





"""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	(  snake_case_    :    Optional[int]							,snake_case_    :    Tuple=False							,snake_case_    :    Union[str, Any]=False		)  ->int:



    '''simple docstring'''







    __A       :		int							=	'''backbone.''' if is_semantic else ''''''

    __A       :		str							=	[]
    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	(  snake_case_    :    int							,snake_case_    :    Optional[int]							,snake_case_    :    Optional[int]=False							,snake_case_    :    Any=False		)  ->Union[str, Any]:



    '''simple docstring'''







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

        __A       :		int							=	in_proj_weight[
            : config.hidden_size, :
        ]
        __A       :		Any							=	q_bias
        __A       :		Any							=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __A       :		Union[str, Any]							=	in_proj_weight[
            -config.hidden_size :, :
        ]
        __A       :		Any							=	v_bias

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

        __A       :		Any							=	gamma_a
        __A       :		Optional[Any]							=	gamma_a







def 						__lowercase	(  snake_case_    :    Tuple							,snake_case_    :    Tuple							,snake_case_    :    Any		)  ->Any:



    '''simple docstring'''







    __A       :		Any							=	dct.pop(snake_case_		)
    __A       :		Optional[Any]							=	val







def 						__lowercase	(  )  ->int:



    '''simple docstring'''







    __A       :		Optional[int]							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __A       :		Optional[int]							=	Image.open(requests.get(snake_case_							,stream=snake_case_		).raw		)
    return im







@torch.no_grad()
def 						__lowercase	(  snake_case_    :    List[str]							,snake_case_    :    str							,snake_case_    :    Tuple=False		)  ->Dict:



    '''simple docstring'''







    __A       :		List[str]							=	False if '''rvlcdip''' in checkpoint_url else True
    __A       :		List[str]							=	BeitConfig(use_absolute_position_embeddings=snake_case_							,use_mask_token=snake_case_		)

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        __A       :		int							=	1024
        __A       :		int							=	4096
        __A       :		Union[str, Any]							=	24
        __A       :		Dict							=	16

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

    # load state_dict of original model, remove and rename some keys
    __A       :		Any							=	torch.hub.load_state_dict_from_url(snake_case_							,map_location='''cpu'''		)['''model''']

    __A       :		Any							=	create_rename_keys(snake_case_							,has_lm_head=snake_case_		)
    for src, dest in rename_keys:
        rename_key(snake_case_							,snake_case_							,snake_case_		)
    read_in_q_k_v(snake_case_							,snake_case_							,has_lm_head=snake_case_		)

    # load HuggingFace model
    __A       :		Optional[int]							=	BeitForMaskedImageModeling(snake_case_		) if has_lm_head else BeitForImageClassification(snake_case_		)
    model.eval()
    model.load_state_dict(snake_case_		)

    # Check outputs on an image
    __A       :		Optional[Any]							=	BeitImageProcessor(
        size=config.image_size							,resample=PILImageResampling.BILINEAR							,do_center_crop=snake_case_		)
    __A       :		List[str]							=	prepare_img()

    __A       :		str							=	image_processor(images=snake_case_							,return_tensors='''pt'''		)
    __A       :		List[str]							=	encoding['''pixel_values''']

    __A       :		str							=	model(snake_case_		)
    __A       :		Optional[Any]							=	outputs.logits

    # verify logits
    __A       :		Dict							=	[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
    assert logits.shape == torch.Size(snake_case_		), "Shape of logits not as expected"

    Path(snake_case_		).mkdir(exist_ok=snake_case_		)
    print(F"""Saving model to {pytorch_dump_folder_path}"""		)
    model.save_pretrained(snake_case_		)
    print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
    image_processor.save_pretrained(snake_case_		)

    if push_to_hub:
        if has_lm_head:
            __A       :		str							=	'''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(snake_case_							,snake_case_		)							,organization='''nielsr'''							,commit_message='''Add image processor'''							,use_temp_dir=snake_case_							,)
        model.push_to_hub(
            repo_path_or_name=Path(snake_case_							,snake_case_		)							,organization='''nielsr'''							,commit_message='''Add model'''							,use_temp_dir=snake_case_							,)


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"""



from collections import namedtuple

a_					=     namedtuple("""from_to""", """from_ to""")

a_					=     {
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1000),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00454, 264.172),
    """cubicyard""": from_to(0.76455, 1.30795),
    """cubicfoot""": from_to(0.028, 35.3147),
    """cup""": from_to(0.000236588, 4226.75),
}







def 						__lowercase	(  snake_case_    :    float							,snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







    if from_type not in METRIC_CONVERSION:
        raise ValueError(
            F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
            + ''', '''.join(snake_case_		)		)
    if to_type not in METRIC_CONVERSION:
        raise ValueError(
            F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
            + ''', '''.join(snake_case_		)		)
    return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
       import doctest

       doctest.testmod()




