"""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()
lowerCAmelCase__    :    Optional[int]			=      logging.get_logger(__name__)



def     a_		(   lowerCamelCase			):
				if "backbone.0.body" in orig_key:
								UpperCAmelCase__			 =				orig_key.replace('backbone.0.body'   ,     'backbone.conv_encoder.model'			)
				if "transformer" in orig_key:
								UpperCAmelCase__			 =				orig_key.replace('transformer.'   ,     ''			)
				if "norm1" in orig_key:
								if "encoder" in orig_key:
												UpperCAmelCase__			 =				orig_key.replace('norm1'   ,     'self_attn_layer_norm'			)
								else:
												UpperCAmelCase__			 =				orig_key.replace('norm1'   ,     'encoder_attn_layer_norm'			)
				if "norm2" in orig_key:
								if "encoder" in orig_key:
												UpperCAmelCase__			 =				orig_key.replace('norm2'   ,     'final_layer_norm'			)
								else:
												UpperCAmelCase__			 =				orig_key.replace('norm2'   ,     'self_attn_layer_norm'			)
				if "norm3" in orig_key:
								UpperCAmelCase__			 =				orig_key.replace('norm3'   ,     'final_layer_norm'			)
				if "linear1" in orig_key:
								UpperCAmelCase__			 =				orig_key.replace('linear1'   ,     'fc1'			)
				if "linear2" in orig_key:
								UpperCAmelCase__			 =				orig_key.replace('linear2'   ,     'fc2'			)
				if "query_embed" in orig_key:
								UpperCAmelCase__			 =				orig_key.replace('query_embed'   ,     'query_position_embeddings'			)
				if "cross_attn" in orig_key:
								UpperCAmelCase__			 =				orig_key.replace('cross_attn'   ,     'encoder_attn'			)

				return orig_key



def     a_		(   lowerCamelCase			):
				# transformer decoder self-attention layers
				for i in range(6			):
								# read in weights + bias of input projection layer of self-attention
								UpperCAmelCase__			 =				state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''			)
								UpperCAmelCase__			 =				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
								UpperCAmelCase__			 =				in_proj_weight[:2_5_6, :]
								UpperCAmelCase__			 =				in_proj_bias[:2_5_6]
								UpperCAmelCase__			 =				in_proj_weight[2_5_6:5_1_2, :]
								UpperCAmelCase__			 =				in_proj_bias[2_5_6:5_1_2]
								UpperCAmelCase__			 =				in_proj_weight[-2_5_6:, :]
								UpperCAmelCase__			 =				in_proj_bias[-2_5_6:]



def     a_		(   ):
				UpperCAmelCase__			 =				'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__			 =				Image.open(requests.get(lowerCamelCase   ,     stream=lowerCamelCase			).raw			)

				return im



@torch.no_grad()
def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     ):
				UpperCAmelCase__			 =				DeformableDetrConfig()
				# set config attributes
				if single_scale:
								UpperCAmelCase__			 =				1
				UpperCAmelCase__			 =				dilation
				UpperCAmelCase__			 =				with_box_refine
				UpperCAmelCase__			 =				two_stage
				# set labels
				UpperCAmelCase__			 =				9_1
				UpperCAmelCase__			 =				'huggingface/label-files'
				UpperCAmelCase__			 =				'coco-detection-id2label.json'
				UpperCAmelCase__			 =				json.load(open(cached_download(hf_hub_url(lowerCamelCase   ,     lowerCamelCase   ,     repo_type='dataset'			)			)   ,     'r'			)			)
				UpperCAmelCase__			 =				{int(lowerCamelCase			): v for k, v in idalabel.items()}
				UpperCAmelCase__			 =				idalabel
				UpperCAmelCase__			 =				{v: k for k, v in idalabel.items()}

				# load image processor
				UpperCAmelCase__			 =				DeformableDetrImageProcessor(format='coco_detection'			)

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

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

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

				UpperCAmelCase__			 =				'cuda' if torch.cuda.is_available() else 'cpu'
				model.to(lowerCamelCase			)
				# verify our conversion
				UpperCAmelCase__			 =				model(pixel_values.to(lowerCamelCase			)			)

				UpperCAmelCase__			 =				torch.tensor(
				    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]			)
				UpperCAmelCase__			 =				torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]			)

				if single_scale:
								UpperCAmelCase__			 =				torch.tensor(
								    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]			)
								UpperCAmelCase__			 =				torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]			)

				if single_scale and dilation:
								UpperCAmelCase__			 =				torch.tensor(
								    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]			)
								UpperCAmelCase__			 =				torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]			)

				if with_box_refine:
								UpperCAmelCase__			 =				torch.tensor(
								    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]			)
								UpperCAmelCase__			 =				torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]			)

				if with_box_refine and two_stage:
								UpperCAmelCase__			 =				torch.tensor(
								    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]			)
								UpperCAmelCase__			 =				torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]			)

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

				assert torch.allclose(outputs.logits[0, :3, :3]   ,     expected_logits.to(lowerCamelCase			)   ,     atol=1e-4			)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,     expected_boxes.to(lowerCamelCase			)   ,     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(lowerCamelCase			).mkdir(exist_ok=lowerCamelCase			)
				model.save_pretrained(lowerCamelCase			)
				image_processor.save_pretrained(lowerCamelCase			)

				# Push to hub
				if push_to_hub:
								UpperCAmelCase__			 =				'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=lowerCamelCase   ,     organization='nielsr'   ,     commit_message='Add model'			)


if __name__ == "__main__":
						lowerCAmelCase__    :    Optional[int]			=      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.'
						)
						lowerCAmelCase__    :    Any			=      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"""






import requests

try:
						from django.contrib.auth import authenticate, login
						from django.shortcuts import redirect, render
except ImportError:
						lowerCAmelCase__    :    Union[str, Any]			=      print



def     a_		(   lowerCamelCase			):
				# Enter your recaptcha secret key here
				UpperCAmelCase__			 =				'secretKey'
				UpperCAmelCase__			 =				'https://www.google.com/recaptcha/api/siteverify'

				# when method is not POST, direct user to login page
				if request.method != "POST":
								return render(lowerCamelCase   ,     'login.html'			)

				# from the frontend, get username, password, and client_key
				UpperCAmelCase__			 =				request.POST.get('username'			)
				UpperCAmelCase__			 =				request.POST.get('password'			)
				UpperCAmelCase__			 =				request.POST.get('g-recaptcha-response'			)

				# post recaptcha response to Google's recaptcha api
				UpperCAmelCase__			 =				requests.post(lowerCamelCase   ,     data={'secret': secret_key, 'response': client_key}			)
				# if the recaptcha api verified our keys
				if response.json().get('success'   ,     lowerCamelCase			):
								# authenticate the user
								UpperCAmelCase__			 =				authenticate(lowerCamelCase   ,     username=lowerCamelCase   ,     password=lowerCamelCase			)
								if user_in_database:
												login(lowerCamelCase   ,     lowerCamelCase			)
												return redirect('/your-webpage'			)
				return render(lowerCamelCase   ,     'login.html'			)



