



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		:Tuple   	=  logging.get_logger(__name__)





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=False     ,				_UpperCAmelCase=False       ):
    __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			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=False     ,				_UpperCAmelCase=False       ):







    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			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       dct.pop(__snake_case       )
    __a		 =       val





def   __snake_case			(    ):
    __a		 =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __a		 =       Image.open(requests.get(__snake_case     ,				stream=__snake_case       ).raw       )
    return im





@torch.no_grad()
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=False       ):
    __a		 =       False if '''rvlcdip''' in checkpoint_url else True
    __a		 =       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		 =       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(__snake_case     ,				__snake_case     ,				repo_type='''dataset'''       )     ,				'''r'''       )       )
        __a		 =       {int(__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(__snake_case     ,				map_location='''cpu'''       )['''model''']

    __a		 =       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		 =       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		 =       BeitImageProcessor(
        size=config.image_size     ,				resample=PILImageResampling.BILINEAR     ,				do_center_crop=__snake_case       )
    __a		 =       prepare_img()

    __a		 =       image_processor(images=__snake_case     ,				return_tensors='''pt'''       )
    __a		 =       encoding['''pixel_values''']

    __a		 =       model(__snake_case       )
    __a		 =       outputs.logits

    # verify logits
    __a		 =       [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		 =       '''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(__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__":
 __snake_case		: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''',
 )
 __snake_case		:int   	=  parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__snake_case		:Any   	=  TypeVar('''KT''')
__snake_case		:List[str]   	=  TypeVar('''VT''')

class 					_A (			Generic[KT, VT]					):



       def __init__(	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  KT | str = "root"  ,	__SCREAMING_SNAKE_CASE							:  VT | None = None):


           '''simple docstring'''

           __a		 =       key
           __a		 =       value
           __a		 =       []



       def __repr__(	self							:  Dict):


           '''simple docstring'''

           return F'Node({self.key}: {self.value})'





       @property
       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           return len(self.forward)


class 					_A (			Generic[KT, VT]					):



       def __init__(	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  float = 0.5  ,	__SCREAMING_SNAKE_CASE							:  int = 16):


           '''simple docstring'''

           __a		 =       Node[KT, VT]()
           __a		 =       0
           __a		 =       p
           __a		 =       max_level



       def __str__(	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       list(self)

           if len(__SCREAMING_SNAKE_CASE) == 0:
               return F'SkipList(level={self.level})'

           __a		 =       max((len(str(__SCREAMING_SNAKE_CASE)) for item in items)  ,	default=4)
           __a		 =       max(__SCREAMING_SNAKE_CASE  ,	4) + 4

           __a		 =       self.head
           __a		 =       []

           __a		 =       node.forward.copy()
           lines.append(F'[{node.key}]'.ljust(__SCREAMING_SNAKE_CASE  ,	'''-''') + '''* ''' * len(__SCREAMING_SNAKE_CASE))
           lines.append(''' ''' * label_size + '''| ''' * len(__SCREAMING_SNAKE_CASE))

           while len(node.forward) != 0:
               __a		 =       node.forward[0]

               lines.append(
                   F'[{node.key}]'.ljust(__SCREAMING_SNAKE_CASE  ,	'''-''')
                   + ''' '''.join(str(n.key) if n.key == node.key else '''|''' for n in forwards))
               lines.append(''' ''' * label_size + '''| ''' * len(__SCREAMING_SNAKE_CASE))
               __a		 =       node.forward

           lines.append('''None'''.ljust(__SCREAMING_SNAKE_CASE) + '''* ''' * len(__SCREAMING_SNAKE_CASE))
           return F'SkipList(level={self.level})\n' + "\n".join(__SCREAMING_SNAKE_CASE)



       def __iter__(	self							:  int):


           '''simple docstring'''

           __a		 =       self.head

           while len(node.forward) != 0:
               yield node.forward[0].key
               __a		 =       node.forward[0]



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       1
           while random() < self.p and level < self.max_level:
               level += 1

           return level



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       []

           __a		 =       self.head

           for i in reversed(range(self.level)):
               # i < node.level - When node level is lesser than `i` decrement `i`.
               # node.forward[i].key < key - Jumping to node with key value higher
               #                             or equal to searched key would result
               #                             in skipping searched key.
               while i < node.level and node.forward[i].key < key:
                   __a		 =       node.forward[i]
               # Each leftmost node (relative to searched node) will potentially have to
               # be updated.
               update_vector.append(__SCREAMING_SNAKE_CASE)

           update_vector.reverse()  # Note that we were inserting values in reverse order.

