




'''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_1_2: '''DiT-XL-2-512x512.pt''', 2_5_6: '''DiT-XL-2-256x256.pt'''}







def      _UpperCamelCase	(_lowerCamelCase							:       List[Any]		)->				str:




     '''simple docstring'''





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







def      _UpperCamelCase	(_lowerCamelCase							:       int		)->				Tuple:




     '''simple docstring'''





     __snake_case         =					download_model(pretrained_models[args.image_size]		)

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

          __snake_case         =					state_dict[
              f'''blocks.{depth}.adaLN_modulation.1.weight'''
          ]
          __snake_case         =					state_dict[
              f'''blocks.{depth}.adaLN_modulation.1.bias'''
          ]

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

          __snake_case         =					q
          __snake_case         =					q_bias
          __snake_case         =					k
          __snake_case         =					k_bias
          __snake_case         =					v
          __snake_case         =					v_bias

          __snake_case         =					state_dict[
              f'''blocks.{depth}.attn.proj.weight'''
          ]
          __snake_case         =					state_dict[f'''blocks.{depth}.attn.proj.bias''']

          __snake_case         =					state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
          __snake_case         =					state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
          __snake_case         =					state_dict[f'''blocks.{depth}.mlp.fc2.weight''']
          __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'''		)

     __snake_case         =					state_dict['''final_layer.adaLN_modulation.1.weight''']
     __snake_case         =					state_dict['''final_layer.adaLN_modulation.1.bias''']
     __snake_case         =					state_dict['''final_layer.linear.weight''']
     __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
     __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=_lowerCamelCase				,		num_attention_heads=16				,		activation_fn='''gelu-approximate'''				,		num_embeds_ada_norm=10_00				,		norm_type='''ada_norm_zero'''				,		norm_elementwise_affine=_lowerCamelCase				,		)
     transformer.load_state_dict(_lowerCamelCase				,		strict=_lowerCamelCase		)

     __snake_case         =					DDIMScheduler(
         num_train_timesteps=10_00				,		beta_schedule='''linear'''				,		prediction_type='''epsilon'''				,		clip_sample=_lowerCamelCase				,		)

     __snake_case         =					AutoencoderKL.from_pretrained(args.vae_model		)

     __snake_case         =					DiTPipeline(transformer=_lowerCamelCase				,		vae=_lowerCamelCase				,		scheduler=_lowerCamelCase		)

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


if __name__ == "__main__":
     UpperCAmelCase_    :					Optional[Any]					      =						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.'''
     )

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












'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Dict		)->				Dict:




     '''simple docstring'''





     __snake_case         =					TaConfig.from_pretrained(_lowerCamelCase		)
     __snake_case         =					FlaxTaForConditionalGeneration(config=_lowerCamelCase		)
     __snake_case         =					checkpoints.load_tax_checkpoint(_lowerCamelCase		)

     __snake_case         =					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_attention_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo
          __snake_case         =					tax_mlp_layer_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_encoder_rel_embedding

     # Assigning
     __snake_case         =					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     __snake_case         =					tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

          # MLP
          if split_mlp_wi:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
               __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_pre_attention_layer_norm

          __snake_case         =					tax_enc_dec_attention_key
          __snake_case         =					tax_enc_dec_attention_out
          __snake_case         =					tax_enc_dec_attention_query
          __snake_case         =					tax_enc_dec_attention_value

          __snake_case         =					tax_cross_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo

          __snake_case         =					txa_mlp_layer_norm

     # Decoder Normalization
     __snake_case         =					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     __snake_case         =					txa_decoder_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_decoder_rel_embedding

     # Token Embeddings
     __snake_case         =					tax_model['''target''']['''token_embedder''']['''embedding''']
     __snake_case         =					txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          __snake_case         =					tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

     flax_model.save_pretrained(_lowerCamelCase		)
     print('''T5X Model was sucessfully converted!'''		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[str]					      =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the TX5 checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     UpperCAmelCase_    :					List[Any]					      =						parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






