






'''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 DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__						:int          =			logging.get_logger(__name__)
def     __lowercase  (_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				DPTConfig(embedding_type="""hybrid"""     )

     if "large" in checkpoint_url:
          __lowerCamelCase						:       List[str]				   =				1_024
          __lowerCamelCase						:       int				   =				4_096
          __lowerCamelCase						:       Tuple				   =				24
          __lowerCamelCase						:       List[Any]				   =				16
          __lowerCamelCase						:       Any				   =				[5, 11, 17, 23]
          __lowerCamelCase						:       Dict				   =				[256, 512, 1_024, 1_024]
          __lowerCamelCase						:       Dict				   =				(1, 384, 384)

     if "nyu" or "midas" in checkpoint_url:
          __lowerCamelCase						:       Dict				   =				768
          __lowerCamelCase						:       List[Any]				   =				[1, 1, 1, 0.5]
          __lowerCamelCase						:       List[str]				   =				[256, 512, 768, 768]
          __lowerCamelCase						:       Union[str, Any]				   =				150
          __lowerCamelCase						:       str				   =				16
          __lowerCamelCase						:       Dict				   =				(1, 384, 384)
          __lowerCamelCase						:       Tuple				   =				False
          __lowerCamelCase						:       Optional[Any]				   =				"""project"""

     if "ade" in checkpoint_url:
          __lowerCamelCase						:       Optional[int]				   =				True
          __lowerCamelCase						:       List[Any]				   =				768
          __lowerCamelCase						:       int				   =				[1, 1, 1, 0.5]
          __lowerCamelCase						:       Dict				   =				150
          __lowerCamelCase						:       int				   =				16
          __lowerCamelCase						:       Union[str, Any]				   =				"""huggingface/label-files"""
          __lowerCamelCase						:       Optional[int]				   =				"""ade20k-id2label.json"""
          __lowerCamelCase						:       int				   =				json.load(open(cached_download(hf_hub_url(_lowercase,						_lowercase,						repo_type="""dataset"""     )     ),						"""r"""     )     )
          __lowerCamelCase						:       str				   =				{int(_lowercase     ): v for k, v in idalabel.items()}
          __lowerCamelCase						:       Optional[int]				   =				idalabel
          __lowerCamelCase						:       Optional[int]				   =				{v: k for k, v in idalabel.items()}
          __lowerCamelCase						:       List[Any]				   =				[1, 150, 480, 480]

     return config, expected_shape
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
     for k in ignore_keys:
          state_dict.pop(_lowercase,						_lowercase     )
def     __lowercase  (_lowercase     )   ->     List[Any]:


