
'''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()
SCREAMING_SNAKE_CASE__     :    str      							=					logging.get_logger(__name__)




def 				__magic_name__				(     __lowerCAmelCase			:		Tuple							)    ->     int:
				if "backbone.0.body" in orig_key:
								__lowerCamelCase					      =			orig_key.replace('''backbone.0.body'''	,						'''backbone.conv_encoder.model'''							)
				if "transformer" in orig_key:
								__lowerCamelCase					      =			orig_key.replace('''transformer.'''	,						''''''							)
				if "norm1" in orig_key:
								if "encoder" in orig_key:
												__lowerCamelCase					      =			orig_key.replace('''norm1'''	,						'''self_attn_layer_norm'''							)
								else:
												__lowerCamelCase					      =			orig_key.replace('''norm1'''	,						'''encoder_attn_layer_norm'''							)
				if "norm2" in orig_key:
								if "encoder" in orig_key:
												__lowerCamelCase					      =			orig_key.replace('''norm2'''	,						'''final_layer_norm'''							)
								else:
												__lowerCamelCase					      =			orig_key.replace('''norm2'''	,						'''self_attn_layer_norm'''							)
				if "norm3" in orig_key:
								__lowerCamelCase					      =			orig_key.replace('''norm3'''	,						'''final_layer_norm'''							)
				if "linear1" in orig_key:
								__lowerCamelCase					      =			orig_key.replace('''linear1'''	,						'''fc1'''							)
				if "linear2" in orig_key:
								__lowerCamelCase					      =			orig_key.replace('''linear2'''	,						'''fc2'''							)
				if "query_embed" in orig_key:
								__lowerCamelCase					      =			orig_key.replace('''query_embed'''	,						'''query_position_embeddings'''							)
				if "cross_attn" in orig_key:
								__lowerCamelCase					      =			orig_key.replace('''cross_attn'''	,						'''encoder_attn'''							)

				return orig_key




def 				__magic_name__				(     __lowerCAmelCase			:		Optional[int]							)    ->     Tuple:
				# transformer decoder self-attention layers
				for i in range(6							):
								# read in weights + bias of input projection layer of self-attention
								__lowerCamelCase					      =			state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''							)
								__lowerCamelCase					      =			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
								__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 				__magic_name__				(     )    ->     str:
				__lowerCamelCase					      =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				__lowerCamelCase					      =			Image.open(requests.get(__lowerCAmelCase	,						stream=__lowerCAmelCase							).raw							)

				return im




@torch.no_grad()
def 				__magic_name__				(     __lowerCAmelCase			:		List[str]	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		str	,						__lowerCAmelCase			:		int	,						)    ->     Optional[Any]:
				__lowerCamelCase					      =			DeformableDetrConfig()
				# set config attributes
				if single_scale:
								__lowerCamelCase					      =			1
				__lowerCamelCase					      =			dilation
				__lowerCamelCase					      =			with_box_refine
				__lowerCamelCase					      =			two_stage
				# set labels
				__lowerCamelCase					      =			91
				__lowerCamelCase					      =			"huggingface/label-files"
				__lowerCamelCase					      =			"coco-detection-id2label.json"
				__lowerCamelCase					      =			json.load(open(cached_download(hf_hub_url(__lowerCAmelCase	,						__lowerCAmelCase	,						repo_type='''dataset'''							)							)	,						'''r'''							)							)
				__lowerCamelCase					      =			{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
				__lowerCamelCase					      =			idalabel
				__lowerCamelCase					      =			{v: k for k, v in idalabel.items()}

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

				# prepare image
				__lowerCamelCase					      =			prepare_img()
				__lowerCamelCase					      =			image_processor(images=__lowerCAmelCase	,						return_tensors='''pt'''							)
				__lowerCamelCase					      =			encoding["pixel_values"]

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

				# load original state dict
				__lowerCamelCase					      =			torch.load(__lowerCAmelCase	,						map_location='''cpu'''							)["model"]
				# rename keys
				for key in state_dict.copy().keys():
								__lowerCamelCase					      =			state_dict.pop(__lowerCAmelCase							)
								__lowerCamelCase					      =			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
				__lowerCamelCase					      =			"model."
				for key in state_dict.copy().keys():
								if not key.startswith('''class_embed'''							) and not key.startswith('''bbox_embed'''							):
												__lowerCamelCase					      =			state_dict.pop(__lowerCAmelCase							)
												__lowerCamelCase					      =			val
								# finally, create HuggingFace model and load state dict
				__lowerCamelCase					      =			DeformableDetrForObjectDetection(__lowerCAmelCase							)
				model.load_state_dict(__lowerCAmelCase							)
				model.eval()

				__lowerCamelCase					      =			"cuda" if torch.cuda.is_available() else "cpu"
				model.to(__lowerCAmelCase							)
				# verify our conversion
				__lowerCamelCase					      =			model(pixel_values.to(__lowerCAmelCase							)							)

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

				if single_scale:
								__lowerCamelCase					      =			torch.tensor(
								    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]							)
								__lowerCamelCase					      =			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:
								__lowerCamelCase					      =			torch.tensor(
								    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]							)
								__lowerCamelCase					      =			torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]							)

				if with_box_refine:
								__lowerCamelCase					      =			torch.tensor(
								    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]							)
								__lowerCamelCase					      =			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:
								__lowerCamelCase					      =			torch.tensor(
								    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]							)
								__lowerCamelCase					      =			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:
								__lowerCamelCase					      =			"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__":
					SCREAMING_SNAKE_CASE__     :    Any      							=					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."
					)
					SCREAMING_SNAKE_CASE__     :    Union[str, 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,
					)





import bsa
import requests




def 				__magic_name__				(     __lowerCAmelCase			:		bsa.element.ResultSet							)    ->     dict[str, str]:
				return {
				    "name": soup.ha.a.text,
				    "genre": soup.find('''span'''	,						class_='''genre'''							).text.strip(),
				    "rating": soup.strong.text,
				    "page_link": f'''https://www.imdb.com{soup.a.get('href'							)}''',
				}




def 				__magic_name__				(     __lowerCAmelCase			:		int = 5							)    ->     tuple:
				__lowerCamelCase					      =			int(float(__lowerCAmelCase							)							)
				if num_movies < 1:
								return ()
				__lowerCamelCase					      =			(
				    '''https://www.imdb.com/search/title?title_type='''
				    f'''feature&sort=num_votes,desc&count={num_movies}'''
				)
				__lowerCamelCase					      =			bsa.BeautifulSoup(requests.get(__lowerCAmelCase							).content	,						'''html.parser'''							)
				return tuple(
				    get_movie_data_from_soup(__lowerCAmelCase							)
				    for movie in source.find_all('''div'''	,						class_='''lister-item mode-advanced'''							)							)


if __name__ == "__main__":
					import json

					SCREAMING_SNAKE_CASE__     :    str      							=					int(input("How many movies would you like to see? "))
					print(
					    ", ".join(
					        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
					    )
					)



