




"""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()
__A   :  str					=      logging.get_logger(__name__)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=False,  _lowerCamelCase=False				)			->Any:






		"""simple docstring"""


		__lowercase				:		str												=				'''backbone.''' if is_semantic else ''''''

		__lowercase				:		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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase=False,  _lowerCamelCase=False				)			->Union[str, Any]:






		"""simple docstring"""


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

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

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

				__lowercase				:		Dict												=				gamma_a
				__lowercase				:		str												=				gamma_a

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		__lowercase				:		int												=				dct.pop(__UpperCamelCase				)
		__lowercase				:		Any												=				val

def 						snake_case__       (					)			->Any:






		"""simple docstring"""


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

@torch.no_grad()
def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase=False				)			->str:






		"""simple docstring"""


		__lowercase				:		List[str]												=				False if '''rvlcdip''' in checkpoint_url else True
		__lowercase				:		int												=				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:
				__lowercase				:		Union[str, Any]												=				10_24
				__lowercase				:		List[Any]												=				40_96
				__lowercase				:		Any												=				24
				__lowercase				:		Union[str, Any]												=				16

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

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

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

		# Check outputs on an image
		__lowercase				:		Optional[int]												=				BeitImageProcessor(
		    size=config.image_size,  resample=PILImageResampling.BILINEAR,  do_center_crop=__UpperCamelCase				)
		__lowercase				:		Optional[Any]												=				prepare_img()

		__lowercase				:		Any												=				image_processor(images=__UpperCamelCase,  return_tensors="pt"				)
		__lowercase				:		List[str]												=				encoding['''pixel_values''']

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

		# verify logits
		__lowercase				:		str												=				[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:
						__lowercase				:		int												=				'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
				else:
						__lowercase				:		Dict												=				'''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__":
						__A   :  List[str]					=      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   :  str					=      parser.parse_args()
						convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""







from collections import deque

from .hash_table import HashTable




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				def __init__(     self					:	Optional[int]    ,   *lowercase__					:	Any    ,   **lowercase__					:	Union[str, Any]						):
						super().__init__(*lowercase__    ,   **lowercase__						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	Dict    ,   lowercase__					:	Dict						):
						__lowercase				:		Dict												=				deque([]						) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(lowercase__						)
						__lowercase				:		Union[str, Any]												=				self.values[key]



				def       snake_case    (     self					:	str						):
						return (
						    sum(self.charge_factor - len(lowercase__						) for slot in self.values						)
						    / self.size_table
						    * self.charge_factor
						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	List[str]    ,   lowercase__					:	str=None						):
						if not (
						    len(self.values[key]						) == self.charge_factor and self.values.count(lowercase__						) == 0
						):
								return key
						return super()._collision_resolution(lowercase__    ,   lowercase__						)
