



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_      (	__a     :				str ,		__a     :				Any=False ,		__a     :				int=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 			A_      (	__a     :				Any ,		__a     :				Optional[Any] ,		__a     :				str=False ,		__a     :				List[Any]=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 			A_      (	__a     :				Any ,		__a     :				Any ,		__a     :				Any		):

				"""simple docstring"""
				a__				      =		dct.pop(__a		)
				a__				      =		val

def 			A_      (	):

				"""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 			A_      (	__a     :				str ,		__a     :				List[str] ,		__a     :				Union[str, Any]=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__				      =		1_024
								a__				      =		4_096
								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, 8_192]
				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__":
	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)











from __future__ import annotations

UpperCAmelCase							 =					8.988E9  # units = N * m^s * C^-2

def 			A_      (	__a     :				float ,		__a     :				float ,		__a     :				float ,		__a     :				float		):

				"""simple docstring"""
				a__				      =		abs(chargea * chargea		)

				if (force, chargea, chargea, distance).count(0		) != 1:
								raise ValueError("""One and only one argument must be 0"""		)
				if distance < 0:
								raise ValueError("""Distance cannot be negative"""		)
				if force == 0:
								a__				      =		COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								a__				      =		(COULOMBS_CONSTANT * charge_product / abs(__a		)) ** 0.5
								return {"distance": distance}
				raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

