'''simple docstring'''




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_UpperCamelCase				:		Optional[Any]       			=		{5_12: 'DiT-XL-2-512x512.pt', 2_56: 'DiT-XL-2-256x256.pt'}



def 						__snake_case (       lowerCAmelCase       :     Tuple			):
 __UpperCAmelCase         =					F"""pretrained_models/{model_name}"""
 if not os.path.isfile(lowerCAmelCase			):
  os.makedirs('pretrained_models'      ,  exist_ok=lowerCAmelCase			)
  __UpperCAmelCase         =					F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
  download_url(lowerCAmelCase      ,  'pretrained_models'			)
 __UpperCAmelCase         =					torch.load(lowerCAmelCase      ,  map_location=lambda lowerCAmelCase      ,  lowerCAmelCase			: storage			)
 return model



def 						__snake_case (       lowerCAmelCase       :     List[Any]			):
 __UpperCAmelCase         =					download_model(pretrained_models[args.image_size]			)

 __UpperCAmelCase         =					state_dict['x_embedder.proj.weight']
 __UpperCAmelCase         =					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			):
  __UpperCAmelCase         =					state_dict[
      't_embedder.mlp.0.weight'
  ]
  __UpperCAmelCase         =					state_dict[
      't_embedder.mlp.0.bias'
  ]
  __UpperCAmelCase         =					state_dict[
      't_embedder.mlp.2.weight'
  ]
  __UpperCAmelCase         =					state_dict[
      't_embedder.mlp.2.bias'
  ]
  __UpperCAmelCase         =					state_dict[
      'y_embedder.embedding_table.weight'
  ]

  __UpperCAmelCase         =					state_dict[
      F"""blocks.{depth}.adaLN_modulation.1.weight"""
  ]
  __UpperCAmelCase         =					state_dict[
      F"""blocks.{depth}.adaLN_modulation.1.bias"""
  ]

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

  __UpperCAmelCase         =					q
  __UpperCAmelCase         =					q_bias
  __UpperCAmelCase         =					k
  __UpperCAmelCase         =					k_bias
  __UpperCAmelCase         =					v
  __UpperCAmelCase         =					v_bias

  __UpperCAmelCase         =					state_dict[
      F"""blocks.{depth}.attn.proj.weight"""
  ]
  __UpperCAmelCase         =					state_dict[F"""blocks.{depth}.attn.proj.bias"""]

  __UpperCAmelCase         =					state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
  __UpperCAmelCase         =					state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
  __UpperCAmelCase         =					state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
  __UpperCAmelCase         =					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'			)

 __UpperCAmelCase         =					state_dict['final_layer.adaLN_modulation.1.weight']
 __UpperCAmelCase         =					state_dict['final_layer.adaLN_modulation.1.bias']
 __UpperCAmelCase         =					state_dict['final_layer.linear.weight']
 __UpperCAmelCase         =					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
 __UpperCAmelCase         =					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=lowerCAmelCase      ,  num_attention_heads=16      ,  activation_fn='gelu-approximate'      ,  num_embeds_ada_norm=1000      ,  norm_type='ada_norm_zero'      ,  norm_elementwise_affine=lowerCAmelCase      ,  )
 transformer.load_state_dict(lowerCAmelCase      ,  strict=lowerCAmelCase			)

 __UpperCAmelCase         =					DDIMScheduler(
     num_train_timesteps=1000      ,  beta_schedule='linear'      ,  prediction_type='epsilon'      ,  clip_sample=lowerCAmelCase      ,  )

 __UpperCAmelCase         =					AutoencoderKL.from_pretrained(args.vae_model			)

 __UpperCAmelCase         =					DiTPipeline(transformer=lowerCAmelCase      ,  vae=lowerCAmelCase      ,  scheduler=lowerCAmelCase			)

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


if __name__ == "__main__":
     _UpperCamelCase				:		Dict       			=		argparse.ArgumentParser()

     parser.add_argument(
         '--image_size',
         default=2_56,
         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[str]       			=		parser.parse_args()
     main(args)




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase				:		Any       			=		logging.get_logger(__name__)

_UpperCamelCase				:		Any       			=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  '''mobilevitv2'''





   def __init__(							self:    Union[str, Any]			,a:    Optional[int]=3			,a:    Any=256			,a:    str=2			,a:    Any=2.0			,a:    Union[str, Any]="swish"			,a:    List[str]=3			,a:    List[str]=32			,a:    int=0.1			,a:    List[Any]=0.02			,a:    List[Any]=1e-5			,a:    Tuple=512			,a:    Optional[Any]=[6, 12, 18]			,a:    Dict=0.1			,a:    List[Any]=255			,a:    Union[str, Any]=[2, 4, 3]			,a:    str=[128, 192, 256]			,a:    int=1.0			,a:    Optional[int]=2			,a:    Optional[Any]=0.0			,a:    int=0.0			,**a:    Optional[Any]			,):
    super().__init__(**a	)

    __UpperCAmelCase         =					num_channels
    __UpperCAmelCase         =					image_size
    __UpperCAmelCase         =					patch_size
    __UpperCAmelCase         =					expand_ratio
    __UpperCAmelCase         =					hidden_act
    __UpperCAmelCase         =					conv_kernel_size
    __UpperCAmelCase         =					output_stride
    __UpperCAmelCase         =					initializer_range
    __UpperCAmelCase         =					layer_norm_eps
    __UpperCAmelCase         =					n_attn_blocks
    __UpperCAmelCase         =					base_attn_unit_dims
    __UpperCAmelCase         =					width_multiplier
    __UpperCAmelCase         =					ffn_multiplier
    __UpperCAmelCase         =					ffn_dropout
    __UpperCAmelCase         =					attn_dropout
    __UpperCAmelCase         =					classifier_dropout_prob

    # decode head attributes for semantic segmentation
    __UpperCAmelCase         =					aspp_out_channels
    __UpperCAmelCase         =					atrous_rates
    __UpperCAmelCase         =					aspp_dropout_prob
    __UpperCAmelCase         =					semantic_loss_ignore_index




class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  version.parse('''1.11'''				)





   @property
   def 			snake_case	(							self:    Optional[int]	):
    return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]	)





   @property
   def 			snake_case	(							self:    Tuple	):
    if self.task == "image-classification":
     return OrderedDict([('logits', {0: 'batch'})]	)
    else:
     return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]	)





   @property
   def 			snake_case	(							self:    Union[str, Any]	):
    return 1e-4



