


"""simple docstring"""


import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
_snake_case          =							logging.get_logger(__name__)



def   snake_case						(				_a:     str							)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__           =		torch.load(_a		,   map_location='cpu'							)
      if "model" in sd.keys():
            lowerCamelCase__           =		torch.load(_a		,   map_location='cpu'							)['model']

      # pop unnecessary weights
      lowerCamelCase__           =		[
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_a							)

      lowerCamelCase__           =		{
          '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:
                  lowerCamelCase__           =		sd.pop(_a							)

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

                  lowerCamelCase__           =		key.replace('.qkv_proj.'		,   '.q_proj.'							)
                  lowerCamelCase__           =		key.replace('.qkv_proj.'		,   '.k_proj.'							)
                  lowerCamelCase__           =		key.replace('.qkv_proj.'		,   '.v_proj.'							)

                  lowerCamelCase__           =		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
                  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		torch.split(_a		,   depth // 3		,   dim=0							)

                  lowerCamelCase__           =		q
                  lowerCamelCase__           =		k
                  lowerCamelCase__           =		v
                  del sd[key]

      return sd



@torch.no_grad()
def   snake_case						(				_a:     Union[str, Any]		,   _a:     str		,   _a:     Tuple=None							)->		Dict:
      '''simple docstring'''


      lowerCamelCase__           =		load_checkpoint(_a							)

      if config is not None:
            lowerCamelCase__           =		OPTConfig.from_pretrained(_a							)
      else:
            lowerCamelCase__           =		OPTConfig()

      lowerCamelCase__           =		OPTModel(_a							).half().eval()
      model.load_state_dict(_a							)

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


if __name__ == "__main__":
   _snake_case          =							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.")
   _snake_case          =							parser.parse_args()
   convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)







"""simple docstring"""


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
   from .camera import create_pan_cameras
   from .pipeline_shap_e import ShapEPipeline
   from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
   from .renderer import (
       BoundingBoxVolume,
       ImportanceRaySampler,
       MLPNeRFModelOutput,
       MLPNeRSTFModel,
       ShapEParamsProjModel,
       ShapERenderer,
       StratifiedRaySampler,
       VoidNeRFModel,
   )



