

"""simple docstring"""


import argparse
from pathlib import Path

import torch

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


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






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Tuple:
  __lowerCAmelCase:	int   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)
  if "model" in sd.keys():
    __lowerCAmelCase:	Union[str, Any]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)["model"]

  # pop unnecessary weights
  __lowerCAmelCase:	Optional[int]   				=      [
      "decoder.version",
      "decoder.output_projection.weight",
  ]
  for key in keys_to_delete:
    if key in sd:
      sd.pop(__SCREAMING_SNAKE_CASE			)

  __lowerCAmelCase:	int   				=      {
      "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:	Tuple   				=      sd.pop(__SCREAMING_SNAKE_CASE			)

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

      __lowerCAmelCase:	Optional[Any]   				=      key.replace(".qkv_proj." ,			".q_proj."			)
      __lowerCAmelCase:	Dict   				=      key.replace(".qkv_proj." ,			".k_proj."			)
      __lowerCAmelCase:	List[str]   				=      key.replace(".qkv_proj." ,			".v_proj."			)

      __lowerCAmelCase:	Tuple   				=      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:	Dict   				=      torch.split(__SCREAMING_SNAKE_CASE ,			depth // 3 ,			dim=0			)

      __lowerCAmelCase:	str   				=      q
      __lowerCAmelCase:	Dict   				=      k
      __lowerCAmelCase:	Tuple   				=      v
      del sd[key]

  return sd






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

  if config is not None:
    __lowerCAmelCase:	Union[str, Any]   				=      OPTConfig.from_pretrained(__SCREAMING_SNAKE_CASE			)
  else:
    __lowerCAmelCase:	List[str]   				=      OPTConfig()

  __lowerCAmelCase:	Optional[Any]   				=      OPTModel(__SCREAMING_SNAKE_CASE			).half().eval()
  model.load_state_dict(__SCREAMING_SNAKE_CASE			)

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


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





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A						       =      {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Swinv2ForImageClassification",
			    "Swinv2ForMaskedImageModeling",
			    "Swinv2Model",
			    "Swinv2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			__A						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


