





'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A						    =							logging.get_logger(__name__)

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	List[Any])			->						Any:

     '''simple docstring'''




     if "backbone.0.body" in orig_key:
          _lowercase							:      Dict  						=							orig_key.replace('backbone.0.body'       ,  'backbone.conv_encoder.model')
     if "transformer" in orig_key:
          _lowercase							:      Optional[Any]  						=							orig_key.replace('transformer.'       ,  '')
     if "norm1" in orig_key:
          if "encoder" in orig_key:
               _lowercase							:      Optional[int]  						=							orig_key.replace('norm1'       ,  'self_attn_layer_norm')
          else:
               _lowercase							:      Tuple  						=							orig_key.replace('norm1'       ,  'encoder_attn_layer_norm')
     if "norm2" in orig_key:
          if "encoder" in orig_key:
               _lowercase							:      Dict  						=							orig_key.replace('norm2'       ,  'final_layer_norm')
          else:
               _lowercase							:      Dict  						=							orig_key.replace('norm2'       ,  'self_attn_layer_norm')
     if "norm3" in orig_key:
          _lowercase							:      str  						=							orig_key.replace('norm3'       ,  'final_layer_norm')
     if "linear1" in orig_key:
          _lowercase							:      Any  						=							orig_key.replace('linear1'       ,  'fc1')
     if "linear2" in orig_key:
          _lowercase							:      List[str]  						=							orig_key.replace('linear2'       ,  'fc2')
     if "query_embed" in orig_key:
          _lowercase							:      Dict  						=							orig_key.replace('query_embed'       ,  'query_position_embeddings')
     if "cross_attn" in orig_key:
          _lowercase							:      Dict  						=							orig_key.replace('cross_attn'       ,  'encoder_attn')

     return orig_key

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	Tuple)			->						Union[str, Any]:

     '''simple docstring'''




     for i in range(6):
          # read in weights + bias of input projection layer of self-attention
          _lowercase							:      Any  						=							state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight''')
          _lowercase							:      Any  						=							state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_bias''')
          # next, add query, keys and values (in that order) to the state dict
          _lowercase							:      List[Any]  						=							in_proj_weight[:2_56, :]
          _lowercase							:      List[str]  						=							in_proj_bias[:2_56]
          _lowercase							:      int  						=							in_proj_weight[2_56:5_12, :]
          _lowercase							:      List[str]  						=							in_proj_bias[2_56:5_12]
          _lowercase							:      int  						=							in_proj_weight[-2_56:, :]
          _lowercase							:      int  						=							in_proj_bias[-2_56:]

def 		SCREAMING_SNAKE_CASE      (					)			->						Optional[int]:

     '''simple docstring'''




     _lowercase							:      int  						=							'http://images.cocodataset.org/val2017/000000039769.jpg'
     _lowercase							:      int  						=							Image.open(requests.get(lowercase_       ,  stream=lowercase_).raw)

     return im

@torch.no_grad()
def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	Tuple       ,  lowerCAmelCase__     :	List[Any]       ,  lowerCAmelCase__     :	List[str]       ,  lowerCAmelCase__     :	str       ,  lowerCAmelCase__     :	Optional[int]       ,  lowerCAmelCase__     :	List[str]       ,  lowerCAmelCase__     :	Tuple       ,  )			->						Optional[Any]:

     '''simple docstring'''




     _lowercase							:      List[Any]  						=							DeformableDetrConfig()
     # set config attributes
     if single_scale:
          _lowercase							:      str  						=							1
     _lowercase							:      Union[str, Any]  						=							dilation
     _lowercase							:      Any  						=							with_box_refine
     _lowercase							:      Any  						=							two_stage
     # set labels
     _lowercase							:      Union[str, Any]  						=							91
     _lowercase							:      Tuple  						=							'huggingface/label-files'
     _lowercase							:      List[str]  						=							'coco-detection-id2label.json'
     _lowercase							:      List[Any]  						=							json.load(open(cached_download(hf_hub_url(lowercase_       ,  lowercase_       ,  repo_type='dataset'))       ,  'r'))
     _lowercase							:      Dict  						=							{int(lowercase_): v for k, v in idalabel.items()}
     _lowercase							:      int  						=							idalabel
     _lowercase							:      List[str]  						=							{v: k for k, v in idalabel.items()}

     # load image processor
     _lowercase							:      Any  						=							DeformableDetrImageProcessor(format='coco_detection')

     # prepare image
     _lowercase							:      str  						=							prepare_img()
     _lowercase							:      Optional[Any]  						=							image_processor(images=lowercase_       ,  return_tensors='pt')
     _lowercase							:      Any  						=							encoding['pixel_values']

     logger.info('Converting model...')

