




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()
__lowerCAmelCase		:				str       			=		logging.get_logger(__name__)




def 			__magic_name__				( A		:					Union[str, Any],  A		:					List[str]=False,  A		:					Optional[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 			__magic_name__				( A		:					Optional[Any],  A		:					str,  A		:					str=False,  A		:					Union[str, 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 			__magic_name__				( A		:					int,  A		:					int,  A		:					int   ):






			'''simple docstring'''







			a    =			dct.pop(A   )
			a    =			val




def 			__magic_name__				( ):






			'''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 			__magic_name__				( A		:					Any,  A		:					Optional[Any],  A		:					Optional[int]=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    =			1024
						a    =			4096
						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, 8192]
			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__":
		__lowerCAmelCase		:				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',
		)
		__lowerCAmelCase		:				List[str]       			=		parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	# Initialise PyTorch model
	A__							:    str        =		AlbertConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	A__							:    List[Any]        =		AlbertForPreTraining(lowercase_					)

	# Load weights from tf checkpoint
	load_tf_weights_in_albert(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)


if __name__ == "__main__":
	A_       : int         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_       : int         =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