     """simple docstring"""
     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          __lowerCamelCase						:       List[str]				   =				name.replace("""pretrained.model""",						"""dpt.encoder"""     )
     if "pretrained.model" in name:
          __lowerCamelCase						:       Optional[int]				   =				name.replace("""pretrained.model""",						"""dpt.embeddings"""     )
     if "patch_embed" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""patch_embed""",						""""""     )
     if "pos_embed" in name:
          __lowerCamelCase						:       Optional[int]				   =				name.replace("""pos_embed""",						"""position_embeddings"""     )
     if "attn.proj" in name:
          __lowerCamelCase						:       int				   =				name.replace("""attn.proj""",						"""attention.output.dense"""     )
     if "proj" in name and "project" not in name:
          __lowerCamelCase						:       str				   =				name.replace("""proj""",						"""projection"""     )
     if "blocks" in name:
          __lowerCamelCase						:       List[str]				   =				name.replace("""blocks""",						"""layer"""     )
     if "mlp.fc1" in name:
          __lowerCamelCase						:       List[str]				   =				name.replace("""mlp.fc1""",						"""intermediate.dense"""     )
     if "mlp.fc2" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""mlp.fc2""",						"""output.dense"""     )
     if "norm1" in name and "backbone" not in name:
          __lowerCamelCase						:       str				   =				name.replace("""norm1""",						"""layernorm_before"""     )
     if "norm2" in name and "backbone" not in name:
          __lowerCamelCase						:       Any				   =				name.replace("""norm2""",						"""layernorm_after"""     )
     if "scratch.output_conv" in name:
          __lowerCamelCase						:       int				   =				name.replace("""scratch.output_conv""",						"""head"""     )
     if "scratch" in name:
          __lowerCamelCase						:       str				   =				name.replace("""scratch""",						"""neck"""     )
     if "layer1_rn" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""layer1_rn""",						"""convs.0"""     )
     if "layer2_rn" in name:
          __lowerCamelCase						:       Tuple				   =				name.replace("""layer2_rn""",						"""convs.1"""     )
     if "layer3_rn" in name:
          __lowerCamelCase						:       List[Any]				   =				name.replace("""layer3_rn""",						"""convs.2"""     )
     if "layer4_rn" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""layer4_rn""",						"""convs.3"""     )
     if "refinenet" in name:
          __lowerCamelCase						:       int				   =				int(name[len("""neck.refinenet"""     ) : len("""neck.refinenet"""     ) + 1]     )
          # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
          __lowerCamelCase						:       Optional[Any]				   =				name.replace(f"refinenet{layer_idx}",						f"fusion_stage.layers.{abs(layer_idx-4     )}"     )
     if "out_conv" in name:
          __lowerCamelCase						:       List[Any]				   =				name.replace("""out_conv""",						"""projection"""     )
     if "resConfUnit1" in name:
          __lowerCamelCase						:       Any				   =				name.replace("""resConfUnit1""",						"""residual_layer1"""     )
     if "resConfUnit2" in name:
          __lowerCamelCase						:       List[str]				   =				name.replace("""resConfUnit2""",						"""residual_layer2"""     )
     if "conv1" in name:
          __lowerCamelCase						:       List[str]				   =				name.replace("""conv1""",						"""convolution1"""     )
     if "conv2" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""conv2""",						"""convolution2"""     )
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""pretrained.act_postprocess1.0.project.0""",						"""neck.reassemble_stage.readout_projects.0.0"""     )
     if "pretrained.act_postprocess2.0.project.0" in name:
          __lowerCamelCase						:       Optional[int]				   =				name.replace("""pretrained.act_postprocess2.0.project.0""",						"""neck.reassemble_stage.readout_projects.1.0"""     )
     if "pretrained.act_postprocess3.0.project.0" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""pretrained.act_postprocess3.0.project.0""",						"""neck.reassemble_stage.readout_projects.2.0"""     )
     if "pretrained.act_postprocess4.0.project.0" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""pretrained.act_postprocess4.0.project.0""",						"""neck.reassemble_stage.readout_projects.3.0"""     )

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          __lowerCamelCase						:       List[str]				   =				name.replace("""pretrained.act_postprocess1.3""",						"""neck.reassemble_stage.layers.0.projection"""     )
     if "pretrained.act_postprocess1.4" in name:
          __lowerCamelCase						:       int				   =				name.replace("""pretrained.act_postprocess1.4""",						"""neck.reassemble_stage.layers.0.resize"""     )
     if "pretrained.act_postprocess2.3" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""pretrained.act_postprocess2.3""",						"""neck.reassemble_stage.layers.1.projection"""     )
     if "pretrained.act_postprocess2.4" in name:
          __lowerCamelCase						:       int				   =				name.replace("""pretrained.act_postprocess2.4""",						"""neck.reassemble_stage.layers.1.resize"""     )
     if "pretrained.act_postprocess3.3" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""pretrained.act_postprocess3.3""",						"""neck.reassemble_stage.layers.2.projection"""     )
     if "pretrained.act_postprocess4.3" in name:
          __lowerCamelCase						:       Any				   =				name.replace("""pretrained.act_postprocess4.3""",						"""neck.reassemble_stage.layers.3.projection"""     )
     if "pretrained.act_postprocess4.4" in name:
          __lowerCamelCase						:       Any				   =				name.replace("""pretrained.act_postprocess4.4""",						"""neck.reassemble_stage.layers.3.resize"""     )
     if "pretrained" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""pretrained""",						"""dpt"""     )
     if "bn" in name:
          __lowerCamelCase						:       List[Any]				   =				name.replace("""bn""",						"""batch_norm"""     )
     if "head" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""head""",						"""head.head"""     )
     if "encoder.norm" in name:
          __lowerCamelCase						:       str				   =				name.replace("""encoder.norm""",						"""layernorm"""     )
     if "auxlayer" in name:
          __lowerCamelCase						:       Tuple				   =				name.replace("""auxlayer""",						"""auxiliary_head.head"""     )
     if "backbone" in name:
          __lowerCamelCase						:       int				   =				name.replace("""backbone""",						"""backbone.bit.encoder"""     )

