
"""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()
_a	 =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_a	 =							[]
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 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
   UpperCAmelCase_      :      Tuple      =					state_dict.pop(__lowerCamelCase						)
   UpperCAmelCase_      :      str      =					val







def 			__a  (	__lowerCamelCase						):
   UpperCAmelCase_      :      str      =					OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         UpperCAmelCase_      :      str      =					key.replace("backbone.0.body",	"backbone.conv_encoder.model"						)
         UpperCAmelCase_      :      List[Any]      =					value
      else:
         UpperCAmelCase_      :      List[str]      =					value

   return new_state_dict







def 			__a  (	__lowerCamelCase,	__lowerCamelCase=False						):
   UpperCAmelCase_      :      Union[str, Any]      =					""
   if is_panoptic:
      UpperCAmelCase_      :      Optional[Any]      =					"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)
      UpperCAmelCase_      :      Optional[int]      =					state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
      UpperCAmelCase_      :      Any      =					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
      UpperCAmelCase_      :      Tuple      =					in_proj_weight[:256, :]
      UpperCAmelCase_      :      List[str]      =					in_proj_bias[:256]
      UpperCAmelCase_      :      Any      =					in_proj_weight[256:512, :]
      UpperCAmelCase_      :      Any      =					in_proj_bias[256:512]
      UpperCAmelCase_      :      List[str]      =					in_proj_weight[-256:, :]
      UpperCAmelCase_      :      List[Any]      =					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
      UpperCAmelCase_      :      Union[str, Any]      =					state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""						)
      UpperCAmelCase_      :      Dict      =					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
      UpperCAmelCase_      :      Union[str, Any]      =					in_proj_weight[:256, :]
      UpperCAmelCase_      :      List[str]      =					in_proj_bias[:256]
      UpperCAmelCase_      :      str      =					in_proj_weight[256:512, :]
      UpperCAmelCase_      :      Dict      =					in_proj_bias[256:512]
      UpperCAmelCase_      :      List[str]      =					in_proj_weight[-256:, :]
      UpperCAmelCase_      :      Optional[Any]      =					in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      UpperCAmelCase_      :      str      =					state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""						)
      UpperCAmelCase_      :      List[Any]      =					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
      UpperCAmelCase_      :      int      =					in_proj_weight_cross_attn[:256, :]
      UpperCAmelCase_      :      Optional[int]      =					in_proj_bias_cross_attn[:256]
      UpperCAmelCase_      :      Dict      =					in_proj_weight_cross_attn[256:512, :]
      UpperCAmelCase_      :      Tuple      =					in_proj_bias_cross_attn[256:512]
      UpperCAmelCase_      :      List[str]      =					in_proj_weight_cross_attn[-256:, :]
      UpperCAmelCase_      :      int      =					in_proj_bias_cross_attn[-256:]







def 			__a  (	):
   UpperCAmelCase_      :      Any      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_      :      List[Any]      =					Image.open(requests.get(__lowerCamelCase,	stream=__lowerCamelCase						).raw						)

   return im







@torch.no_grad()
def 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
   UpperCAmelCase_      :      Optional[int]      =					DetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      UpperCAmelCase_      :      List[Any]      =					"resnet101"
   if "dc5" in model_name:
      UpperCAmelCase_      :      Union[str, Any]      =					True
   UpperCAmelCase_      :      Dict      =					"panoptic" in model_name
   if is_panoptic:
      UpperCAmelCase_      :      Tuple      =					250
   else:
      UpperCAmelCase_      :      Union[str, Any]      =					91
      UpperCAmelCase_      :      int      =					"huggingface/label-files"
      UpperCAmelCase_      :      List[Any]      =					"coco-detection-id2label.json"
      UpperCAmelCase_      :      List[str]      =					json.load(open(hf_hub_download(__lowerCamelCase,	__lowerCamelCase,	repo_type="dataset"						),	"r"						)						)
      UpperCAmelCase_      :      Union[str, Any]      =					{int(__lowerCamelCase						): v for k, v in idalabel.items()}
      UpperCAmelCase_      :      Union[str, Any]      =					idalabel
      UpperCAmelCase_      :      List[str]      =					{v: k for k, v in idalabel.items()}

   # load image processor
   UpperCAmelCase_      :      Optional[int]      =					"coco_panoptic" if is_panoptic else "coco_detection"
   UpperCAmelCase_      :      List[Any]      =					DetrImageProcessor(format=__lowerCamelCase						)

   # prepare image
   UpperCAmelCase_      :      int      =					prepare_img()
   UpperCAmelCase_      :      Tuple      =					image_processor(images=__lowerCamelCase,	return_tensors="pt"						)
   UpperCAmelCase_      :      int      =					encoding["pixel_values"]

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

   # load original model from torch hub
   UpperCAmelCase_      :      Optional[Any]      =					torch.hub.load("facebookresearch/detr",	__lowerCamelCase,	pretrained=__lowerCamelCase						).eval()
   UpperCAmelCase_      :      Tuple      =					detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         UpperCAmelCase_      :      Any      =					"detr." + src
      rename_key(__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						)
   UpperCAmelCase_      :      Optional[int]      =					rename_backbone_keys(__lowerCamelCase						)
   # query, key and value matrices need special treatment
   read_in_q_k_v(__lowerCamelCase,	is_panoptic=__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_      :      Optional[Any]      =					"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"						)
         ):
            UpperCAmelCase_      :      List[Any]      =					state_dict.pop(__lowerCamelCase						)
            UpperCAmelCase_      :      int      =					val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            UpperCAmelCase_      :      Tuple      =					state_dict.pop(__lowerCamelCase						)
            UpperCAmelCase_      :      str      =					val
         elif key.startswith("bbox_attention"						) or key.startswith("mask_head"						):
            continue
         else:
            UpperCAmelCase_      :      Union[str, Any]      =					state_dict.pop(__lowerCamelCase						)
            UpperCAmelCase_      :      List[Any]      =					val
      else:
         if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
            UpperCAmelCase_      :      int      =					state_dict.pop(__lowerCamelCase						)
            UpperCAmelCase_      :      str      =					val
    # finally, create HuggingFace model and load state dict
   UpperCAmelCase_      :      Tuple      =					DetrForSegmentation(__lowerCamelCase						) if is_panoptic else DetrForObjectDetection(__lowerCamelCase						)
   model.load_state_dict(__lowerCamelCase						)
   model.eval()
   # verify our conversion
   UpperCAmelCase_      :      str      =					detr(__lowerCamelCase						)
   UpperCAmelCase_      :      int      =					model(__lowerCamelCase						)
   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(__lowerCamelCase						).mkdir(exist_ok=__lowerCamelCase						)
   model.save_pretrained(__lowerCamelCase						)
   image_processor.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
    _a	 =							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.'
    )
    _a	 =							parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




"""simple docstring"""





from __future__ import annotations

from statistics import mean







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







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


if __name__ == "__main__":
    _a	 =							[3, 5, 7]
    _a	 =							calculate_waiting_times(burst_times)
    _a	 =							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}""")


