
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int)					->    int:






							'''simple docstring'''





							__UpperCamelCase	:   Dict			   =					[1]

							__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					0, 0, 0
							__UpperCamelCase	:   List[str]			   =					ugly_nums[ia] * 2
							__UpperCamelCase	:   Optional[int]			   =					ugly_nums[ia] * 3
							__UpperCamelCase	:   Optional[int]			   =					ugly_nums[ia] * 5

							for _ in range(1						,  _lowerCamelCase):
														__UpperCamelCase	:   List[str]			   =					min(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
														ugly_nums.append(_lowerCamelCase)
														if next_num == next_a:
																					ia += 1
																					__UpperCamelCase	:   Tuple			   =					ugly_nums[ia] * 2
														if next_num == next_a:
																					ia += 1
																					__UpperCamelCase	:   Dict			   =					ugly_nums[ia] * 3
														if next_num == next_a:
																					ia += 1
																					__UpperCamelCase	:   Union[str, Any]			   =					ugly_nums[ia] * 5
							return ugly_nums[-1]


if __name__ == "__main__":
		from doctest import testmod

		testmod(verbose=True)
		print(f"{ugly_numbers(200) = }")

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()
lowercase  :					Dict													=       logging.get_logger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[int]			   =					"backbone." if is_semantic else ""

							__UpperCamelCase	:   Optional[Any]			   =					[]
							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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			Optional[int]=False)					->    List[str]:






							'''simple docstring'''





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

														__UpperCamelCase	:   Optional[int]			   =					in_proj_weight[
														    : config.hidden_size, :
														]
														__UpperCamelCase	:   Any			   =					q_bias
														__UpperCamelCase	:   Dict			   =					in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__UpperCamelCase	:   List[Any]			   =					in_proj_weight[
														    -config.hidden_size :, :
														]
														__UpperCamelCase	:   Tuple			   =					v_bias

														# gamma_1 and gamma_2
														# we call them lambda because otherwise they are renamed when using .from_pretrained
														__UpperCamelCase	:   Union[str, Any]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_1')
														__UpperCamelCase	:   Optional[int]			   =					state_dict.pop(F'{prefix}blocks.{i}.gamma_2')

														__UpperCamelCase	:   Any			   =					gamma_a
														__UpperCamelCase	:   List[Any]			   =					gamma_a




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Optional[Any])					->    Any:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					dct.pop(_lowerCamelCase)
							__UpperCamelCase	:   str			   =					val




def 	_SCREAMING_SNAKE_CASE					(		)					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase	:   Dict			   =					Image.open(requests.get(_lowerCamelCase						,  stream=_lowerCamelCase).raw)
							return im




@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Dict=False)					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					False if "rvlcdip" in checkpoint_url else True
							__UpperCamelCase	:   int			   =					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:
														__UpperCamelCase	:   Optional[int]			   =					1_024
														__UpperCamelCase	:   Union[str, Any]			   =					4_096
														__UpperCamelCase	:   Tuple			   =					24
														__UpperCamelCase	:   int			   =					16

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

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

							__UpperCamelCase	:   Optional[Any]			   =					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
							__UpperCamelCase	:   List[Any]			   =					BeitForMaskedImageModeling(_lowerCamelCase) if has_lm_head else BeitForImageClassification(_lowerCamelCase)
							model.eval()
							model.load_state_dict(_lowerCamelCase)

							# Check outputs on an image
							__UpperCamelCase	:   Dict			   =					BeitImageProcessor(
							    size=config.image_size						,  resample=PILImageResampling.BILINEAR						,  do_center_crop=_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					prepare_img()

							__UpperCamelCase	:   Union[str, Any]			   =					image_processor(images=_lowerCamelCase						,  return_tensors="pt")
							__UpperCamelCase	:   str			   =					encoding["pixel_values"]

							__UpperCamelCase	:   str			   =					model(_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					outputs.logits

							# verify logits
							__UpperCamelCase	:   Tuple			   =					[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:
																					__UpperCamelCase	:   List[str]			   =					"dit-base" if "base" in checkpoint_url else "dit-large"
														else:
																					__UpperCamelCase	:   Optional[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__":
		lowercase  :					Optional[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',
		)
		lowercase  :					Tuple													=       parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)