

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()
lowercase_       		=  logging.get_logger(__name__)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Dict=False    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=False      )							->      str:





    '''simple docstring'''






    A__          =   'backbone.' if is_semantic else ''

    A__          =   []
    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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Dict=False    ,			SCREAMING_SNAKE_CASE__			:	int=False      )							->      List[str]:





    '''simple docstring'''






    for i in range(config.num_hidden_layers      ):
        A__          =   'backbone.' if is_semantic else ''
        # queries, keys and values
        A__          =   state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'      )
        A__          =   state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'      )
        A__          =   state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'      )

        A__          =   in_proj_weight[
            : config.hidden_size, :
        ]
        A__          =   q_bias
        A__          =   in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        A__          =   in_proj_weight[
            -config.hidden_size :, :
        ]
        A__          =   v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        A__          =   state_dict.pop(f'{prefix}blocks.{i}.gamma_1'      )
        A__          =   state_dict.pop(f'{prefix}blocks.{i}.gamma_2'      )

        A__          =   gamma_a
        A__          =   gamma_a
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      Dict:





    '''simple docstring'''






    A__          =   dct.pop(SCREAMING_SNAKE_CASE__      )
    A__          =   val
def 						_snake_case(    )							->      str:





    '''simple docstring'''






    A__          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
    A__          =   Image.open(requests.get(SCREAMING_SNAKE_CASE__    ,			stream=SCREAMING_SNAKE_CASE__      ).raw      )
    return im
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]=False      )							->      str:





    '''simple docstring'''






    A__          =   False if 'rvlcdip' in checkpoint_url else True
    A__          =   BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE__    ,			use_mask_token=SCREAMING_SNAKE_CASE__      )

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        A__          =   1024
        A__          =   4096
        A__          =   24
        A__          =   16

    # labels
    if "rvlcdip" in checkpoint_url:
        A__          =   16
        A__          =   'huggingface/label-files'
        A__          =   'rvlcdip-id2label.json'
        A__          =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			repo_type='dataset'      )    ,			'r'      )      )
        A__          =   {int(SCREAMING_SNAKE_CASE__      ): v for k, v in idalabel.items()}
        A__          =   idalabel
        A__          =   {v: k for k, v in idalabel.items()}

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

    A__          =   create_rename_keys(SCREAMING_SNAKE_CASE__    ,			has_lm_head=SCREAMING_SNAKE_CASE__      )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    read_in_q_k_v(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			has_lm_head=SCREAMING_SNAKE_CASE__      )

    # load HuggingFace model
    A__          =   BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE__      ) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE__      )
    model.eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE__      )

    # Check outputs on an image
    A__          =   BeitImageProcessor(
        size=config.image_size    ,			resample=PILImageResampling.BILINEAR    ,			do_center_crop=SCREAMING_SNAKE_CASE__      )
    A__          =   prepare_img()

    A__          =   image_processor(images=SCREAMING_SNAKE_CASE__    ,			return_tensors='pt'      )
    A__          =   encoding['pixel_values']

    A__          =   model(SCREAMING_SNAKE_CASE__      )
    A__          =   outputs.logits

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

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

    if push_to_hub:
        if has_lm_head:
            A__          =   'dit-base' if 'base' in checkpoint_url else 'dit-large'
        else:
            A__          =   '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(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			organization='nielsr'    ,			commit_message='Add image processor'    ,			use_temp_dir=SCREAMING_SNAKE_CASE__    ,			)
        model.push_to_hub(
            repo_path_or_name=Path(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			organization='nielsr'    ,			commit_message='Add model'    ,			use_temp_dir=SCREAMING_SNAKE_CASE__    ,			)


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




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Tuple:





    '''simple docstring'''






    A__          =   0
    A__          =   len(SCREAMING_SNAKE_CASE__      ) - 1

    while left <= right:
        # avoid divided by 0 during interpolation
        if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                return left
            else:
                return None

        A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
            sorted_collection[right] - sorted_collection[left]
        )

        # out of range check
        if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
            return None

        A__          =   sorted_collection[point]
        if current_item == item:
            return point
        else:
            if point < left:
                A__          =   left
                A__          =   point
            elif point > right:
                A__          =   right
                A__          =   point
            else:
                if item < current_item:
                    A__          =   point - 1
                else:
                    A__          =   point + 1
    return None
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      str:





    '''simple docstring'''






    if sorted_collection[left] == sorted_collection[right]:
        if sorted_collection[left] == item:
            return left
        else:
            return None

    A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
        sorted_collection[right] - sorted_collection[left]
    )

    # out of range check
    if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
        return None

    if sorted_collection[point] == item:
        return point
    elif point < left:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    elif point > right:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    else:
        if sorted_collection[point] > item:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point - 1      )
        else:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point + 1    ,			SCREAMING_SNAKE_CASE__      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      Tuple:





    '''simple docstring'''






    if collection != sorted(SCREAMING_SNAKE_CASE__      ):
        raise ValueError('Collection must be ascending sorted'      )
    return True


if __name__ == "__main__":
     import sys
     lowercase_       		=  0
     if debug == 1:
          lowercase_       		=  [10, 30, 40, 45, 50, 66, 77, 93]
          try:
               __assert_sorted(collection)
          except ValueError:
               sys.exit("Sequence must be ascending sorted to apply interpolation search")
          lowercase_       		=  67

     lowercase_       		=  interpolation_search(collection, target)
     if result is not None:
          print(f"""{target} found at positions: {result}""")
     else:
          print("Not found")