           # len(node.forward) != 0 - If current node doesn't contain any further
           #                          references then searched key is not present.
           # node.forward[0].key == key - Next node key should be equal to search key
           #                              if key is present.
           if len(node.forward) != 0 and node.forward[0].key == key:
               return node.forward[0], update_vector
           else:
               return None, update_vector



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  KT):


           '''simple docstring'''

           __a ,     __a		 =       self._locate_node(__SCREAMING_SNAKE_CASE)

           if node is not None:
               for i, update_node in enumerate(__SCREAMING_SNAKE_CASE):
                   # Remove or replace all references to removed node.
                   if update_node.level > i and update_node.forward[i].key == key:
                       if node.level > i:
                           __a		 =       node.forward[i]
                       else:
                           __a		 =       update_node.forward[:i]



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  KT  ,	__SCREAMING_SNAKE_CASE							:  VT):


           '''simple docstring'''

           __a ,     __a		 =       self._locate_node(__SCREAMING_SNAKE_CASE)
           if node is not None:
               __a		 =       value
           else:
               __a		 =       self.random_level()

               if level > self.level:
                   # After level increase we have to add additional nodes to head.
                   for _ in range(self.level - 1  ,	__SCREAMING_SNAKE_CASE):
                       update_vector.append(self.head)
                   __a		 =       level

               __a		 =       Node(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)

               for i, update_node in enumerate(update_vector[:level]):
                   # Change references to pass through new node.
                   if update_node.level > i:
                       new_node.forward.append(update_node.forward[i])

                   if update_node.level < i + 1:
                       update_node.forward.append(__SCREAMING_SNAKE_CASE)
                   else:
                       __a		 =       new_node





       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  VT):


           '''simple docstring'''

           __a ,     __a		 =       self._locate_node(__SCREAMING_SNAKE_CASE)

           if node is not None:
               return node.value

           return None





def   __snake_case			(    ):
    __a		 =       SkipList()
    skip_list.insert('''Key1'''     ,				3       )
    skip_list.insert('''Key2'''     ,				12       )
    skip_list.insert('''Key3'''     ,				41       )
    skip_list.insert('''Key4'''     ,				-19       )

    __a		 =       skip_list.head
    __a		 =       {}
    while node.level != 0:
        __a		 =       node.forward[0]
        __a		 =       node.value

    assert len(_UpperCAmelCase       ) == 4
    assert all_values["Key1"] == 3
    assert all_values["Key2"] == 12
    assert all_values["Key3"] == 41
    assert all_values["Key4"] == -19





def   __snake_case			(    ):
    __a		 =       SkipList()
    skip_list.insert('''Key1'''     ,				10       )
    skip_list.insert('''Key1'''     ,				12       )

    skip_list.insert('''Key5'''     ,				7       )
    skip_list.insert('''Key7'''     ,				10       )
    skip_list.insert('''Key10'''     ,				5       )

    skip_list.insert('''Key7'''     ,				7       )
    skip_list.insert('''Key5'''     ,				5       )
    skip_list.insert('''Key10'''     ,				10       )

    __a		 =       skip_list.head
    __a		 =       {}
    while node.level != 0:
        __a		 =       node.forward[0]
        __a		 =       node.value

    if len(_UpperCAmelCase       ) != 4:
        print()
    assert len(_UpperCAmelCase       ) == 4
    assert all_values["Key1"] == 12
    assert all_values["Key7"] == 7
    assert all_values["Key5"] == 5
    assert all_values["Key10"] == 10





def   __snake_case			(    ):
    __a		 =       SkipList()
    assert skip_list.find('''Some key'''       ) is None





def   __snake_case			(    ):
    __a		 =       SkipList()

    skip_list.insert('''Key2'''     ,				20       )
    assert skip_list.find('''Key2'''       ) == 20

    skip_list.insert('''Some Key'''     ,				10       )
    skip_list.insert('''Key2'''     ,				8       )
    skip_list.insert('''V'''     ,				13       )

    assert skip_list.find('''Y'''       ) is None
    assert skip_list.find('''Key2'''       ) == 8
    assert skip_list.find('''Some Key'''       ) == 10
    assert skip_list.find('''V'''       ) == 13





