
"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__          =	logging.get_logger(__name__)
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Dict     							=					SwinConfig(
        embed_dim=192    ,depths=(2, 2, 18, 2)    ,num_heads=(6, 12, 24, 48)    ,window_size=12    ,out_features=['stage2', 'stage3', 'stage4']    ,)

    UpperCAmelCase__	:      int     							=					DetaConfig(
        backbone_config=_snake_case    ,num_queries=900    ,encoder_ffn_dim=2048    ,decoder_ffn_dim=2048    ,num_feature_levels=5    ,assign_first_stage=_snake_case    ,with_box_refine=_snake_case    ,two_stage=_snake_case    ,)

    # set labels
    UpperCAmelCase__	:      Dict     							=					'huggingface/label-files'
    if "o365" in model_name:
        UpperCAmelCase__	:      Dict     							=					366
        UpperCAmelCase__	:      List[Any]     							=					'object365-id2label.json'
    else:
        UpperCAmelCase__	:      int     							=					91
        UpperCAmelCase__	:      List[Any]     							=					'coco-detection-id2label.json'

    UpperCAmelCase__	:      Optional[int]     							=					num_labels
    UpperCAmelCase__	:      Optional[Any]     							=					json.load(open(cached_download(hf_hub_url(_snake_case    ,_snake_case    ,repo_type='dataset' ) )    ,'r' ) )
    UpperCAmelCase__	:      Tuple     							=					{int(_snake_case ): v for k, v in idalabel.items()}
    UpperCAmelCase__	:      Optional[int]     							=					idalabel
    UpperCAmelCase__	:      Dict     							=					{v: k for k, v in idalabel.items()}

    return config
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Union[str, Any]     							=					[]

    # stem
    # fmt: off
    rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight') )
    rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias') )
    rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight') )
    rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias') )
    # stages
    for i in range(len(config.backbone_config.depths ) ):
        for j in range(config.backbone_config.depths[i] ):
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )

        if i < 3:
            rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''') )
            rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''') )

    rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight') )
    rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias') )
    rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight') )
    rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias') )
    rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight') )
    rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias') )

    # transformer encoder
    for i in range(config.encoder_layers ):
        rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''') )
        rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''') )

    # transformer decoder
    for i in range(config.decoder_layers ):
        rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''') )
        rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''') )

    # fmt: on

    return rename_keys
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Any     							=					dct.pop(_snake_case )
    UpperCAmelCase__	:      Dict     							=					val
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Dict     							=					[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
    for i in range(len(backbone_config.depths ) ):
        UpperCAmelCase__	:      Union[str, Any]     							=					num_features[i]
        for j in range(backbone_config.depths[i] ):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            UpperCAmelCase__	:      Tuple     							=					state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight''' )
            UpperCAmelCase__	:      Dict     							=					state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias''' )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase__	:      List[str]     							=					in_proj_weight[:dim, :]
            UpperCAmelCase__	:      int     							=					in_proj_bias[: dim]
            UpperCAmelCase__	:      Dict     							=					in_proj_weight[
                dim : dim * 2, :
            ]
            UpperCAmelCase__	:      Union[str, Any]     							=					in_proj_bias[
                dim : dim * 2
            ]
            UpperCAmelCase__	:      Optional[Any]     							=					in_proj_weight[
                -dim :, :
            ]
            UpperCAmelCase__	:      Union[str, Any]     							=					in_proj_bias[-dim :]
            # fmt: on
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					config.d_model
    for i in range(config.decoder_layers ):
        # read in weights + bias of input projection layer of self-attention
        UpperCAmelCase__	:      int     							=					state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
        UpperCAmelCase__	:      List[str]     							=					state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase__	:      int     							=					in_proj_weight[:hidden_size, :]
        UpperCAmelCase__	:      Tuple     							=					in_proj_bias[:hidden_size]
        UpperCAmelCase__	:      Optional[int]     							=					in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        UpperCAmelCase__	:      str     							=					in_proj_bias[hidden_size : hidden_size * 2]
        UpperCAmelCase__	:      Any     							=					in_proj_weight[-hidden_size:, :]
        UpperCAmelCase__	:      Optional[Any]     							=					in_proj_bias[-hidden_size:]
def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      Optional[int]     							=					'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase__	:      Any     							=					Image.open(requests.get(_snake_case    ,stream=_snake_case ).raw )

    return im
@torch.no_grad()
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Dict     							=					get_deta_config(_snake_case )

    # load original state dict
    if model_name == "deta-swin-large":
        UpperCAmelCase__	:      Dict     							=					hf_hub_download(repo_id='nielsr/deta-checkpoints'    ,filename='adet_swin_ft.pth' )
    elif model_name == "deta-swin-large-o365":
        UpperCAmelCase__	:      Optional[int]     							=					hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'    ,filename='deta_swin_pt_o365.pth' )
    else:
        raise ValueError(F'''Model name {model_name} not supported''' )

    UpperCAmelCase__	:      List[Any]     							=					torch.load(_snake_case    ,map_location='cpu' )['model']

    # original state dict
    for name, param in state_dict.items():
        print(_snake_case    ,param.shape )

    # rename keys
    UpperCAmelCase__	:      Union[str, Any]     							=					create_rename_keys(_snake_case )
    for src, dest in rename_keys:
        rename_key(_snake_case    ,_snake_case    ,_snake_case )
    read_in_swin_q_k_v(_snake_case    ,config.backbone_config )
    read_in_decoder_q_k_v(_snake_case    ,_snake_case )

