





'''simple docstring'''
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
    a__							:							List[str] =argparse.ArgumentParser()

    parser.add_argument(
        '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
    )
    parser.add_argument(
        '''--original_config_file''',
        type=str,
        required=True,
        help='''The YAML config file corresponding to the original architecture.''',
    )
    parser.add_argument(
        '''--num_in_channels''',
        default=None,
        type=int,
        help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
    )
    parser.add_argument(
        '''--image_size''',
        default=512,
        type=int,
        help=(
            '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
            ''' Base. Use 768 for Stable Diffusion v2.'''
        ),
    )
    parser.add_argument(
        '''--extract_ema''',
        action='''store_true''',
        help=(
            '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
            ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
            ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
        ),
    )
    parser.add_argument(
        '''--upcast_attention''',
        action='''store_true''',
        help=(
            '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
            ''' diffusion 2.1.'''
        ),
    )
    parser.add_argument(
        '''--from_safetensors''',
        action='''store_true''',
        help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
    )
    parser.add_argument(
        '''--to_safetensors''',
        action='''store_true''',
        help='''Whether to store pipeline in safetensors format or not.''',
    )
    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
    parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
    def 				lowercase__   (   __lowercase						:  Any     ) ->      int:



           """simple docstring"""






           if string == "True":
                  return True
           elif string == "False":
                  return False
           else:
                  raise ValueError(F'''could not parse string as bool {string}'''     )

    parser.add_argument(
        '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
    )

    parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

    a__							:							List[Any] =parser.parse_args()

    a__							:							Tuple =download_controlnet_from_original_ckpt(
        checkpoint_path=args.checkpoint_path,
        original_config_file=args.original_config_file,
        image_size=args.image_size,
        extract_ema=args.extract_ema,
        num_in_channels=args.num_in_channels,
        upcast_attention=args.upcast_attention,
        from_safetensors=args.from_safetensors,
        device=args.device,
        use_linear_projection=args.use_linear_projection,
        cross_attention_dim=args.cross_attention_dim,
    )


    controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           '_float_tensor',
           'decoder.output_projection.weight',
       ]
       for k in ignore_keys:
              state_dict.pop(__lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       =  emb.weight.shape
       __UpperCamelCase       =  nn.Linear(__lowercase				,  __lowercase				,  bias=__lowercase     )
       __UpperCamelCase       =  emb.weight.data
       return lin_layer
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[str]="facebook/mbart-large-en-ro"				,  __lowercase						:  str=False				,  __lowercase						:  List[Any]=False     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )['model']
       remove_ignore_keys_(__lowercase     )
       __UpperCamelCase       =  state_dict['encoder.embed_tokens.weight'].shape[0]

       __UpperCamelCase       =  MBartConfig.from_pretrained(__lowercase				,  vocab_size=__lowercase     )
       if mbart_aa and finetuned:
              __UpperCamelCase       =  'relu'

       __UpperCamelCase       =  state_dict['decoder.embed_tokens.weight']
       __UpperCamelCase       =  MBartForConditionalGeneration(__lowercase     )
       model.model.load_state_dict(__lowercase     )

       if finetuned:
              __UpperCamelCase       =  make_linear_from_emb(model.model.shared     )

       return model


if __name__ == "__main__":
    a__							:							Dict =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
    )
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--hf_config''',
        default='''facebook/mbart-large-cc25''',
        type=str,
        help='''Which huggingface architecture to use: mbart-large''',
    )
    parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
    parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
    a__							:							Union[str, Any] =parser.parse_args()
    a__							:							str =convert_fairseq_mbart_checkpoint_from_disk(
        args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
    )
    model.save_pretrained(args.pytorch_dump_folder_path)
