





"""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()
__UpperCAmelCase						=			logging.get_logger(__name__)






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Optional[Any]=False							,			lowerCAmelCase__   :			Tuple=False			)    ->			Optional[int]:



			'''simple docstring'''
			a__  :	Tuple		 =			"backbone." if is_semantic else ""

			a__  :	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      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Dict=False							,			lowerCAmelCase__   :			Any=False			)    ->			int:



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

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

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						a__  :	Optional[int]		 =			state_dict.pop(F"{prefix}blocks.{i}.gamma_1"			)
						a__  :	Tuple		 =			state_dict.pop(F"{prefix}blocks.{i}.gamma_2"			)

						a__  :	Optional[int]		 =			gamma_a
						a__  :	List[Any]		 =			gamma_a






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Any			)    ->			Union[str, Any]:



			'''simple docstring'''
			a__  :	List[Any]		 =			dct.pop(lowerCAmelCase__			)
			a__  :	Tuple		 =			val






def      lowercase__ (					)    ->			str:



			'''simple docstring'''
			a__  :	Any		 =			"http://images.cocodataset.org/val2017/000000039769.jpg"
			a__  :	Optional[int]		 =			Image.open(requests.get(lowerCAmelCase__							,			stream=lowerCAmelCase__			).raw			)
			return im






@torch.no_grad()
def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Any=False			)    ->			int:



			'''simple docstring'''
			a__  :	List[str]		 =			False if "rvlcdip" in checkpoint_url else True
			a__  :	str		 =			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:
						a__  :	Optional[int]		 =			1_0_2_4
						a__  :	List[str]		 =			4_0_9_6
						a__  :	List[str]		 =			2_4
						a__  :	List[str]		 =			1_6

			# labels
			if "rvlcdip" in checkpoint_url:
						a__  :	Union[str, Any]		 =			1_6
						a__  :	Tuple		 =			"huggingface/label-files"
						a__  :	Optional[Any]		 =			"rvlcdip-id2label.json"
						a__  :	Any		 =			json.load(open(hf_hub_download(lowerCAmelCase__							,			lowerCAmelCase__							,			repo_type="dataset"			)							,			"r"			)			)
						a__  :	Union[str, Any]		 =			{int(lowerCAmelCase__			): v for k, v in idalabel.items()}
						a__  :	Dict		 =			idalabel
						a__  :	int		 =			{v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			a__  :	List[str]		 =			torch.hub.load_state_dict_from_url(lowerCAmelCase__							,			map_location="cpu"			)["model"]

			a__  :	str		 =			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
			a__  :	Any		 =			BeitForMaskedImageModeling(lowerCAmelCase__			) if has_lm_head else BeitForImageClassification(lowerCAmelCase__			)
			model.eval()
			model.load_state_dict(lowerCAmelCase__			)

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

			a__  :	Optional[int]		 =			image_processor(images=lowerCAmelCase__							,			return_tensors="pt"			)
			a__  :	List[Any]		 =			encoding["pixel_values"]

			a__  :	Optional[Any]		 =			model(lowerCAmelCase__			)
			a__  :	Tuple		 =			outputs.logits

			# verify logits
			a__  :	Optional[int]		 =			[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:
									a__  :	int		 =			"dit-base" if "base" in checkpoint_url else "dit-large"
						else:
									a__  :	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(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__":
		__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)






"""simple docstring"""






import pytest

import datasets


# Import fixture modules as plugins
__UpperCAmelCase						=			['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']






def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			str			)    ->			str:



			'''simple docstring'''
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ["integration", "unit"]			):
									continue
						item.add_marker(pytest.mark.unit			)






def      lowercase__ (					lowerCAmelCase__   :			List[Any]			)    ->			Any:



			'''simple docstring'''
			config.addinivalue_line("markers"							,			"torchaudio_latest: mark test to run with torchaudio>=0.12"			)






@pytest.fixture(autouse=lowerCAmelCase__			)
def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			Dict			)    ->			List[str]:



			'''simple docstring'''
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			a__  :	Dict		 =			tmp_path_factory.getbasetemp() / "cache"
			a__  :	int		 =			test_hf_cache_home / "datasets"
			a__  :	Tuple		 =			test_hf_cache_home / "metrics"
			a__  :	Any		 =			test_hf_cache_home / "modules"
			monkeypatch.setattr("datasets.config.HF_DATASETS_CACHE"							,			str(lowerCAmelCase__			)			)
			monkeypatch.setattr("datasets.config.HF_METRICS_CACHE"							,			str(lowerCAmelCase__			)			)
			monkeypatch.setattr("datasets.config.HF_MODULES_CACHE"							,			str(lowerCAmelCase__			)			)
			a__  :	Optional[int]		 =			test_hf_datasets_cache / "downloads"
			monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH"							,			str(lowerCAmelCase__			)			)
			a__  :	Tuple		 =			test_hf_datasets_cache / "downloads" / "extracted"
			monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"							,			str(lowerCAmelCase__			)			)






@pytest.fixture(autouse=lowerCAmelCase__							,			scope="session"			)
def      lowercase__ (					)    ->			Union[str, Any]:



			'''simple docstring'''
			datasets.disable_progress_bar()






@pytest.fixture(autouse=lowerCAmelCase__			)
def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]			)    ->			Optional[Any]:



			'''simple docstring'''
			# don't take tests into account when counting downloads
			monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"							,			lowerCAmelCase__			)






@pytest.fixture
def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]			)    ->			str:



			'''simple docstring'''
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"							,			lowerCAmelCase__			)