




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_		:				Optional[Any]									=     logging.get_logger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=False	,     lowerCamelCase=False   ):
    __magic_name__	:	Any								="""backbone.""" if is_semantic else """"""

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

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

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        __magic_name__	:	Union[str, Any]								=state_dict.pop(F"{prefix}blocks.{i}.gamma_1"   )
        __magic_name__	:	Any								=state_dict.pop(F"{prefix}blocks.{i}.gamma_2"   )

        __magic_name__	:	Union[str, Any]								=gamma_a
        __magic_name__	:	Union[str, Any]								=gamma_a




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
    __magic_name__	:	Union[str, Any]								=dct.pop(lowerCamelCase   )
    __magic_name__	:	str								=val




def 		lowerCAmelCase_   (	):
    __magic_name__	:	str								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
    __magic_name__	:	Any								=Image.open(requests.get(lowerCamelCase	,     stream=lowerCamelCase   ).raw   )
    return im




@torch.no_grad()
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=False   ):
    __magic_name__	:	Tuple								=False if """rvlcdip""" in checkpoint_url else True
    __magic_name__	:	Any								=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:
        __magic_name__	:	List[str]								=1024
        __magic_name__	:	Optional[int]								=4096
        __magic_name__	:	Optional[Any]								=24
        __magic_name__	:	Dict								=16

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

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

    __magic_name__	:	Union[str, Any]								=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
    __magic_name__	:	Optional[int]								=BeitForMaskedImageModeling(lowerCamelCase   ) if has_lm_head else BeitForImageClassification(lowerCamelCase   )
    model.eval()
    model.load_state_dict(lowerCamelCase   )

    # Check outputs on an image
    __magic_name__	:	str								=BeitImageProcessor(
        size=config.image_size	,     resample=PILImageResampling.BILINEAR	,     do_center_crop=lowerCamelCase   )
    __magic_name__	:	Dict								=prepare_img()

    __magic_name__	:	int								=image_processor(images=lowerCamelCase	,     return_tensors="""pt"""   )
    __magic_name__	:	Dict								=encoding["""pixel_values"""]

    __magic_name__	:	Union[str, Any]								=model(lowerCamelCase   )
    __magic_name__	:	List[Any]								=outputs.logits

    # verify logits
    __magic_name__	:	Tuple								=[1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
    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:
            __magic_name__	:	List[Any]								="""dit-base""" if """base""" in checkpoint_url else """dit-large"""
        else:
            __magic_name__	:	Tuple								="""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_		:				List[Any]									=     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_		:				int									=     parser.parse_args()
       convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










import pytest

import datasets


# Import fixture modules as plugins
UpperCAmelCase_		:				Union[str, Any]									=     ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				# 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 		lowerCAmelCase_   (	lowerCamelCase   ):
				config.addinivalue_line("""markers"""	,     """torchaudio_latest: mark test to run with torchaudio>=0.12"""   )




@pytest.fixture(autouse=lowerCamelCase   )
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
				__magic_name__	:	Tuple								=tmp_path_factory.getbasetemp() / """cache"""
				__magic_name__	:	Optional[int]								=test_hf_cache_home / """datasets"""
				__magic_name__	:	List[str]								=test_hf_cache_home / """metrics"""
				__magic_name__	:	List[str]								=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   )   )
				__magic_name__	:	Optional[Any]								=test_hf_datasets_cache / """downloads"""
				monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""	,     str(lowerCamelCase   )   )
				__magic_name__	:	Tuple								=test_hf_datasets_cache / """downloads""" / """extracted"""
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""	,     str(lowerCamelCase   )   )




@pytest.fixture(autouse=lowerCamelCase	,     scope="""session"""   )
def 		lowerCAmelCase_   (	):
				datasets.disable_progress_bar()




@pytest.fixture(autouse=lowerCamelCase   )
def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# don't take tests into account when counting downloads
				monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""	,     lowerCamelCase   )




@pytest.fixture
def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# 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   )