def   __snake_case			(    ):
    __a		 =       SkipList()
    skip_list.delete('''Some key'''       )

    assert len(skip_list.head.forward       ) == 0





def   __snake_case			(    ):
    __a		 =       SkipList()

    skip_list.insert('''Key1'''     ,				12       )
    skip_list.insert('''V'''     ,				13       )
    skip_list.insert('''X'''     ,				14       )
    skip_list.insert('''Key2'''     ,				15       )

    skip_list.delete('''V'''       )
    skip_list.delete('''Key2'''       )

    assert skip_list.find('''V'''       ) is None
    assert skip_list.find('''Key2'''       ) is None





def   __snake_case			(    ):
    __a		 =       SkipList()

    skip_list.insert('''Key1'''     ,				12       )
    skip_list.insert('''V'''     ,				13       )
    skip_list.insert('''X'''     ,				14       )
    skip_list.insert('''Key2'''     ,				15       )

    skip_list.delete('''V'''       )
    assert skip_list.find('''V'''       ) is None
    assert skip_list.find('''X'''       ) == 14
    assert skip_list.find('''Key1'''       ) == 12
    assert skip_list.find('''Key2'''       ) == 15

    skip_list.delete('''X'''       )
    assert skip_list.find('''V'''       ) is None
    assert skip_list.find('''X'''       ) is None
    assert skip_list.find('''Key1'''       ) == 12
    assert skip_list.find('''Key2'''       ) == 15

    skip_list.delete('''Key1'''       )
    assert skip_list.find('''V'''       ) is None
    assert skip_list.find('''X'''       ) is None
    assert skip_list.find('''Key1'''       ) is None
    assert skip_list.find('''Key2'''       ) == 15

    skip_list.delete('''Key2'''       )
    assert skip_list.find('''V'''       ) is None
    assert skip_list.find('''X'''       ) is None
    assert skip_list.find('''Key1'''       ) is None
    assert skip_list.find('''Key2'''       ) is None





def   __snake_case			(    ):
    __a		 =       SkipList()

    skip_list.insert('''Key1'''     ,				12       )
    skip_list.insert('''V'''     ,				13       )
    skip_list.insert('''X'''     ,				142       )
    skip_list.insert('''Key2'''     ,				15       )

    skip_list.delete('''X'''       )

    def traverse_keys(_UpperCAmelCase       ):
        yield node.key
        for forward_node in node.forward:
            yield from traverse_keys(_UpperCAmelCase       )

    assert len(set(traverse_keys(skip_list.head       )       )       ) == 4





def   __snake_case			(    ):
    def is_sorted(_UpperCAmelCase       ):
        return all(next_item >= item for item, next_item in zip(_UpperCAmelCase     ,				lst[1:]       )       )

    __a		 =       SkipList()
    for i in range(10       ):
        skip_list.insert(_UpperCAmelCase     ,				_UpperCAmelCase       )
    assert is_sorted(list(_UpperCAmelCase       )       )
    skip_list.delete(5       )
    skip_list.delete(8       )
    skip_list.delete(2       )
    assert is_sorted(list(_UpperCAmelCase       )       )
    skip_list.insert(-12     ,				-12       )
    skip_list.insert(77     ,				77       )
    assert is_sorted(list(_UpperCAmelCase       )       )





def   __snake_case			(    ):
    for _ in range(100       ):
        # Repeat test 100 times due to the probabilistic nature of skip list
        # random values == random bugs
        test_insert()
        test_insert_overrides_existing_value()

        test_searching_empty_list_returns_none()
        test_search()

        test_deleting_item_from_empty_list_do_nothing()
        test_deleted_items_are_not_founded_by_find_method()
        test_delete_removes_only_given_key()
        test_delete_doesnt_leave_dead_nodes()

        test_iter_always_yields_sorted_values()





def   __snake_case			(    ):
    __a		 =       SkipList()
    skip_list.insert(2     ,				'''2'''       )
    skip_list.insert(4     ,				'''4'''       )
    skip_list.insert(6     ,				'''4'''       )
    skip_list.insert(4     ,				'''5'''       )
    skip_list.insert(8     ,				'''4'''       )
    skip_list.insert(9     ,				'''4'''       )

    skip_list.delete(4       )

    print(_UpperCAmelCase       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





