


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowercase_           =	{5_1_2: """DiT-XL-2-512x512.pt""", 2_5_6: """DiT-XL-2-256x256.pt"""}
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]							=						f'pretrained_models/{model_name}'
 if not os.path.isfile(SCREAMING_SNAKE_CASE__						):
  os.makedirs('pretrained_models'			,       exist_ok=SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Any							=						f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
  download_url(SCREAMING_SNAKE_CASE__			,       'pretrained_models'						)
 __lowerCamelCase		:							str							=						torch.load(SCREAMING_SNAKE_CASE__			,       map_location=lambda SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__						: storage						)
 return model
def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]							=						download_model(pretrained_models[args.image_size]						)

 __lowerCamelCase		:							Dict							=						state_dict["x_embedder.proj.weight"]
 __lowerCamelCase		:							List[str]							=						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						):
  __lowerCamelCase		:							Optional[Any]							=						state_dict[
      "t_embedder.mlp.0.weight"
  ]
  __lowerCamelCase		:							Union[str, Any]							=						state_dict[
      "t_embedder.mlp.0.bias"
  ]
  __lowerCamelCase		:							List[Any]							=						state_dict[
      "t_embedder.mlp.2.weight"
  ]
  __lowerCamelCase		:							Dict							=						state_dict[
      "t_embedder.mlp.2.bias"
  ]
  __lowerCamelCase		:							int							=						state_dict[
      "y_embedder.embedding_table.weight"
  ]

  __lowerCamelCase		:							Optional[Any]							=						state_dict[
      f'blocks.{depth}.adaLN_modulation.1.weight'
  ]
  __lowerCamelCase		:							List[str]							=						state_dict[
      f'blocks.{depth}.adaLN_modulation.1.bias'
  ]

  __lowerCamelCase		:							str							=						torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']			,       3			,       dim=0						)
  __lowerCamelCase		:							List[str]							=						torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']			,       3			,       dim=0						)

  __lowerCamelCase		:							Union[str, Any]							=						q
  __lowerCamelCase		:							str							=						q_bias
  __lowerCamelCase		:							Union[str, Any]							=						k
  __lowerCamelCase		:							str							=						k_bias
  __lowerCamelCase		:							Optional[Any]							=						v
  __lowerCamelCase		:							Any							=						v_bias

  __lowerCamelCase		:							List[str]							=						state_dict[
      f'blocks.{depth}.attn.proj.weight'
  ]
  __lowerCamelCase		:							Dict							=						state_dict[f'blocks.{depth}.attn.proj.bias']

  __lowerCamelCase		:							Union[str, Any]							=						state_dict[f'blocks.{depth}.mlp.fc1.weight']
  __lowerCamelCase		:							Optional[int]							=						state_dict[f'blocks.{depth}.mlp.fc1.bias']
  __lowerCamelCase		:							int							=						state_dict[f'blocks.{depth}.mlp.fc2.weight']
  __lowerCamelCase		:							List[str]							=						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'						)

 __lowerCamelCase		:							Any							=						state_dict["final_layer.adaLN_modulation.1.weight"]
 __lowerCamelCase		:							Any							=						state_dict["final_layer.adaLN_modulation.1.bias"]
 __lowerCamelCase		:							Dict							=						state_dict["final_layer.linear.weight"]
 __lowerCamelCase		:							List[Any]							=						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
 __lowerCamelCase		:							Union[str, Any]							=						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__						)

 __lowerCamelCase		:							List[str]							=						DDIMScheduler(
     num_train_timesteps=1_000			,       beta_schedule='linear'			,       prediction_type='epsilon'			,       clip_sample=SCREAMING_SNAKE_CASE__			,       )

 __lowerCamelCase		:							Optional[int]							=						AutoencoderKL.from_pretrained(args.vae_model						)

 __lowerCamelCase		:							Optional[Any]							=						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__":
       lowercase_           =	argparse.ArgumentParser()

       parser.add_argument(
           '--image_size',
           default=2_5_6,
           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.'
       )

       lowercase_           =	parser.parse_args()
       main(args)









"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCAmelCase     :     Any            =	logging.get_logger(__name__)




