




import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_		):
    a__      =       RoFormerConfig.from_json_file(UpperCAmelCase_		)
    print(F"""Building PyTorch model from configuration: {config}"""		)
    a__      =       RoFormerForMaskedLM(UpperCAmelCase_		)

    # Load weights from tf checkpoint
    load_tf_weights_in_roformer(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_		)

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""		)
    torch.save(model.state_dict()   ,    UpperCAmelCase_   ,    _use_new_zipfile_serialization=UpperCAmelCase_		)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_						:					Any			   =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--bert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained BERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    SCREAMING_SNAKE_CASE_						:					Tuple			   =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


SCREAMING_SNAKE_CASE_						:					Tuple			   =       {5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       F"""pretrained_models/{model_name}"""
    if not os.path.isfile(UpperCAmelCase_		):
        os.makedirs("""pretrained_models"""   ,    exist_ok=UpperCAmelCase_		)
        a__      =       F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
        download_url(UpperCAmelCase_   ,    """pretrained_models"""		)
    a__      =       torch.load(UpperCAmelCase_   ,    map_location=lambda UpperCAmelCase_   ,    UpperCAmelCase_		: storage		)
    return model
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       download_model(pretrained_models[args.image_size]		)

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

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

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

        a__      =       q
        a__      =       q_bias
        a__      =       k
        a__      =       k_bias
        a__      =       v
        a__      =       v_bias

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

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

    a__      =       state_dict["""final_layer.adaLN_modulation.1.weight"""]
    a__      =       state_dict["""final_layer.adaLN_modulation.1.bias"""]
    a__      =       state_dict["""final_layer.linear.weight"""]
    a__      =       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
    a__      =       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=UpperCAmelCase_   ,    num_attention_heads=16   ,    activation_fn="""gelu-approximate"""   ,    num_embeds_ada_norm=1_000   ,    norm_type="""ada_norm_zero"""   ,    norm_elementwise_affine=UpperCAmelCase_   ,    )
    transformer.load_state_dict(UpperCAmelCase_   ,    strict=UpperCAmelCase_		)

    a__      =       DDIMScheduler(
        num_train_timesteps=1_000   ,    beta_schedule="""linear"""   ,    prediction_type="""epsilon"""   ,    clip_sample=UpperCAmelCase_   ,    )

    a__      =       AutoencoderKL.from_pretrained(args.vae_model		)

    a__      =       DiTPipeline(transformer=UpperCAmelCase_   ,    vae=UpperCAmelCase_   ,    scheduler=UpperCAmelCase_		)

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


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

 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       parser.parse_args()
 main(args)






