





'''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() and is_transformers_version(">=", "4.25.0")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
  from .pipeline_unclip import UnCLIPPipeline
  from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
  from .text_proj import UnCLIPTextProjModel






'''simple docstring'''

import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    if "model" in sd.keys():
        A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)["model"]

    # pop unnecessary weights
    A__    =      [
        "decoder.version",
        "decoder.output_projection.weight",
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(_lowerCamelCase	)

    A__    =      {
        "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__    =      sd.pop(_lowerCamelCase	)

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

            A__    =      key.replace(".qkv_proj."						,	".q_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".k_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".v_proj."	)

            A__    =      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__    =      torch.split(_lowerCamelCase						,	depth // 3						,	dim=0	)

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

    return sd





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Dict=None	):
    A__    =      load_checkpoint(_lowerCamelCase	)

    if config is not None:
        A__    =      OPTConfig.from_pretrained(_lowerCamelCase	)
    else:
        A__    =      OPTConfig()

    A__    =      OPTModel(_lowerCamelCase	).half().eval()
    model.load_state_dict(_lowerCamelCase	)

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


if __name__ == "__main__":
  __lowerCAmelCase							:     List[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.")
  __lowerCAmelCase							:     List[Any]		     =parser.parse_args()
  convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



