




'''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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase				:		List[str]				      =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase				:		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}.multihead_attn.out_proj.weight''',
						        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
						    )
						)
						rename_keys.append(
						    (
						        F'''transformer.decoder.layers.{i}.multihead_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'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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'),
    ]
)






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			state_dict.pop(UpperCamelCase_    )
		_snake_case      			=			val






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







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

		return new_state_dict






def 						_lowercase						(      lowercase_		,				lowercase_=False    ):



		'''simple docstring'''







		_snake_case      			=			""
		if is_panoptic:
				_snake_case      			=			"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)
				_snake_case      			=			state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''    )
				_snake_case      			=			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
				_snake_case      			=			in_proj_weight[:256, :]
				_snake_case      			=			in_proj_bias[:256]
				_snake_case      			=			in_proj_weight[256:512, :]
				_snake_case      			=			in_proj_bias[256:512]
				_snake_case      			=			in_proj_weight[-256:, :]
				_snake_case      			=			in_proj_bias[-256:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6    ):
				# read in weights + bias of input projection layer of self-attention
				_snake_case      			=			state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''    )
				_snake_case      			=			state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''    )
				# next, add query, keys and values (in that order) to the state dict
				_snake_case      			=			in_proj_weight[:256, :]
				_snake_case      			=			in_proj_bias[:256]
				_snake_case      			=			in_proj_weight[256:512, :]
				_snake_case      			=			in_proj_bias[256:512]
				_snake_case      			=			in_proj_weight[-256:, :]
				_snake_case      			=			in_proj_bias[-256:]
				# read in weights + bias of input projection layer of cross-attention
				_snake_case      			=			state_dict.pop(
				    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''    )
				_snake_case      			=			state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''    )
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				_snake_case      			=			in_proj_weight_cross_attn[:256, :]
				_snake_case      			=			in_proj_bias_cross_attn[:256]
				_snake_case      			=			in_proj_weight_cross_attn[256:512, :]
				_snake_case      			=			in_proj_bias_cross_attn[256:512]
				_snake_case      			=			in_proj_weight_cross_attn[-256:, :]
				_snake_case      			=			in_proj_bias_cross_attn[-256:]






def 						_lowercase						(      ):



		'''simple docstring'''







		_snake_case      			=			"http://images.cocodataset.org/val2017/000000039769.jpg"
		_snake_case      			=			Image.open(requests.get(UpperCamelCase_		,				stream=UpperCamelCase_    ).raw    )

		return im






@torch.no_grad()
def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







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

		# load image processor
		_snake_case      			=			"coco_panoptic" if is_panoptic else "coco_detection"
		_snake_case      			=			DetrImageProcessor(format=UpperCamelCase_    )

		# prepare image
		_snake_case      			=			prepare_img()
		_snake_case      			=			image_processor(images=UpperCamelCase_		,				return_tensors="pt"    )
		_snake_case      			=			encoding["pixel_values"]

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

		# load original model from torch hub
		_snake_case      			=			torch.hub.load("facebookresearch/detr"		,				UpperCamelCase_		,				pretrained=UpperCamelCase_    ).eval()
		_snake_case      			=			detr.state_dict()
		# rename keys
		for src, dest in rename_keys:
				if is_panoptic:
						_snake_case      			=			"detr." + src
				rename_key(UpperCamelCase_		,				UpperCamelCase_		,				UpperCamelCase_    )
		_snake_case      			=			rename_backbone_keys(UpperCamelCase_    )
		# query, key and value matrices need special treatment
		read_in_q_k_v(UpperCamelCase_		,				is_panoptic=UpperCamelCase_    )
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		_snake_case      			=			"detr.model." if is_panoptic else "model."
		for key in state_dict.copy().keys():
				if is_panoptic:
						if (
						    key.startswith("detr"    )
						    and not key.startswith("class_labels_classifier"    )
						    and not key.startswith("bbox_predictor"    )
						):
								_snake_case      			=			state_dict.pop(UpperCamelCase_    )
								_snake_case      			=			val
						elif "class_labels_classifier" in key or "bbox_predictor" in key:
								_snake_case      			=			state_dict.pop(UpperCamelCase_    )
								_snake_case      			=			val
						elif key.startswith("bbox_attention"    ) or key.startswith("mask_head"    ):
								continue
						else:
								_snake_case      			=			state_dict.pop(UpperCamelCase_    )
								_snake_case      			=			val
				else:
						if not key.startswith("class_labels_classifier"    ) and not key.startswith("bbox_predictor"    ):
								_snake_case      			=			state_dict.pop(UpperCamelCase_    )
								_snake_case      			=			val
    # finally, create HuggingFace model and load state dict
		_snake_case      			=			DetrForSegmentation(UpperCamelCase_    ) if is_panoptic else DetrForObjectDetection(UpperCamelCase_    )
		model.load_state_dict(UpperCamelCase_    )
		model.eval()
		# verify our conversion
		_snake_case      			=			detr(UpperCamelCase_    )
		_snake_case      			=			model(UpperCamelCase_    )
		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(UpperCamelCase_    ).mkdir(exist_ok=UpperCamelCase_    )
		model.save_pretrained(UpperCamelCase_    )
		image_processor.save_pretrained(UpperCamelCase_    )


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

						parser.add_argument(
						    '--model_name', default='detr_resnet50', type=str, help='Name of the 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				:		str				      =    parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












from __future__ import annotations




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		int | None:
       for r in range(1       ,		UpperCamelCase_	):
              UpperCamelCase_ 							=	[]
              for x in range(n - 1	):
                     UpperCamelCase_ 							=	pow(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)
                     if val in li:
                            break
                     li.append(UpperCamelCase_	)
              else:
                     return r
       return None


if __name__ == "__main__":
     _UpperCAmelCase										=					int(input('Enter a prime number q: '))
     _UpperCAmelCase										=					find_primitive(q)
     if a is None:
          print(f'''Cannot find the primitive for the value: {a!r}''')
     else:
          _UpperCAmelCase										=					int(input('Enter private key of A: '))
          _UpperCAmelCase										=					pow(a, a_private, q)
          _UpperCAmelCase										=					int(input('Enter private key of B: '))
          _UpperCAmelCase										=					pow(a, b_private, q)

          _UpperCAmelCase										=					pow(b_public, a_private, q)
          _UpperCAmelCase										=					pow(a_public, b_private, q)

          print('The key value generated by A is: ', a_secret)
          print('The key value generated by B is: ', b_secret)