    # fix some prefixes
    for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
            UpperCAmelCase__	:      int     							=					state_dict.pop(_snake_case )
            UpperCAmelCase__	:      Dict     							=					val
        if "input_proj" in key:
            UpperCAmelCase__	:      Dict     							=					state_dict.pop(_snake_case )
            UpperCAmelCase__	:      List[Any]     							=					val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            UpperCAmelCase__	:      Optional[Any]     							=					state_dict.pop(_snake_case )
            UpperCAmelCase__	:      str     							=					val

        # finally, create HuggingFace model and load state dict
    UpperCAmelCase__	:      Any     							=					DetaForObjectDetection(_snake_case )
    model.load_state_dict(_snake_case )
    model.eval()

    UpperCAmelCase__	:      int     							=					'cuda' if torch.cuda.is_available() else 'cpu'
    model.to(_snake_case )

    # load image processor
    UpperCAmelCase__	:      Optional[Any]     							=					DetaImageProcessor(format='coco_detection' )

    # verify our conversion on image
    UpperCAmelCase__	:      Any     							=					prepare_img()
    UpperCAmelCase__	:      Dict     							=					processor(images=_snake_case    ,return_tensors='pt' )
    UpperCAmelCase__	:      List[Any]     							=					encoding['pixel_values']
    UpperCAmelCase__	:      Union[str, Any]     							=					model(pixel_values.to(_snake_case ) )

    # verify logits
    print('Logits:'    ,outputs.logits[0, :3, :3] )
    print('Boxes:'    ,outputs.pred_boxes[0, :3, :3] )
    if model_name == "deta-swin-large":
        UpperCAmelCase__	:      Optional[int]     							=					torch.tensor(
            [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] )
        UpperCAmelCase__	:      Dict     							=					torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]] )
    elif model_name == "deta-swin-large-o365":
        UpperCAmelCase__	:      Dict     							=					torch.tensor(
            [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]] )
        UpperCAmelCase__	:      List[Any]     							=					torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]] )
    assert torch.allclose(outputs.logits[0, :3, :3]    ,expected_logits.to(_snake_case )    ,atol=1e-4 )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,expected_boxes.to(_snake_case )    ,atol=1e-4 )
    print('Everything ok!' )

    if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...''' )
        Path(_snake_case ).mkdir(exist_ok=_snake_case )
        model.save_pretrained(_snake_case )
        processor.save_pretrained(_snake_case )

    # Push to hub
    if push_to_hub:
        print('Pushing model and processor to hub...' )
        model.push_to_hub(F'''jozhang97/{model_name}''' )
        processor.push_to_hub(F'''jozhang97/{model_name}''' )


if __name__ == "__main__":
    UpperCamelCase__          =	argparse.ArgumentParser()

    parser.add_argument(
        '--model_name',
        type=str,
        default='deta-swin-large',
        choices=['deta-swin-large', 'deta-swin-large-o365'],
        help='Name of the 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.',
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )
    UpperCamelCase__          =	parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""


from __future__ import annotations

import pandas as pd






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [0] * no_of_processes
  _UpperCAmelCase    			=   [0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(lowercase		):
    _UpperCAmelCase    			=   burst_time[i]

  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   9_99_99_99_99
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   False

  # Process until all processes are completed
  while complete != no_of_processes:
    for j in range(lowercase		):
      if arrival_time[j] <= increment_time and remaining_time[j] > 0:
        if remaining_time[j] < minm:
          _UpperCAmelCase    			=   remaining_time[j]
          _UpperCAmelCase    			=   j
          _UpperCAmelCase    			=   True

    if not check:
      increment_time += 1
      continue
    remaining_time[short] -= 1

    _UpperCAmelCase    			=   remaining_time[short]
    if minm == 0:
      _UpperCAmelCase    			=   9_99_99_99_99

    if remaining_time[short] == 0:
      complete += 1
      _UpperCAmelCase    			=   False

      # Find finish time of current process
      _UpperCAmelCase    			=   increment_time + 1

      # Calculate waiting time
      _UpperCAmelCase    			=   finish_time - arrival_time[short]
      _UpperCAmelCase    			=   finar - burst_time[short]

      if waiting_time[short] < 0:
        _UpperCAmelCase    			=   0

        # Increment time
    increment_time += 1
  return waiting_time






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [0] * no_of_processes
  for i in range(lowercase		):
    _UpperCAmelCase    			=   burst_time[i] + waiting_time[i]
  return turn_around_time






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  for i in range(lowercase		):
    _UpperCAmelCase    			=   total_waiting_time + waiting_time[i]
    _UpperCAmelCase    			=   total_turn_around_time + turn_around_time[i]
  print(f'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''		)
  print("""Average turn around time =""" ,total_turn_around_time / no_of_processes		)


if __name__ == "__main__":
    print("""Enter how many process you want to analyze""")
    UpperCAmelCase__			=    int(input())
    UpperCAmelCase__			=    [0] * no_of_processes
    UpperCAmelCase__			=    [0] * no_of_processes
    UpperCAmelCase__			=    list(range(1, no_of_processes + 1))

    for i in range(no_of_processes):
        print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
        UpperCAmelCase__		, UpperCAmelCase__			=    map(int, input().split())

    UpperCAmelCase__			=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

    UpperCAmelCase__			=    burst_time
    UpperCAmelCase__			=    no_of_processes
    UpperCAmelCase__			=    waiting_time
    UpperCAmelCase__			=    calculate_turnaroundtime(bt, n, wt)

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    UpperCAmelCase__			=    pd.DataFrame(
        list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
        columns=[
            """Process""",
            """BurstTime""",
            """ArrivalTime""",
            """WaitingTime""",
            """TurnAroundTime""",
        ],
    )

    # Printing the dataFrame
    pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
    print(fcfs)
