


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







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  List[Any]=False						,							lowercase  :  Tuple=False )	->						List[Any]:


    """simple docstring"""





    snake_case    :      List[str]      					=    "backbone." if is_semantic else ""

    snake_case    :      int      					=    []
    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						(						lowercase  :  Tuple						,							lowercase  :  str						,							lowercase  :  Tuple=False						,							lowercase  :  Tuple=False )	->						int:


    """simple docstring"""





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

        snake_case    :      List[Any]      					=    in_proj_weight[
            : config.hidden_size, :
        ]
        snake_case    :      Union[str, Any]      					=    q_bias
        snake_case    :      Optional[Any]      					=    in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case    :      List[Any]      					=    in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case    :      List[Any]      					=    v_bias

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

        snake_case    :      str      					=    gamma_a
        snake_case    :      str      					=    gamma_a







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Optional[int]						,							lowercase  :  Any )	->						Any:


    """simple docstring"""





    snake_case    :      List[Any]      					=    dct.pop(lowercase )
    snake_case    :      Optional[Any]      					=    val







def   __lowerCAmelCase						(						)	->						Union[str, Any]:


    """simple docstring"""





    snake_case    :      List[Any]      					=    "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case    :      List[Any]      					=    Image.open(requests.get(lowercase						,							stream=lowercase ).raw )
    return im







@torch.no_grad()
def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  str						,							lowercase  :  Dict=False )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Any      					=    False if "rvlcdip" in checkpoint_url else True
    snake_case    :      Optional[Any]      					=    BeitConfig(use_absolute_position_embeddings=lowercase						,							use_mask_token=lowercase )

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

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

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

    snake_case    :      Optional[Any]      					=    create_rename_keys(lowercase						,							has_lm_head=lowercase )
    for src, dest in rename_keys:
        rename_key(lowercase						,							lowercase						,							lowercase )
    read_in_q_k_v(lowercase						,							lowercase						,							has_lm_head=lowercase )

    # load HuggingFace model
    snake_case    :      Union[str, Any]      					=    BeitForMaskedImageModeling(lowercase ) if has_lm_head else BeitForImageClassification(lowercase )
    model.eval()
    model.load_state_dict(lowercase )

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

    snake_case    :      Optional[Any]      					=    image_processor(images=lowercase						,							return_tensors="pt" )
    snake_case    :      Tuple      					=    encoding["pixel_values"]

    snake_case    :      Any      					=    model(lowercase )
    snake_case    :      Optional[Any]      					=    outputs.logits

    # verify logits
    snake_case    :      List[str]      					=    [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
    assert logits.shape == torch.Size(lowercase ), "Shape of logits not as expected"

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

    if push_to_hub:
        if has_lm_head:
            snake_case    :      Tuple      					=    "dit-base" if "base" in checkpoint_url else "dit-large"
        else:
            snake_case    :      Optional[Any]      					=    "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(lowercase						,							lowercase )						,							organization="nielsr"						,							commit_message="Add image processor"						,							use_temp_dir=lowercase						,							)
        model.push_to_hub(
            repo_path_or_name=Path(lowercase						,							lowercase )						,							organization="nielsr"						,							commit_message="Add model"						,							use_temp_dir=lowercase						,							)


if __name__ == "__main__":
  __snake_case   			=    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""",
  )
  __snake_case   			=    parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""



from typing import List

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  dict )	->						int:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    {key: len(lowercase ) for key, value in gen_kwargs.items() if isinstance(lowercase						,							lowercase )}
    if len(set(lists_lengths.values() ) ) > 1:
        raise RuntimeError(
            (
                "Sharding is ambiguous for this dataset: "
                + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
                + "\n".join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
                + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
                + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
            ) )
    snake_case    :      int      					=    max(lists_lengths.values()						,							default=0 )
    return max(1						,							lowercase )







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int )	->						List[range]:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    []
    for group_idx in range(lowercase ):
        snake_case    :      Union[str, Any]      					=    num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        snake_case    :      int      					=    shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        snake_case    :      Dict      					=    range(lowercase						,							start + num_shards_to_add )
        shards_indices_per_group.append(lowercase )
    return shards_indices_per_group







def   __lowerCAmelCase						(						lowercase  :  dict						,							lowercase  :  int )	->						List[dict]:


    """simple docstring"""





    snake_case    :      int      					=    _number_of_shards_in_gen_kwargs(lowercase )
    if num_shards == 1:
        return [dict(lowercase )]
    else:
        snake_case    :      Optional[int]      					=    _distribute_shards(num_shards=lowercase						,							max_num_jobs=lowercase )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(lowercase						,							lowercase )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(lowercase ) )
        ]







def   __lowerCAmelCase						(						lowercase  :  List[dict] )	->						dict:


    """simple docstring"""





    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key]						,							lowercase )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }







def   __lowerCAmelCase						(						lowercase  :  np.random.Generator						,							lowercase  :  dict )	->						dict:


    """simple docstring"""





    snake_case    :      Tuple      					=    {len(lowercase ) for value in gen_kwargs.values() if isinstance(lowercase						,							lowercase )}
    snake_case    :      str      					=    {}
    for size in list_sizes:
        snake_case    :      Optional[int]      					=    list(range(lowercase ) )
        rng.shuffle(indices_per_size[size] )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    snake_case    :      Dict      					=    dict(lowercase )
    for key, value in shuffled_kwargs.items():
        if isinstance(lowercase						,							lowercase ):
            snake_case    :      Dict      					=    [value[i] for i in indices_per_size[len(lowercase )]]
    return shuffled_kwargs





