
"""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_    							=					logging.get_logger(__name__)






def  lowercase						(	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  lowercase						(	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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  dct.pop(lowerCAmelCase__						)
			lowerCamelCase_					=  val






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






@torch.no_grad()
def  lowercase						(	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_024
						lowerCamelCase_					=  4_096
						lowerCamelCase_					=  24
						lowerCamelCase_					=  16

			# labels
			if "rvlcdip" in checkpoint_url:
						lowerCamelCase_					=  16
						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, 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:
									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)





"""simple docstring"""


def  lowercase						(	lowerCAmelCase__						):
			return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



