




'''simple docstring'''

import random




def    snake_case__     (  a	)			->       Optional[Any]:





			'''simple docstring'''






			def is_sorted(a	):
						for i in range(len(a	) - 1	):
									if collection[i] > collection[i + 1]:
												return False
						return True

			while not is_sorted(a	):
						random.shuffle(a	)
			return collection


if __name__ == "__main__":
		a__         =     input('''Enter numbers separated by a comma:\n''').strip()
		a__         =     [int(item) for item in user_input.split(''',''')]
		print(bogo_sort(unsorted))

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()
__a							  =						logging.get_logger(__name__)

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

		return orig_key

def    UpperCamelCase_ (	a_		)     ->Dict:
		# transformer decoder self-attention layers
		for i in range(6		):
				# read in weights + bias of input projection layer of self-attention
				A				=state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''		)
				A				=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
				A				=in_proj_weight[:256, :]
				A				=in_proj_bias[:256]
				A				=in_proj_weight[256:512, :]
				A				=in_proj_bias[256:512]
				A				=in_proj_weight[-256:, :]
				A				=in_proj_bias[-256:]

def    UpperCamelCase_ (	)     ->Any:
		A				="http://images.cocodataset.org/val2017/000000039769.jpg"
		A				=Image.open(requests.get(a_  ,				stream=a_		).raw		)

		return im

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

		# load image processor
		A				=DeformableDetrImageProcessor(format="coco_detection"		)

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

		logger.info("Converting model..."		)

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

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

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

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

		if with_box_refine:
				A				=torch.tensor(
				    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]		)
				A				=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:
				A				=torch.tensor(
				    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]		)
				A				=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(a_		)  ,				atol=1E-4		)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,				expected_boxes.to(a_		)  ,				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(a_		).mkdir(exist_ok=a_		)
		model.save_pretrained(a_		)
		image_processor.save_pretrained(a_		)

		# Push to hub
		if push_to_hub:
				A				="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=a_  ,				organization="nielsr"  ,				commit_message="Add model"		)


if __name__ == "__main__":
	__a							  =						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."""
	)
	__a							  =						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,
	)




