



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()
_lowerCAmelCase	:   Union[str, Any]    =   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase	:   Tuple    =   []
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     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  state_dict.pop(A__					)
				lowerCAmelCase__		  =  val







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[int]:



				"""simple docstring"""





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

				return new_state_dict







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__=False					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  ''
				if is_panoptic:
								lowerCAmelCase__		  =  '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)
								lowerCAmelCase__		  =  state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'					)
								lowerCAmelCase__		  =  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
								lowerCAmelCase__		  =  in_proj_weight[:256, :]
								lowerCAmelCase__		  =  in_proj_bias[:256]
								lowerCAmelCase__		  =  in_proj_weight[256:512, :]
								lowerCAmelCase__		  =  in_proj_bias[256:512]
								lowerCAmelCase__		  =  in_proj_weight[-256:, :]
								lowerCAmelCase__		  =  in_proj_bias[-256:]







def     UpperCAmelCase_		(					)	->     str:



				"""simple docstring"""





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

				return im







@torch.no_grad()
def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





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

				# load image processor
				lowerCAmelCase__		  =  'coco_panoptic' if is_panoptic else 'coco_detection'
				lowerCAmelCase__		  =  ConditionalDetrImageProcessor(format=A__					)

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

				logger.info(f'Converting model {model_name}...'					)

				# load original model from torch hub
				lowerCAmelCase__		  =  torch.hub.load('DeppMeng/ConditionalDETR'    ,			A__    ,			pretrained=A__					).eval()
				lowerCAmelCase__		  =  conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												lowerCAmelCase__		  =  'conditional_detr.' + src
								rename_key(A__    ,			A__    ,			A__					)
				lowerCAmelCase__		  =  rename_backbone_keys(A__					)
				# query, key and value matrices need special treatment
				read_in_q_k_v(A__    ,			is_panoptic=A__					)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				lowerCAmelCase__		  =  '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'					)
												):
																lowerCAmelCase__		  =  state_dict.pop(A__					)
																lowerCAmelCase__		  =  val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																lowerCAmelCase__		  =  state_dict.pop(A__					)
																lowerCAmelCase__		  =  val
												elif key.startswith('bbox_attention'					) or key.startswith('mask_head'					):
																continue
												else:
																lowerCAmelCase__		  =  state_dict.pop(A__					)
																lowerCAmelCase__		  =  val
								else:
												if not key.startswith('class_labels_classifier'					) and not key.startswith('bbox_predictor'					):
																lowerCAmelCase__		  =  state_dict.pop(A__					)
																lowerCAmelCase__		  =  val
    # finally, create HuggingFace model and load state dict
				lowerCAmelCase__		  =  ConditionalDetrForSegmentation(A__					) if is_panoptic else ConditionalDetrForObjectDetection(A__					)
				model.load_state_dict(A__					)
				model.eval()
				model.push_to_hub(repo_id=A__    ,			organization='DepuMeng'    ,			commit_message='Add model'					)
				# verify our conversion
				lowerCAmelCase__		  =  conditional_detr(A__					)
				lowerCAmelCase__		  =  model(A__					)
				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(A__					).mkdir(exist_ok=A__					)
				model.save_pretrained(A__					)
				image_processor.save_pretrained(A__					)


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   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."
	)
	_lowerCAmelCase	:   Optional[Any]    =   parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					''
			for i in table:
						res += inp[i - 1]
			return res






def 						a__			(				A__				):
			return data[1:] + data[0]






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					''
			for i in range(len(A__				)				):
						if a[i] == b[i]:
									res += "0"
						else:
									res += "1"
			return res






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Tuple					=					int('0b' + data[0] + data[-1],      2				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					int('0b' + data[1:3],      2				)
			return bin(s[row][col]				)[2:]






def 						a__			(				A__,      A__,      A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					message[:4]
			SCREAMING_SNAKE_CASE_		:     int					=					message[4:]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					apply_table(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					xor(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					apply_sbox(A__,      temp[:4]				)  # noqa: E741
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					apply_sbox(A__,      temp[4:]				)
			SCREAMING_SNAKE_CASE_		:     Any					=					'0' * (2 - len(A__				)) + l  # noqa: E741
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					'0' * (2 - len(A__				)) + r
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					apply_table(l + r,      A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					xor(A__,      A__				)
			return temp + right


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[Any]								=input('Enter 10 bit key: ')
				lowerCAmelCase__			:      int								=input('Enter 8 bit message: ')

				lowerCAmelCase__			:      Any								=[6, 3, 7, 4, 8, 5, 10, 9]
				lowerCAmelCase__			:      List[str]								=[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
				lowerCAmelCase__			:      Dict								=[2, 4, 3, 1]
				lowerCAmelCase__			:      Dict								=[2, 6, 3, 1, 4, 8, 5, 7]
				lowerCAmelCase__			:      Union[str, Any]								=[4, 1, 3, 5, 7, 2, 8, 6]
				lowerCAmelCase__			:      Dict								=[4, 1, 2, 3, 2, 3, 4, 1]
				lowerCAmelCase__			:      str								=[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
				lowerCAmelCase__			:      Any								=[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

				# key generation
				lowerCAmelCase__			:      Dict								=apply_table(key, paa_table)
				lowerCAmelCase__			:      List[str]								=temp[:5]
				lowerCAmelCase__			:      List[Any]								=temp[5:]
				lowerCAmelCase__			:      Tuple								=left_shift(left)
				lowerCAmelCase__			:      Optional[Any]								=left_shift(right)
				lowerCAmelCase__			:      Optional[int]								=apply_table(left + right, pa_table)
				lowerCAmelCase__			:      Optional[Any]								=left_shift(left)
				lowerCAmelCase__			:      Optional[Any]								=left_shift(right)
				lowerCAmelCase__			:      List[str]								=left_shift(left)
				lowerCAmelCase__			:      Optional[int]								=left_shift(right)
				lowerCAmelCase__			:      Tuple								=apply_table(left + right, pa_table)

				# encryption
				lowerCAmelCase__			:      Tuple								=apply_table(message, IP)
				lowerCAmelCase__			:      List[Any]								=function(expansion, sa, sa, keya, temp)
				lowerCAmelCase__			:      str								=temp[4:] + temp[:4]
				lowerCAmelCase__			:      Optional[Any]								=function(expansion, sa, sa, keya, temp)
				lowerCAmelCase__			:      Optional[int]								=apply_table(temp, IP_inv)
				print('Cipher text is:', CT)

				# decryption
				lowerCAmelCase__			:      Union[str, Any]								=apply_table(CT, IP)
				lowerCAmelCase__			:      Tuple								=function(expansion, sa, sa, keya, temp)
				lowerCAmelCase__			:      Union[str, Any]								=temp[4:] + temp[:4]
				lowerCAmelCase__			:      Dict								=function(expansion, sa, sa, keya, temp)
				lowerCAmelCase__			:      int								=apply_table(temp, IP_inv)
				print('Plain text after decypting is:', PT)





