






from __future__ import annotations

from statistics import mean

def 					UpperCamelCase		(   _A				):






						"""simple docstring"""







						__magic_name__    :     Any     		=      2
						__magic_name__    :     Any     		=      list(__a				)
						__magic_name__    :     Optional[Any]     		=      [0] * len(__a				)
						__magic_name__    :     int     		=      0
						while True:
												__magic_name__    :     Union[str, Any]     		=      True
												for i, burst_time in enumerate(__a				):
																		if rem_burst_times[i] > 0:
																								__magic_name__    :     Dict     		=      False
																								if rem_burst_times[i] > quantum:
																														t += quantum
																														rem_burst_times[i] -= quantum
																								else:
																														t += rem_burst_times[i]
																														__magic_name__    :     Optional[int]     		=      t - burst_time
																														__magic_name__    :     int     		=      0
												if done is True:
																		return waiting_times

def 					UpperCamelCase		(   _A,					_A				):






						"""simple docstring"""







						return [burst + waiting for burst, waiting in zip(__a,					__a				)]


if __name__ == "__main__":
		__magic_name__:  List[str]  	=			[3, 5, 7]
		__magic_name__:  Optional[Any]  	=			calculate_waiting_times(burst_times)
		__magic_name__:  Optional[Any]  	=			calculate_turn_around_times(burst_times, waiting_times)
		print("Process ID \tBurst Time \tWaiting Time \tTurnaround Time")
		for i, burst_time in enumerate(burst_times):
				print(
				    F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
				    F"""{turn_around_times[i]}"""
				)
		print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
		print(F"""Average turn around time = {mean(turn_around_times):.5f}""")








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()
__magic_name__:  List[Any]  	=			logging.get_logger(__name__)

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						if "backbone.0.body" in orig_key:
												__magic_name__    :     Optional[int]     		=      orig_key.replace("""backbone.0.body""",					"""backbone.conv_encoder.model"""				)
						if "transformer" in orig_key:
												__magic_name__    :     Tuple     		=      orig_key.replace("""transformer.""",					""""""				)
						if "norm1" in orig_key:
												if "encoder" in orig_key:
																		__magic_name__    :     int     		=      orig_key.replace("""norm1""",					"""self_attn_layer_norm"""				)
												else:
																		__magic_name__    :     List[str]     		=      orig_key.replace("""norm1""",					"""encoder_attn_layer_norm"""				)
						if "norm2" in orig_key:
												if "encoder" in orig_key:
																		__magic_name__    :     str     		=      orig_key.replace("""norm2""",					"""final_layer_norm"""				)
												else:
																		__magic_name__    :     Optional[int]     		=      orig_key.replace("""norm2""",					"""self_attn_layer_norm"""				)
						if "norm3" in orig_key:
												__magic_name__    :     Optional[int]     		=      orig_key.replace("""norm3""",					"""final_layer_norm"""				)
						if "linear1" in orig_key:
												__magic_name__    :     Dict     		=      orig_key.replace("""linear1""",					"""fc1"""				)
						if "linear2" in orig_key:
												__magic_name__    :     Optional[int]     		=      orig_key.replace("""linear2""",					"""fc2"""				)
						if "query_embed" in orig_key:
												__magic_name__    :     Optional[Any]     		=      orig_key.replace("""query_embed""",					"""query_position_embeddings"""				)
						if "cross_attn" in orig_key:
												__magic_name__    :     str     		=      orig_key.replace("""cross_attn""",					"""encoder_attn"""				)

						return orig_key

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						for i in range(6				):
												# read in weights + bias of input projection layer of self-attention
												__magic_name__    :     Tuple     		=      state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'				)
												__magic_name__    :     Dict     		=      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
												__magic_name__    :     Tuple     		=      in_proj_weight[:256, :]
												__magic_name__    :     Union[str, Any]     		=      in_proj_bias[:256]
												__magic_name__    :     Union[str, Any]     		=      in_proj_weight[256:512, :]
												__magic_name__    :     List[Any]     		=      in_proj_bias[256:512]
												__magic_name__    :     Optional[int]     		=      in_proj_weight[-256:, :]
												__magic_name__    :     Dict     		=      in_proj_bias[-256:]

def 					UpperCamelCase		( ):






						"""simple docstring"""







						__magic_name__    :     Union[str, Any]     		=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
						__magic_name__    :     List[Any]     		=      Image.open(requests.get(_A,					stream=_A				).raw				)

						return im

@torch.no_grad()
def 					UpperCamelCase		( _A,					_A,					_A,					_A,					_A,					_A,					_A,					):






						"""simple docstring"""







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

						# load image processor
						__magic_name__    :     Any     		=      DeformableDetrImageProcessor(format="""coco_detection"""				)

						# prepare image
						__magic_name__    :     str     		=      prepare_img()
						__magic_name__    :     List[str]     		=      image_processor(images=_A,					return_tensors="""pt"""				)
						__magic_name__    :     List[Any]     		=      encoding["""pixel_values"""]

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

						# load original state dict
						__magic_name__    :     Optional[int]     		=      torch.load(_A,					map_location="""cpu"""				)["""model"""]
						# rename keys
						for key in state_dict.copy().keys():
												__magic_name__    :     List[str]     		=      state_dict.pop(_A				)
												__magic_name__    :     List[Any]     		=      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
						__magic_name__    :     str     		=      """model."""
						for key in state_dict.copy().keys():
												if not key.startswith("""class_embed"""				) and not key.startswith("""bbox_embed"""				):
																		__magic_name__    :     Tuple     		=      state_dict.pop(_A				)
																		__magic_name__    :     Optional[int]     		=      val
    # finally, create HuggingFace model and load state dict
						__magic_name__    :     str     		=      DeformableDetrForObjectDetection(_A				)
						model.load_state_dict(_A				)
						model.eval()

						__magic_name__    :     Optional[int]     		=      """cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(_A				)
						# verify our conversion
						__magic_name__    :     List[Any]     		=      model(pixel_values.to(_A				)				)

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

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

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