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 (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__								=logging.get_logger(__name__)







def  __UpperCamelCase    (						A ,							A=False							):
					UpperCamelCase__										=      OrderedDict()
					for key, value in state_dict.items():
										if encoder_only and not key.startswith('''head'''							):
															UpperCamelCase__										=      '''segformer.encoder.''' + key
										if key.startswith('''backbone'''							):
															UpperCamelCase__										=      key.replace('''backbone''' ,							'''segformer.encoder'''							)
										if "patch_embed" in key:
															# replace for example patch_embed1 by patch_embeddings.0
															UpperCamelCase__										=      key[key.find('''patch_embed'''							) + len('''patch_embed'''							)]
															UpperCamelCase__										=      key.replace(f"patch_embed{idx}" ,							f"patch_embeddings.{int(A							)-1}"							)
										if "norm" in key:
															UpperCamelCase__										=      key.replace('''norm''' ,							'''layer_norm'''							)
										if "segformer.encoder.layer_norm" in key:
															# replace for example layer_norm1 by layer_norm.0
															UpperCamelCase__										=      key[key.find('''segformer.encoder.layer_norm'''							) + len('''segformer.encoder.layer_norm'''							)]
															UpperCamelCase__										=      key.replace(f"layer_norm{idx}" ,							f"layer_norm.{int(A							)-1}"							)
										if "layer_norm1" in key:
															UpperCamelCase__										=      key.replace('''layer_norm1''' ,							'''layer_norm_1'''							)
										if "layer_norm2" in key:
															UpperCamelCase__										=      key.replace('''layer_norm2''' ,							'''layer_norm_2'''							)
										if "block" in key:
															# replace for example block1 by block.0
															UpperCamelCase__										=      key[key.find('''block'''							) + len('''block'''							)]
															UpperCamelCase__										=      key.replace(f"block{idx}" ,							f"block.{int(A							)-1}"							)
										if "attn.q" in key:
															UpperCamelCase__										=      key.replace('''attn.q''' ,							'''attention.self.query'''							)
										if "attn.proj" in key:
															UpperCamelCase__										=      key.replace('''attn.proj''' ,							'''attention.output.dense'''							)
										if "attn" in key:
															UpperCamelCase__										=      key.replace('''attn''' ,							'''attention.self'''							)
										if "fc1" in key:
															UpperCamelCase__										=      key.replace('''fc1''' ,							'''dense1'''							)
										if "fc2" in key:
															UpperCamelCase__										=      key.replace('''fc2''' ,							'''dense2'''							)
										if "linear_pred" in key:
															UpperCamelCase__										=      key.replace('''linear_pred''' ,							'''classifier'''							)
										if "linear_fuse" in key:
															UpperCamelCase__										=      key.replace('''linear_fuse.conv''' ,							'''linear_fuse'''							)
															UpperCamelCase__										=      key.replace('''linear_fuse.bn''' ,							'''batch_norm'''							)
										if "linear_c" in key:
															# replace for example linear_c4 by linear_c.3
															UpperCamelCase__										=      key[key.find('''linear_c'''							) + len('''linear_c'''							)]
															UpperCamelCase__										=      key.replace(f"linear_c{idx}" ,							f"linear_c.{int(A							)-1}"							)
										if key.startswith('''head'''							):
															UpperCamelCase__										=      key.replace('''head''' ,							'''classifier'''							)
										UpperCamelCase__										=      value

					return new_state_dict







def  __UpperCamelCase    (						A ,							A							):
					# for each of the encoder blocks:
					for i in range(config.num_encoder_blocks							):
										for j in range(config.depths[i]							):
															# read in weights + bias of keys and values (which is a single matrix in the original implementation)
															UpperCamelCase__										=      state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.weight"							)
															UpperCamelCase__										=      state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.bias"							)
															# next, add keys and values (in that order) to the state dict
															UpperCamelCase__										=      kv_weight[
															    : config.hidden_sizes[i], :
															]
															UpperCamelCase__										=      kv_bias[: config.hidden_sizes[i]]
															UpperCamelCase__										=      kv_weight[
															    config.hidden_sizes[i] :, :
															]
															UpperCamelCase__										=      kv_bias[
															    config.hidden_sizes[i] :
															]







def  __UpperCamelCase    (						):
					UpperCamelCase__										=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					UpperCamelCase__										=      Image.open(requests.get(A ,							stream=A							).raw							)

					return image







@torch.no_grad()
def  __UpperCamelCase    (						A ,							A ,							A							):
					UpperCamelCase__										=      SegformerConfig()
					UpperCamelCase__										=      False