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =['''pixel_values''']
   def __init__(						self	:     str				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Dict[str, int]] = None				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     bool = True				,       snake_case__	:     Union[int, float] = 1 / 2_5_5				,       snake_case__	:     bool = True				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        super().__init__(**snake_case__				)
        UpperCAmelCase__		:  Dict				=				size if size is not None else {"height": 2_2_4, "width": 2_2_4}
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)
        UpperCAmelCase__		:  Optional[Any]				=				do_resize
        UpperCAmelCase__		:  Any				=				do_rescale
        UpperCAmelCase__		:  Optional[int]				=				do_normalize
        UpperCAmelCase__		:  Dict				=				size
        UpperCAmelCase__		:  Optional[Any]				=				resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor
        UpperCAmelCase__		:  str				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCAmelCase__		:  Dict				=				image_std if image_std is not None else IMAGENET_STANDARD_STD
   def 	__a				(						self	:     Optional[Any]				,       snake_case__	:     np.ndarray				,       snake_case__	:     Dict[str, int]				,       snake_case__	:     PILImageResampling = PILImageResampling.BILINEAR				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Dict				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				get_size_dict(snake_case__				)
        if "height" not in size or "width" not in size:
             raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'				)
        return resize(
            snake_case__				,       size=(size["height"], size["width"])				,       resample=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     Any				,       snake_case__	:     np.ndarray				,       snake_case__	:     float				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     Any				):


        '''simple docstring'''
        return rescale(snake_case__				,       scale=snake_case__				,       data_format=snake_case__				,       **snake_case__				)
   def 	__a				(						self	:     str				,       snake_case__	:     np.ndarray				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Union[float, List[float]]				,       snake_case__	:     Optional[Union[str, ChannelDimension]] = None				,       **snake_case__	:     List[Any]				,       ):


        '''simple docstring'''
        return normalize(snake_case__				,       mean=snake_case__				,       std=snake_case__				,       data_format=snake_case__				,       **snake_case__				)




   def 	__a				(						self	:     Union[str, Any]				,       snake_case__	:     ImageInput				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Dict[str, int] = None				,       snake_case__	:     PILImageResampling = None				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[float] = None				,       snake_case__	:     Optional[bool] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[float, List[float]]] = None				,       snake_case__	:     Optional[Union[str, TensorType]] = None				,       snake_case__	:     Union[str, ChannelDimension] = ChannelDimension.FIRST				,       **snake_case__	:     List[Any]				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Union[str, Any]				=				do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase__		:  Optional[Any]				=				do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase__		:  str				=				do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase__		:  Dict				=				resample if resample is not None else self.resample
        UpperCAmelCase__		:  List[str]				=				rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase__		:  Optional[int]				=				image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase__		:  Optional[int]				=				image_std if image_std is not None else self.image_std

        UpperCAmelCase__		:  Any				=				size if size is not None else self.size
        UpperCAmelCase__		:  Optional[Any]				=				get_size_dict(snake_case__				)

        UpperCAmelCase__		:  List[Any]				=				make_list_of_images(snake_case__				)

        if not valid_images(snake_case__				):
             raise ValueError(
                 "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                 "torch.Tensor, tf.Tensor or jax.ndarray."				)

        if do_resize and size is None:
             raise ValueError("Size must be specified if do_resize is True."				)

        if do_rescale and rescale_factor is None:
             raise ValueError("Rescale factor must be specified if do_rescale is True."				)

        # All transformations expect numpy arrays.
        UpperCAmelCase__		:  Optional[int]				=				[to_numpy_array(snake_case__				) for image in images]

        if do_resize:
             UpperCAmelCase__		:  List[Any]				=				[self.resize(image=snake_case__				,       size=snake_case__				,       resample=snake_case__				) for image in images]

        if do_rescale:
             UpperCAmelCase__		:  Tuple				=				[self.rescale(image=snake_case__				,       scale=snake_case__				) for image in images]

        if do_normalize:
             UpperCAmelCase__		:  List[Any]				=				[self.normalize(image=snake_case__				,       mean=snake_case__				,       std=snake_case__				) for image in images]

        UpperCAmelCase__		:  Any				=				[to_channel_dimension_format(snake_case__				,       snake_case__				) for image in images]

        UpperCAmelCase__		:  str				=				{"pixel_values": images}
        return BatchFeature(data=snake_case__				,       tensor_type=snake_case__				)


