

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCamelCase__	:   Optional[Any]								=     {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Any							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    f"""pretrained_models/{model_name}"""
    if not os.path.isfile(_SCREAMING_SNAKE_CASE							):
        os.makedirs('pretrained_models'			, exist_ok=_SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
        download_url(_SCREAMING_SNAKE_CASE			, 'pretrained_models'							)
    SCREAMING_SNAKE_CASE_						=    torch.load(_SCREAMING_SNAKE_CASE			, map_location=lambda _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							: storage							)
    return model


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Optional[int]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    download_model(pretrained_models[args.image_size]							)

    SCREAMING_SNAKE_CASE_						=    state_dict['x_embedder.proj.weight']
    SCREAMING_SNAKE_CASE_						=    state_dict['x_embedder.proj.bias']
    state_dict.pop('x_embedder.proj.weight'							)
    state_dict.pop('x_embedder.proj.bias'							)

    for depth in range(28							):
        SCREAMING_SNAKE_CASE_						=    state_dict[
            't_embedder.mlp.0.weight'
        ]
        SCREAMING_SNAKE_CASE_						=    state_dict[
            't_embedder.mlp.0.bias'
        ]
        SCREAMING_SNAKE_CASE_						=    state_dict[
            't_embedder.mlp.2.weight'
        ]
        SCREAMING_SNAKE_CASE_						=    state_dict[
            't_embedder.mlp.2.bias'
        ]
        SCREAMING_SNAKE_CASE_						=    state_dict[
            'y_embedder.embedding_table.weight'
        ]

        SCREAMING_SNAKE_CASE_						=    state_dict[
            f"""blocks.{depth}.adaLN_modulation.1.weight"""
        ]
        SCREAMING_SNAKE_CASE_						=    state_dict[
            f"""blocks.{depth}.adaLN_modulation.1.bias"""
        ]

        SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""]			, 3			, dim=0							)
        SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""]			, 3			, dim=0							)

        SCREAMING_SNAKE_CASE_						=    q
        SCREAMING_SNAKE_CASE_						=    q_bias
        SCREAMING_SNAKE_CASE_						=    k
        SCREAMING_SNAKE_CASE_						=    k_bias
        SCREAMING_SNAKE_CASE_						=    v
        SCREAMING_SNAKE_CASE_						=    v_bias

        SCREAMING_SNAKE_CASE_						=    state_dict[
            f"""blocks.{depth}.attn.proj.weight"""
        ]
        SCREAMING_SNAKE_CASE_						=    state_dict[f"""blocks.{depth}.attn.proj.bias"""]

        SCREAMING_SNAKE_CASE_						=    state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
        SCREAMING_SNAKE_CASE_						=    state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
        SCREAMING_SNAKE_CASE_						=    state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
        SCREAMING_SNAKE_CASE_						=    state_dict[f"""blocks.{depth}.mlp.fc2.bias"""]

        state_dict.pop(f"""blocks.{depth}.attn.qkv.weight"""							)
        state_dict.pop(f"""blocks.{depth}.attn.qkv.bias"""							)
        state_dict.pop(f"""blocks.{depth}.attn.proj.weight"""							)
        state_dict.pop(f"""blocks.{depth}.attn.proj.bias"""							)
        state_dict.pop(f"""blocks.{depth}.mlp.fc1.weight"""							)
        state_dict.pop(f"""blocks.{depth}.mlp.fc1.bias"""							)
        state_dict.pop(f"""blocks.{depth}.mlp.fc2.weight"""							)
        state_dict.pop(f"""blocks.{depth}.mlp.fc2.bias"""							)
        state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.weight"""							)
        state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.bias"""							)

    state_dict.pop('t_embedder.mlp.0.weight'							)
    state_dict.pop('t_embedder.mlp.0.bias'							)
    state_dict.pop('t_embedder.mlp.2.weight'							)
    state_dict.pop('t_embedder.mlp.2.bias'							)
    state_dict.pop('y_embedder.embedding_table.weight'							)

