





from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
      import pyximport

      pyximport.install(setup_args={"""include_dirs""": np.get_include()})
      from . import algos_graphormer  # noqa E402


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[int]  ,				__lowercase:		int = 5_12):



  '''simple docstring'''






  __lowerCAmelCase								=					x.shape[1] if len(x.shape) > 1 else 1
  __lowerCAmelCase								=					1 + np.arange(0  ,				feature_num * offset  ,				__lowercase  ,				dtype=np.intaa)
  __lowerCAmelCase								=					x + feature_offset
  return x


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Union[str, Any]  ,				__lowercase:		Union[str, Any]=True):



  '''simple docstring'''






  requires_backends(__lowercase  ,				['''cython'''])

  if keep_features and "edge_attr" in item.keys():  # edge_attr
    __lowerCAmelCase								=					np.asarray(item['''edge_attr''']  ,				dtype=np.intaa)
  else:
    __lowerCAmelCase								=					np.ones((len(item['''edge_index'''][0]), 1)  ,				dtype=np.intaa)  # same embedding for all

  if keep_features and "node_feat" in item.keys():  # input_nodes
    __lowerCAmelCase								=					np.asarray(item['''node_feat''']  ,				dtype=np.intaa)
  else:
    __lowerCAmelCase								=					np.ones((item['''num_nodes'''], 1)  ,				dtype=np.intaa)  # same embedding for all

  __lowerCAmelCase								=					np.asarray(item['''edge_index''']  ,				dtype=np.intaa)

  __lowerCAmelCase								=					convert_to_single_emb(__lowercase) + 1
  __lowerCAmelCase								=					item['''num_nodes''']

  if len(edge_attr.shape) == 1:
    __lowerCAmelCase								=					edge_attr[:, None]
  __lowerCAmelCase								=					np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]  ,				dtype=np.intaa)
  __lowerCAmelCase								=					convert_to_single_emb(__lowercase) + 1

  # node adj matrix [num_nodes, num_nodes] bool
  __lowerCAmelCase								=					np.zeros([num_nodes, num_nodes]  ,				dtype=__lowercase)
  __lowerCAmelCase								=					True

  __lowerCAmelCase						,    __lowerCAmelCase								=					algos_graphormer.floyd_warshall(__lowercase)
  __lowerCAmelCase								=					np.amax(__lowercase)

  __lowerCAmelCase								=					algos_graphormer.gen_edge_input(__lowercase  ,				__lowercase  ,				__lowercase)
  __lowerCAmelCase								=					np.zeros([num_nodes + 1, num_nodes + 1]  ,				dtype=np.single)  # with graph token

  # combine
  __lowerCAmelCase								=					input_nodes + 1  # we shift all indices by one for padding
  __lowerCAmelCase								=					attn_bias
  __lowerCAmelCase								=					attn_edge_type
  __lowerCAmelCase								=					shortest_path_result.astype(np.intaa) + 1  # we shift all indices by one for padding
  __lowerCAmelCase								=					np.sum(__lowercase  ,				axis=1).reshape(-1) + 1  # we shift all indices by one for padding
  __lowerCAmelCase								=					item['''in_degree''']  # for undirected graph
  __lowerCAmelCase								=					input_edges + 1  # we shift all indices by one for padding
  if "labels" not in item:
    __lowerCAmelCase								=					item['''y''']

  return item




class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		Dict      ,    snake_case    :		List[Any]=20      ,    snake_case    :		List[str]=False				):

      """simple docstring"""





      if not is_cython_available():
        raise ImportError('''Graphormer preprocessing needs Cython (pyximport)'''				)

      __lowerCAmelCase								=					spatial_pos_max
      __lowerCAmelCase								=					on_the_fly_processing





    def __call__( self    :		List[str]      ,    snake_case    :		List[dict]				):

      """simple docstring"""





      if self.on_the_fly_processing:
        __lowerCAmelCase								=					[preprocess_item(snake_case				) for i in features]

      if not isinstance(features[0]      ,    snake_case				):
        __lowerCAmelCase								=					[vars(snake_case				) for f in features]
      __lowerCAmelCase								=					{}