     # load original state dict
     _lowercase							:      Union[str, Any]  						=							torch.load(lowercase_       ,  map_location='cpu')['model']
     # rename keys
     for key in state_dict.copy().keys():
          _lowercase							:      Tuple  						=							state_dict.pop(lowercase_)
          _lowercase							:      Union[str, Any]  						=							val
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowercase_)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     _lowercase							:      int  						=							'model.'
     for key in state_dict.copy().keys():
          if not key.startswith('class_embed') and not key.startswith('bbox_embed'):
               _lowercase							:      Dict  						=							state_dict.pop(lowercase_)
               _lowercase							:      Dict  						=							val
    # finally, create HuggingFace model and load state dict
     _lowercase							:      int  						=							DeformableDetrForObjectDetection(lowercase_)
     model.load_state_dict(lowercase_)
     model.eval()

     _lowercase							:      Any  						=							'cuda' if torch.cuda.is_available() else 'cpu'
     model.to(lowercase_)
     # verify our conversion
     _lowercase							:      Dict  						=							model(pixel_values.to(lowercase_))

     _lowercase							:      Optional[Any]  						=							torch.tensor(
         [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]])
     _lowercase							:      Dict  						=							torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]])

     if single_scale:
          _lowercase							:      Optional[int]  						=							torch.tensor(
              [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]])
          _lowercase							:      Any  						=							torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]])

     if single_scale and dilation:
          _lowercase							:      Optional[Any]  						=							torch.tensor(
              [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]])
          _lowercase							:      int  						=							torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]])

     if with_box_refine:
          _lowercase							:      Dict  						=							torch.tensor(
              [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]])
          _lowercase							:      List[str]  						=							torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]])

     if with_box_refine and two_stage:
          _lowercase							:      int  						=							torch.tensor(
              [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]])
          _lowercase							:      Optional[Any]  						=							torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]])

     print('Logits:'       ,  outputs.logits[0, :3, :3])

     assert torch.allclose(outputs.logits[0, :3, :3]       ,  expected_logits.to(lowercase_)       ,  atol=1E-4)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,  expected_boxes.to(lowercase_)       ,  atol=1E-4)

     print('Everything ok!')

     # Save model and image processor
     logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
     Path(lowercase_).mkdir(exist_ok=lowercase_)
     model.save_pretrained(lowercase_)
     image_processor.save_pretrained(lowercase_)

     # Push to hub
     if push_to_hub:
          _lowercase							:      List[str]  						=							'deformable-detr'
          model_name += "-single-scale" if single_scale else ""
          model_name += "-dc5" if dilation else ""
          model_name += "-with-box-refine" if with_box_refine else ""
          model_name += "-two-stage" if two_stage else ""
          print('Pushing model to hub...')
          model.push_to_hub(repo_path_or_name=lowercase_       ,  organization='nielsr'       ,  commit_message='Add model')


if __name__ == "__main__":
    A						    =							argparse.ArgumentParser()

    parser.add_argument(
        '''--checkpoint_path''',
        type=str,
        default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
        help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
    )
    parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
    parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
    parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
    parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default=None,
        type=str,
        required=True,
        help='''Path to the folder to output PyTorch model.''',
    )
    parser.add_argument(
        '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
    )
    A						    =							parser.parse_args()
    convert_deformable_detr_checkpoint(
        args.checkpoint_path,
        args.single_scale,
        args.dilation,
        args.with_box_refine,
        args.two_stage,
        args.pytorch_dump_folder_path,
        args.push_to_hub,
    )






'''simple docstring'''







A						    =							{
    '''A''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
    '''B''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
    '''C''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
    '''D''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
    '''E''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
    '''F''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
    '''G''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
    '''H''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
    '''I''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
    '''J''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
    '''K''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
    '''L''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
    '''M''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
    '''N''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
    '''O''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
    '''P''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
    '''Q''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
    '''R''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
    '''S''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
    '''T''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
    '''U''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
    '''V''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
    '''W''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
    '''X''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
    '''Y''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
    '''Z''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
}

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str)			->						list[tuple[str, str]]:

     '''simple docstring'''




     return [alphabet[char] for char in key.upper()]

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     _lowercase							:      Optional[int]  						=							''
     _lowercase							:      List[str]  						=							0
     _lowercase							:      Optional[Any]  						=							generate_table(lowerCAmelCase__)
     for char in words.upper():
          cipher += get_opponent(table[count]       ,  lowerCAmelCase__)
          _lowercase							:      Dict  						=							(count + 1) % len(lowerCAmelCase__)
     return cipher

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     return encrypt(lowerCAmelCase__       ,  lowerCAmelCase__)

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	tuple[str, str]       ,  lowerCAmelCase__     :	str)			->						tuple[int, int]:

     '''simple docstring'''




     _lowercase							:      Optional[Any]  						=							0 if char in table[0] else 1
     _lowercase							:      Tuple  						=							table[row].index(lowerCAmelCase__)
     return row, col

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	tuple[str, str]       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     _lowercase						, _lowercase							:      str  						=							get_position(lowerCAmelCase__       ,  char.upper())
     if row == 1:
          return table[0][col]
     else:
          return table[1][col] if row == 0 else char


if __name__ == "__main__":
    import doctest

    doctest.testmod()  # Fist ensure that all our tests are passing...
    A						    =							input('''Enter key: ''').strip()
    A						    =							input('''Enter text to encrypt: ''').strip()
    A						    =							encrypt(key, text)

    print(F"""Encrypted: {cipher_text}""")
    print(F"""Decrypted with key: {decrypt(key, cipher_text)}""")