
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()
__A				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__=False	, lowerCamelCase__=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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False	, lowerCamelCase__=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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			dct.pop(lowerCamelCase__    )
				lowerCamelCase_						=			val





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





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False    ):
				lowerCamelCase_						=			False if "rvlcdip" in checkpoint_url else True
				lowerCamelCase_						=			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:
								lowerCamelCase_						=			1_0_2_4
								lowerCamelCase_						=			4_0_9_6
								lowerCamelCase_						=			2_4
								lowerCamelCase_						=			1_6

				# labels
				if "rvlcdip" in checkpoint_url:
								lowerCamelCase_						=			1_6
								lowerCamelCase_						=			"huggingface/label-files"
								lowerCamelCase_						=			"rvlcdip-id2label.json"
								lowerCamelCase_						=			json.load(open(hf_hub_download(lowerCamelCase__	, lowerCamelCase__	, repo_type="dataset"    )	, "r"    )    )
								lowerCamelCase_						=			{int(lowerCamelCase__    ): 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(lowerCamelCase__	, map_location="cpu"    )["model"]

				lowerCamelCase_						=			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
				lowerCamelCase_						=			BeitForMaskedImageModeling(lowerCamelCase__    ) if has_lm_head else BeitForImageClassification(lowerCamelCase__    )
				model.eval()
				model.load_state_dict(lowerCamelCase__    )

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

				lowerCamelCase_						=			image_processor(images=lowerCamelCase__	, return_tensors="pt"    )
				lowerCamelCase_						=			encoding["pixel_values"]

				lowerCamelCase_						=			model(lowerCamelCase__    )
				lowerCamelCase_						=			outputs.logits

				# verify logits
				lowerCamelCase_						=			[1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
				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:
												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(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__":
			__A				=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''',
			)
			__A				=parser.parse_args()
			convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






from __future__ import annotations


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   List[Any]:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      text, pattern
									lowercase__     ,		lowercase__            =      len(UpperCamelCase_							), len(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if char == self.pattern[i]:
													return i
									return -1

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if self.pattern[i] != self.text[current_pos + i]:
													return current_pos + i
									return -1


							def       lowerCamelCase_ (							self:	List[Any]							)			->   list[int]:





									"""simple docstring"""







									lowercase__            =      []
									for i in range(self.textLen - self.patLen + 1							):
											lowercase__            =      self.mismatch_in_text(UpperCamelCase_							)
											if mismatch_index == -1:
													positions.append(UpperCamelCase_							)
											else:
													lowercase__            =      self.match_in_pattern(self.text[mismatch_index]							)
													lowercase__            =      (
													    mismatch_index - match_index
													)  # shifting index lgtm [py/multiple-definition]
									return positions
lowerCAmelCase       =							'ABAABA'
lowerCAmelCase       =							'AB'
lowerCAmelCase       =							BoyerMooreSearch(text, pattern)
lowerCAmelCase       =							bms.bad_character_heuristic()

if len(positions) == 0:
			print('No match found')
else:
			print('Pattern found in following positions: ')
			print(positions)