      __lowerCAmelCase								=					max(len(i['''input_nodes''']				) for i in features				)
      __lowerCAmelCase								=					len(features[0]['''input_nodes'''][0]				)
      __lowerCAmelCase								=					len(features[0]['''attn_edge_type'''][0][0]				)
      __lowerCAmelCase								=					max(len(i['''input_edges'''][0][0]				) for i in features				)
      __lowerCAmelCase								=					len(features[0]['''input_edges'''][0][0][0]				)
      __lowerCAmelCase								=					len(snake_case				)

      __lowerCAmelCase								=					torch.zeros(snake_case      ,    max_node_num + 1      ,    max_node_num + 1      ,    dtype=torch.float				)
      __lowerCAmelCase								=					torch.zeros(snake_case      ,    snake_case      ,    snake_case      ,    snake_case      ,    dtype=torch.long				)
      __lowerCAmelCase								=					torch.zeros(snake_case      ,    snake_case      ,    snake_case      ,    dtype=torch.long				)
      __lowerCAmelCase								=					torch.zeros(snake_case      ,    snake_case      ,    dtype=torch.long				)
      __lowerCAmelCase								=					torch.zeros(snake_case      ,    snake_case      ,    snake_case      ,    dtype=torch.long				)
      __lowerCAmelCase								=					torch.zeros(
          snake_case      ,    snake_case      ,    snake_case      ,    snake_case      ,    snake_case      ,    dtype=torch.long				)

      for ix, f in enumerate(snake_case				):
        for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
          __lowerCAmelCase								=					torch.tensor(f[k]				)

        if len(f['''attn_bias'''][1:, 1:][f['''spatial_pos'''] >= self.spatial_pos_max]				) > 0:
          __lowerCAmelCase								=					float('''-inf'''				)

        __lowerCAmelCase								=					f['''attn_bias''']
        __lowerCAmelCase								=					f[
            '''attn_edge_type'''
        ]
        __lowerCAmelCase								=					f['''spatial_pos''']
        __lowerCAmelCase								=					f['''in_degree''']
        __lowerCAmelCase								=					f['''input_nodes''']
        __lowerCAmelCase								=					f['''input_edges''']

      __lowerCAmelCase								=					batch['''in_degree''']

      __lowerCAmelCase								=					features[0]['''labels''']
      if len(snake_case				) == 1:  # one task
        if isinstance(sample[0]      ,    snake_case				):  # regression
          __lowerCAmelCase								=					torch.from_numpy(np.concatenate([i['''labels'''] for i in features]				)				)
        else:  # binary classification
          __lowerCAmelCase								=					torch.from_numpy(np.concatenate([i['''labels'''] for i in features]				)				)
      else:  # multi task classification, left to float to keep the NaNs
        __lowerCAmelCase								=					torch.from_numpy(np.stack([i['''labels'''] for i in features]      ,    axis=0				)				)

      return batch







import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__lowercase				    =      {512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					download_model(pretrained_models[args.image_size])

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

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.bias'''
    ]

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

    __lowerCAmelCase								=					q
    __lowerCAmelCase								=					q_bias
    __lowerCAmelCase								=					k
    __lowerCAmelCase								=					k_bias
    __lowerCAmelCase								=					v
    __lowerCAmelCase								=					v_bias

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.attn.proj.weight'''
    ]
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.attn.proj.bias''']

    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
    __lowerCAmelCase								=					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								=					state_dict['''final_layer.adaLN_modulation.1.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.bias''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.weight''']
  __lowerCAmelCase								=					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								=					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=__lowercase  ,				num_attention_heads=16  ,				activation_fn='''gelu-approximate'''  ,				num_embeds_ada_norm=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

  __lowerCAmelCase								=					DDIMScheduler(
      num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				prediction_type='''epsilon'''  ,				clip_sample=__lowercase  ,				)

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

  __lowerCAmelCase								=					DiTPipeline(transformer=__lowercase  ,				vae=__lowercase  ,				scheduler=__lowercase)

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


if __name__ == "__main__":
      __lowercase				    =      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."""
      )

      __lowercase				    =      parser.parse_args()
      main(args)