     if ".." in name:
          __lowerCamelCase						:       str				   =				name.replace("""..""",						"""."""     )

     if "stem.conv" in name:
          __lowerCamelCase						:       Dict				   =				name.replace("""stem.conv""",						"""bit.embedder.convolution"""     )
     if "blocks" in name:
          __lowerCamelCase						:       Tuple				   =				name.replace("""blocks""",						"""layers"""     )
     if "convolution" in name and "backbone" in name:
          __lowerCamelCase						:       str				   =				name.replace("""convolution""",						"""conv"""     )
     if "layer" in name and "backbone" in name:
          __lowerCamelCase						:       Optional[int]				   =				name.replace("""layer""",						"""layers"""     )
     if "backbone.bit.encoder.bit" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""backbone.bit.encoder.bit""",						"""backbone.bit"""     )
     if "embedder.conv" in name:
          __lowerCamelCase						:       Optional[int]				   =				name.replace("""embedder.conv""",						"""embedder.convolution"""     )
     if "backbone.bit.encoder.stem.norm" in name:
          __lowerCamelCase						:       Union[str, Any]				   =				name.replace("""backbone.bit.encoder.stem.norm""",						"""backbone.bit.embedder.norm"""     )
     return name
def     __lowercase  (_lowercase,						_lowercase     )   ->     int:


     """simple docstring"""
     for i in range(config.num_hidden_layers     ):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          __lowerCamelCase						:       Tuple				   =				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"     )
          __lowerCamelCase						:       int				   =				state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"     )
          # next, add query, keys and values (in that order) to the state dict
          __lowerCamelCase						:       List[str]				   =				in_proj_weight[: config.hidden_size, :]
          __lowerCamelCase						:       Union[str, Any]				   =				in_proj_bias[: config.hidden_size]
          __lowerCamelCase						:       Optional[Any]				   =				in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowerCamelCase						:       List[Any]				   =				in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowerCamelCase						:       Tuple				   =				in_proj_weight[
              -config.hidden_size :, :
          ]
          __lowerCamelCase						:       Dict				   =				in_proj_bias[-config.hidden_size :]
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __lowerCamelCase						:       Tuple				   =				Image.open(requests.get(_lowercase,						stream=_lowercase     ).raw     )
     return im
@torch.no_grad()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     List[Any]:


     """simple docstring"""
     __lowerCamelCase						:       List[str]				   =				get_dpt_config(_lowercase     )
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     __lowerCamelCase						:       Tuple				   =				torch.load(_lowercase,						map_location="""cpu"""     )
     # remove certain keys
     remove_ignore_keys_(_lowercase     )
     # rename keys
     for key in state_dict.copy().keys():
          __lowerCamelCase						:       List[Any]				   =				state_dict.pop(_lowercase     )
          __lowerCamelCase						:       Union[str, Any]				   =				val
     # read in qkv matrices
     read_in_q_k_v(_lowercase,						_lowercase     )

     # load HuggingFace model
     __lowerCamelCase						:       Union[str, Any]				   =				DPTForSemanticSegmentation(_lowercase     ) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowercase     )
     model.load_state_dict(_lowercase     )
     model.eval()

     # Check outputs on an image
     __lowerCamelCase						:       Union[str, Any]				   =				480 if """ade""" in checkpoint_url else 384
     __lowerCamelCase						:       int				   =				DPTImageProcessor(size=_lowercase     )

     __lowerCamelCase						:       Any				   =				prepare_img()
     __lowerCamelCase						:       Union[str, Any]				   =				image_processor(_lowercase,						return_tensors="""pt"""     )

