


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase	: Any  				={'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: int  				=[
						    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMSNModel',
						    'ViTMSNForImageClassification',
						    'ViTMSNPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_msn import (
												    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMSNForImageClassification,
												    ViTMSNModel,
												    ViTMSNPreTrainedModel,
												)

else:
						import sys

						__lowerCAmelCase	: Union[str, Any]  				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase      :       Any									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

       # pop unnecessary weights
       a__						:	Optional[Any] =				[
           'decoder.version',
           'decoder.output_projection.weight',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(A__				)

       a__						:	Union[str, Any] =				{
           'decoder.project_in_dim.weight': 'decoder.project_in.weight',
           'decoder.project_out_dim.weight': 'decoder.project_out.weight',
           'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
           'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     a__						:	Any =				sd.pop(A__				)

       a__						:	Optional[int] =				list(sd.keys()				)
       for key in keys:
              if ".qkv_proj." in key:
                     a__						:	str =				sd[key]
                     # We split QKV in separate Q,K,V

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     a__ ,							a__ ,							a__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

       # Check results
       Path(A__				).mkdir(exist_ok=A__				)
       model.save_pretrained(A__				)


if __name__ == "__main__":
    lowercase      :       str									=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--fairseq_path""",
        type=str,
        help=(
            """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
            """ https://huggingface.co/models?other=opt_metasq"""
        ),
    )
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