					# set attributes based on model_name
					UpperCamelCase__										=      '''huggingface/label-files'''
					if "segformer" in model_name:
										UpperCamelCase__										=      model_name[len('''segformer.'''							) : len('''segformer.'''							) + 2]
										if "ade" in model_name:
															UpperCamelCase__										=      150
															UpperCamelCase__										=      '''ade20k-id2label.json'''
															UpperCamelCase__										=      (1, 150, 128, 128)
										elif "city" in model_name:
															UpperCamelCase__										=      19
															UpperCamelCase__										=      '''cityscapes-id2label.json'''
															UpperCamelCase__										=      (1, 19, 128, 128)
										else:
															raise ValueError(f"Model {model_name} not supported"							)
					elif "mit" in model_name:
										UpperCamelCase__										=      True
										UpperCamelCase__										=      model_name[4:6]
										UpperCamelCase__										=      1000
										UpperCamelCase__										=      '''imagenet-1k-id2label.json'''
										UpperCamelCase__										=      (1, 1000)
					else:
										raise ValueError(f"Model {model_name} not supported"							)

					# set config attributes
					UpperCamelCase__										=      json.load(open(hf_hub_download(A ,							A ,							repo_type='''dataset'''							) ,							'''r'''							)							)
					UpperCamelCase__										=      {int(A							): v for k, v in idalabel.items()}
					UpperCamelCase__										=      idalabel
					UpperCamelCase__										=      {v: k for k, v in idalabel.items()}
					if size == "b0":
										pass
					elif size == "b1":
										UpperCamelCase__										=      [64, 128, 320, 512]
										UpperCamelCase__										=      256
					elif size == "b2":
										UpperCamelCase__										=      [64, 128, 320, 512]
										UpperCamelCase__										=      768
										UpperCamelCase__										=      [3, 4, 6, 3]
					elif size == "b3":
										UpperCamelCase__										=      [64, 128, 320, 512]
										UpperCamelCase__										=      768
										UpperCamelCase__										=      [3, 4, 18, 3]
					elif size == "b4":
										UpperCamelCase__										=      [64, 128, 320, 512]
										UpperCamelCase__										=      768
										UpperCamelCase__										=      [3, 8, 27, 3]
					elif size == "b5":
										UpperCamelCase__										=      [64, 128, 320, 512]
										UpperCamelCase__										=      768
										UpperCamelCase__										=      [3, 6, 40, 3]
					else:
										raise ValueError(f"Size {size} not supported"							)

					# load image processor (only resize + normalize)
					UpperCamelCase__										=      SegformerImageProcessor(
					    image_scale=(512, 512) ,							keep_ratio=A ,							align=A ,							do_random_crop=A							)

					# prepare image
					UpperCamelCase__										=      prepare_img()
					UpperCamelCase__										=      image_processor(images=A ,							return_tensors='''pt'''							).pixel_values

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

					# load original state dict
					if encoder_only:
										UpperCamelCase__										=      torch.load(A ,							map_location=torch.device('''cpu'''							)							)
					else:
										UpperCamelCase__										=      torch.load(A ,							map_location=torch.device('''cpu'''							)							)['''state_dict''']

					# rename keys
					UpperCamelCase__										=      rename_keys(A ,							encoder_only=A							)
					if not encoder_only:
										del state_dict["decode_head.conv_seg.weight"]
										del state_dict["decode_head.conv_seg.bias"]