    SCREAMING_SNAKE_CASE_						=    state_dict['final_layer.adaLN_modulation.1.weight']
    SCREAMING_SNAKE_CASE_						=    state_dict['final_layer.adaLN_modulation.1.bias']
    SCREAMING_SNAKE_CASE_						=    state_dict['final_layer.linear.weight']
    SCREAMING_SNAKE_CASE_						=    state_dict['final_layer.linear.bias']

    state_dict.pop('final_layer.linear.weight'							)
    state_dict.pop('final_layer.linear.bias'							)
    state_dict.pop('final_layer.adaLN_modulation.1.weight'							)
    state_dict.pop('final_layer.adaLN_modulation.1.bias'							)

    # DiT XL/2
    SCREAMING_SNAKE_CASE_						=    TransformeraDModel(
        sample_size=args.image_size // 8			, num_layers=28			, attention_head_dim=72			, in_channels=4			, out_channels=8			, patch_size=2			, attention_bias=_SCREAMING_SNAKE_CASE			, num_attention_heads=16			, activation_fn='gelu-approximate'			, num_embeds_ada_norm=1_000			, norm_type='ada_norm_zero'			, norm_elementwise_affine=_SCREAMING_SNAKE_CASE			, )
    transformer.load_state_dict(_SCREAMING_SNAKE_CASE			, strict=_SCREAMING_SNAKE_CASE							)

    SCREAMING_SNAKE_CASE_						=    DDIMScheduler(
        num_train_timesteps=1_000			, beta_schedule='linear'			, prediction_type='epsilon'			, clip_sample=_SCREAMING_SNAKE_CASE			, )

    SCREAMING_SNAKE_CASE_						=    AutoencoderKL.from_pretrained(args.vae_model							)

    SCREAMING_SNAKE_CASE_						=    DiTPipeline(transformer=_SCREAMING_SNAKE_CASE			, vae=_SCREAMING_SNAKE_CASE			, scheduler=_SCREAMING_SNAKE_CASE							)

    if args.save:
        pipeline.save_pretrained(args.checkpoint_path							)


if __name__ == "__main__":
       UpperCamelCase__	:   Optional[int]								=     argparse.ArgumentParser()

       parser.add_argument(
           "--image_size",
           default=256,
           type=int,
           required=False,
           help="Image size of pretrained model, either 256 or 512.",
       )
       parser.add_argument(
           "--vae_model",
           default="stabilityai/sd-vae-ft-ema",
           type=str,
           required=False,
           help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.",
       )
       parser.add_argument(
           "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not."
       )
       parser.add_argument(
           "--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline."
       )

       UpperCamelCase__	:   List[Any]								=     parser.parse_args()
       main(args)





from __future__ import annotations


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    [0] * len(_SCREAMING_SNAKE_CASE							)
    for i in range(1			, len(_SCREAMING_SNAKE_CASE							)							):
        SCREAMING_SNAKE_CASE_						=    duration_times[i - 1] + waiting_times[i - 1]
    return waiting_times


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]			, _SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return [
        duration_time + waiting_times[i]
        for i, duration_time in enumerate(_SCREAMING_SNAKE_CASE							)
    ]


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       # process id's
       UpperCamelCase__	:   List[str]								=     [1, 2, 3]

       # ensure that we actually have processes
       if len(processes) == 0:
              print("Zero amount of processes")
              raise SystemExit(0)

       # duration time of all processes
       UpperCamelCase__	:   Any								=     [19, 8, 9]

       # ensure we can match each id to a duration time
       if len(duration_times) != len(processes):
              print("Unable to match all id's with their duration time")
              raise SystemExit(0)

       # get the waiting times and the turnaround times
       UpperCamelCase__	:   List[str]								=     calculate_waiting_times(duration_times)
       UpperCamelCase__	:   Union[str, Any]								=     calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       UpperCamelCase__	:   str								=     calculate_average_waiting_time(waiting_times)
       UpperCamelCase__	:   List[Any]								=     calculate_average_turnaround_time(turnaround_times)

       # print all the results
       print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
       for i, process in enumerate(processes):
              print(
                  F'{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'
                  F'{turnaround_times[i]}'
              )
       print(F'Average waiting time = {average_waiting_time}')
       print(F'Average turn around time = {average_turnaround_time}')