     # forward pass
     __lowerCamelCase						:       str				   =				model(**_lowercase     ).logits if """ade""" in checkpoint_url else model(**_lowercase     ).predicted_depth

     if show_prediction:
          __lowerCamelCase						:       List[str]				   =				(
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1     ),						size=(image.size[1], image.size[0]),						mode="""bicubic""",						align_corners=_lowercase,						)
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255     ).show()

     if pytorch_dump_folder_path is not None:
          Path(_lowercase     ).mkdir(exist_ok=_lowercase     )
          print(f"Saving model to {pytorch_dump_folder_path}"     )
          model.save_pretrained(_lowercase     )
          print(f"Saving image processor to {pytorch_dump_folder_path}"     )
          image_processor.save_pretrained(_lowercase     )

     if push_to_hub:
          model.push_to_hub("""ybelkada/dpt-hybrid-midas"""     )
          image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""     )


if __name__ == "__main__":
   UpperCAmelCase__						:Union[str, Any]          =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
       type=str,
       help="""URL of the original DPT checkpoint you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=False,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument(
       """--push_to_hub""",
       action="""store_true""",
   )
   parser.add_argument(
       """--model_name""",
       default="""dpt-large""",
       type=str,
       help="""Name of the model, in case you're pushing to the hub.""",
   )
   parser.add_argument(
       """--show_prediction""",
       action="""store_true""",
   )

   UpperCAmelCase__						:str          =			parser.parse_args()
   convert_dpt_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
   )











'''simple docstring'''

from statistics import mean

import numpy as np
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     list:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				0
     # Number of processes finished
     __lowerCamelCase						:       List[Any]				   =				0
     # Displays the finished process.
     # If it is 0, the performance is completed if it is 1, before the performance.
     __lowerCamelCase						:       int				   =				[0] * no_of_process
     # List to include calculation results
     __lowerCamelCase						:       Optional[int]				   =				[0] * no_of_process

     # Sort by arrival time.
     __lowerCamelCase						:       int				   =				[burst_time[i] for i in np.argsort(_lowercase     )]
     __lowerCamelCase						:       Union[str, Any]				   =				[process_name[i] for i in np.argsort(_lowercase     )]
     arrival_time.sort()

     while no_of_process > finished_process_count:
          __lowerCamelCase						:       Tuple				   =				0
          while finished_process[i] == 1:
               i += 1
          if current_time < arrival_time[i]:
               __lowerCamelCase						:       List[str]				   =				arrival_time[i]

          __lowerCamelCase						:       Dict				   =				0
          # Index showing the location of the process being performed
          __lowerCamelCase						:       Optional[Any]				   =				0
          # Saves the current response ratio.
          __lowerCamelCase						:       List[str]				   =				0
          for i in range(0,						_lowercase     ):
               if finished_process[i] == 0 and arrival_time[i] <= current_time:
                    __lowerCamelCase						:       Optional[int]				   =				(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                        i
                    ]
               if response_ratio < temp:
                    __lowerCamelCase						:       List[Any]				   =				temp
                    __lowerCamelCase						:       str				   =				i

        # Calculate the turn around time
          __lowerCamelCase						:       Optional[Any]				   =				current_time + burst_time[loc] - arrival_time[loc]
          current_time += burst_time[loc]
          # Indicates that the process has been performed.
          __lowerCamelCase						:       Optional[Any]				   =				1
          # Increase finished_process_count by 1
          finished_process_count += 1

     return turn_around_time
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     list:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				[0] * no_of_process
     for i in range(0,						_lowercase     ):
          __lowerCamelCase						:       Tuple				   =				turn_around_time[i] - burst_time[i]
     return waiting_time


if __name__ == "__main__":
   UpperCAmelCase__						:List[Any]          =			5
   UpperCAmelCase__						:Tuple          =			["""A""", """B""", """C""", """D""", """E"""]
   UpperCAmelCase__						:Tuple          =			[1, 2, 3, 4, 5]
   UpperCAmelCase__						:List[Any]          =			[1, 2, 3, 4, 5]

   UpperCAmelCase__						:Union[str, Any]          =			calculate_turn_around_time(
       process_name, arrival_time, burst_time, no_of_process
   )
   UpperCAmelCase__						:Optional[int]          =			calculate_waiting_time(
       process_name, turn_around_time, burst_time, no_of_process
   )

   print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
   for i in range(0, no_of_process):
      print(
          f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
          f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
      )

   print(f'''average waiting time : {mean(waiting_time):.5f}''')
   print(f'''average turn around time : {mean(turn_around_time):.5f}''')