					# key and value matrices need special treatment
					read_in_k_v(A ,							A							)

					# create HuggingFace model and load state dict
					if encoder_only:
										UpperCamelCase__										=      False
										UpperCamelCase__										=      SegformerForImageClassification(A							)
					else:
										UpperCamelCase__										=      SegformerForSemanticSegmentation(A							)
					model.load_state_dict(A							)
					model.eval()

					# forward pass
					UpperCamelCase__										=      model(A							)
					UpperCamelCase__										=      outputs.logits

					# set expected_slice based on model name
					# ADE20k checkpoints
					if model_name == "segformer.b0.512x512.ade.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
										        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
										        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
										    ]							)
					elif model_name == "segformer.b1.512x512.ade.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
										        [[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
										        [[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
										    ]							)
					elif model_name == "segformer.b2.512x512.ade.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
										        [[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
										        [[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
										    ]							)
					elif model_name == "segformer.b3.512x512.ade.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
										        [[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
										        [[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
										    ]							)
					elif model_name == "segformer.b4.512x512.ade.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
										        [[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
										        [[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
										    ]							)
					elif model_name == "segformer.b5.640x640.ade.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
										        [[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
										        [[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
										    ]							)
					# Cityscapes checkpoints
					elif model_name == "segformer.b0.1024x1024.city.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
										        [[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
										        [[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
										    ]							)
					elif model_name == "segformer.b0.512x1024.city.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
										        [[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
										        [[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
										    ]							)
					elif model_name == "segformer.b0.640x1280.city.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [
										            [-1.13_72e01, -1.27_87e01, -1.34_77e01],
										            [-1.25_36e01, -1.41_94e01, -1.44_09e01],
										            [-1.32_17e01, -1.48_88e01, -1.53_27e01],
										        ],
										        [
										            [-1.47_91e01, -1.71_22e01, -1.82_77e01],
										            [-1.71_63e01, -1.91_92e01, -1.95_33e01],
										            [-1.78_97e01, -1.99_91e01, -2.03_15e01],
										        ],
										        [
										            [7.67_23e-01, 4.19_21e-01, -7.78_78e-02],
										            [4.77_72e-01, 9.55_57e-03, -2.80_82e-01],
										            [3.60_32e-01, -2.48_26e-01, -5.11_68e-01],
										        ],
										    ]							)
					elif model_name == "segformer.b0.768x768.city.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
										        [[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
										        [[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
										    ]							)
					elif model_name == "segformer.b1.1024x1024.city.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
										        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
										        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
										    ]							)
					elif model_name == "segformer.b2.1024x1024.city.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
										        [[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
										        [[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
										    ]							)
					elif model_name == "segformer.b3.1024x1024.city.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
										        [[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
										        [[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
										    ]							)
					elif model_name == "segformer.b4.1024x1024.city.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
										        [[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
										        [[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
										    ]							)
					elif model_name == "segformer.b5.1024x1024.city.160k":
										UpperCamelCase__										=      torch.tensor(
										    [
										        [[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
										        [[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
										        [[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
										    ]							)
					else:
										UpperCamelCase__										=      logits.argmax(-1							).item()
										print('''Predicted class:''' ,							model.config.idalabel[predicted_class_idx]							)

					# verify logits
					if not encoder_only:
										assert logits.shape == expected_shape
										assert torch.allclose(logits[0, :3, :3, :3] ,							A ,							atol=1e-2							)

					# finally, 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							)


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

					parser.add_argument(
					    '''--model_name''',
					    default='''segformer.b0.512x512.ade.160k''',
					    type=str,
					    help='''Name of the model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
					)
					__magic_name__								=parser.parse_args()
					convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




from __future__ import annotations

import pandas as pd







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     UpperCamelCase__										=      [0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i]

     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      999999999
     UpperCamelCase__										=      0
     UpperCamelCase__										=      False

     # Process until all processes are completed
     while complete != no_of_processes:
          for j in range(A							):
               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__										=      999999999

          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    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i] + waiting_time[i]
     return turn_around_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     for i in range(A							):
          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''')
     __magic_name__								=int(input())
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=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))
          __magic_name__		,  __magic_name__								=map(int, input().split())

     __magic_name__								=calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     __magic_name__								=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)



