
"""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()
UpperCamelCase__          =	logging.get_logger(__name__)
def 					lowerCamelCase   (     _snake_case    ,_snake_case=False    ,_snake_case=False ):
    UpperCAmelCase__	:      List[str]     							=					'backbone.' if is_semantic else ''

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

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

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

        UpperCAmelCase__	:      Any     							=					gamma_a
        UpperCAmelCase__	:      str     							=					gamma_a
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					dct.pop(_snake_case )
    UpperCAmelCase__	:      Any     							=					val
def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      Optional[Any]     							=					'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase__	:      Any     							=					Image.open(requests.get(_snake_case    ,stream=_snake_case ).raw )
    return im
@torch.no_grad()
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case=False ):
    UpperCAmelCase__	:      Optional[Any]     							=					False if 'rvlcdip' in checkpoint_url else True
    UpperCAmelCase__	:      Dict     							=					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:
        UpperCAmelCase__	:      List[str]     							=					1024
        UpperCAmelCase__	:      Union[str, Any]     							=					4096
        UpperCAmelCase__	:      Union[str, Any]     							=					24
        UpperCAmelCase__	:      List[str]     							=					16

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

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

    UpperCAmelCase__	:      Tuple     							=					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
    UpperCAmelCase__	:      List[str]     							=					BeitForMaskedImageModeling(_snake_case ) if has_lm_head else BeitForImageClassification(_snake_case )
    model.eval()
    model.load_state_dict(_snake_case )

    # Check outputs on an image
    UpperCAmelCase__	:      Optional[int]     							=					BeitImageProcessor(
        size=config.image_size    ,resample=PILImageResampling.BILINEAR    ,do_center_crop=_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					prepare_img()

    UpperCAmelCase__	:      Dict     							=					image_processor(images=_snake_case    ,return_tensors='pt' )
    UpperCAmelCase__	:      Optional[int]     							=					encoding['pixel_values']

    UpperCAmelCase__	:      Optional[Any]     							=					model(_snake_case )
    UpperCAmelCase__	:      int     							=					outputs.logits

    # verify logits
    UpperCAmelCase__	:      Tuple     							=					[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:
            UpperCAmelCase__	:      Tuple     							=					'dit-base' if 'base' in checkpoint_url else 'dit-large'
        else:
            UpperCAmelCase__	:      List[str]     							=					'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__":
    UpperCamelCase__          =	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__          =	parser.parse_args()
    convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






from __future__ import annotations

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:dict	,   _snake_case	:str				)  ->     set[str]:
   _A  ,    _A      =       set(_snake_case				), [start]

   while stack:
      _A      =       stack.pop()
      explored.add(_snake_case				)
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]				):
         if adj not in explored:
            stack.append(_snake_case				)
   return explored


UpperCAmelCase_		 =					{
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(depth_first_search(G, """A"""))



