





'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__						:      Any	=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						:      Optional[Any]	=    []
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
   )
   rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
   rename_keys.append(
       (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
   )
   rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
   rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
   )
   rename_keys.append(
       (
           F"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
           F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
       )
   )
   rename_keys.append(
       (
           F"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
           F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
       )
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

   # q, k, v projections in self/cross-attention in decoder for conditional DETR
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
   )

   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
   )
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
   )
   rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
   rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
   rename_keys.append(
       (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
   )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)







def  a__    (    lowercase :					Union[str, Any], lowercase :					Any, lowercase :					Any				)		->		List[Any]:





     """simple docstring"""

     _UpperCamelCase       =	state_dict.pop(lowercase				)
     _UpperCamelCase       =	val







def  a__    (    lowercase :					List[str]				)		->		Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       =	OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               _UpperCamelCase       =	key.replace('''backbone.0.body''', '''backbone.conv_encoder.model'''				)
               _UpperCamelCase       =	value
          else:
               _UpperCamelCase       =	value

     return new_state_dict







def  a__    (    lowercase :					List[str], lowercase :					Dict=False				)		->		Dict:





     """simple docstring"""

     _UpperCamelCase       =	''''''
     if is_panoptic:
          _UpperCamelCase       =	'''conditional_detr.'''

     # first: transformer encoder
     for i in range(6				):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""				)
          # next, add query, keys and values (in that order) to the state dict
          _UpperCamelCase       =	in_proj_weight[:256, :]
          _UpperCamelCase       =	in_proj_bias[:256]
          _UpperCamelCase       =	in_proj_weight[256:512, :]
          _UpperCamelCase       =	in_proj_bias[256:512]
          _UpperCamelCase       =	in_proj_weight[-256:, :]
          _UpperCamelCase       =	in_proj_bias[-256:]







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

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

     return im







@torch.no_grad()
def  a__    (    lowercase :					Optional[Any], lowercase :					Dict				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase       =	ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          _UpperCamelCase       =	'''resnet101'''
     if "dc5" in model_name:
          _UpperCamelCase       =	True
     _UpperCamelCase       =	'''panoptic''' in model_name
     if is_panoptic:
          _UpperCamelCase       =	250
     else:
          _UpperCamelCase       =	91
          _UpperCamelCase       =	'''huggingface/label-files'''
          _UpperCamelCase       =	'''coco-detection-id2label.json'''
          _UpperCamelCase       =	json.load(open(hf_hub_download(lowercase, lowercase, repo_type='''dataset'''				), '''r'''				)				)
          _UpperCamelCase       =	{int(lowercase				): v for k, v in idalabel.items()}
          _UpperCamelCase       =	idalabel
          _UpperCamelCase       =	{v: k for k, v in idalabel.items()}

     # load image processor
     _UpperCamelCase       =	'''coco_panoptic''' if is_panoptic else '''coco_detection'''
     _UpperCamelCase       =	ConditionalDetrImageProcessor(format=lowercase				)

     # prepare image
     _UpperCamelCase       =	prepare_img()
     _UpperCamelCase       =	image_processor(images=lowercase, return_tensors='''pt'''				)
     _UpperCamelCase       =	encoding['''pixel_values''']

     logger.info(F"""Converting model {model_name}..."""				)

     # load original model from torch hub
     _UpperCamelCase       =	torch.hub.load('''DeppMeng/ConditionalDETR''', lowercase, pretrained=lowercase				).eval()
     _UpperCamelCase       =	conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               _UpperCamelCase       =	'''conditional_detr.''' + src
          rename_key(lowercase, lowercase, lowercase				)
     _UpperCamelCase       =	rename_backbone_keys(lowercase				)
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowercase, is_panoptic=lowercase				)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     _UpperCamelCase       =	'''conditional_detr.model.''' if is_panoptic else '''model.'''
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith('''conditional_detr'''				)
                   and not key.startswith('''class_labels_classifier'''				)
                   and not key.startswith('''bbox_predictor'''				)
               ):
                    _UpperCamelCase       =	state_dict.pop(lowercase				)
                    _UpperCamelCase       =	val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    _UpperCamelCase       =	state_dict.pop(lowercase				)
                    _UpperCamelCase       =	val
               elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
                    continue
               else:
                    _UpperCamelCase       =	state_dict.pop(lowercase				)
                    _UpperCamelCase       =	val
          else:
               if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
                    _UpperCamelCase       =	state_dict.pop(lowercase				)
                    _UpperCamelCase       =	val
    # finally, create HuggingFace model and load state dict
     _UpperCamelCase       =	ConditionalDetrForSegmentation(lowercase				) if is_panoptic else ConditionalDetrForObjectDetection(lowercase				)
     model.load_state_dict(lowercase				)
     model.eval()
     model.push_to_hub(repo_id=lowercase, organization='''DepuMeng''', commit_message='''Add model'''				)
     # verify our conversion
     _UpperCamelCase       =	conditional_detr(lowercase				)
     _UpperCamelCase       =	model(lowercase				)
     assert torch.allclose(outputs.logits, original_outputs['''pred_logits'''], atol=1e-4				)
     assert torch.allclose(outputs.pred_boxes, original_outputs['''pred_boxes'''], atol=1e-4				)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks, original_outputs['''pred_masks'''], atol=1e-4				)

     # 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				)


if __name__ == "__main__":
   lowercase__						:      List[str]	=    argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       default='conditional_detr_resnet50',
       type=str,
       help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   lowercase__						:      List[str]	=    parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''

import random
class     __lowerCAmelCase					:






       """simple docstring"""





       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		str					)   ->					tuple[list[int], list[int]]:

            '''simple docstring'''




            _UpperCamelCase       =	[ord(lowerCAmelCase__					) for i in text]
            _UpperCamelCase       =	[]
            _UpperCamelCase       =	[]
            for i in plain:
                 _UpperCamelCase       =	random.randint(1	,			300					)
                 _UpperCamelCase       =	(i + k) * k
                 cipher.append(lowerCAmelCase__					)
                 key.append(lowerCAmelCase__					)
            return cipher, key







       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		list[int]	,			lowerCAmelCase__       :		list[int]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	[]
            for i in range(len(lowerCAmelCase__					)					):
                 _UpperCamelCase       =	int((cipher[i] - (key[i]) ** 2) / key[i]					)
                 plain.append(chr(lowerCAmelCase__					)					)
            return "".join(lowerCAmelCase__					)


if __name__ == "__main__":
   lowercase__	,       lowercase__						:      List[str]	=    Onepad().encrypt('Hello')
   print(c, k)
   print(Onepad().decrypt(c, k))



