index







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int = 1_00_00_00  ):
					__lowerCAmelCase            = limit - 1
					if divisor % 2 == 0:
										divisor += 1
					while least_divisible_repunit(SCREAMING_SNAKE_CASE_  ) <= limit:
										divisor += 2
					return divisor


if __name__ == "__main__":
					print(f'''{solution() = }''')




