





"""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__				  =   logging.get_logger(__name__)




def 					lowercase__    (					lowercase_			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	int						=       torch.load(lowercase_		,map_location="cpu"			)
      if "model" in sd.keys():
            _UpperCamelCase      :	Union[str, Any]						=       torch.load(lowercase_		,map_location="cpu"			)["model"]

      # pop unnecessary weights
      _UpperCamelCase      :	List[Any]						=       [
          "decoder.version",
          "decoder.output_projection.weight",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(lowercase_			)

      _UpperCamelCase      :	Optional[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:
                  _UpperCamelCase      :	str						=       sd.pop(lowercase_			)

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

                  _UpperCamelCase      :	Tuple						=       key.replace(".qkv_proj."		,".q_proj."			)
                  _UpperCamelCase      :	Any						=       key.replace(".qkv_proj."		,".k_proj."			)
                  _UpperCamelCase      :	Tuple						=       key.replace(".qkv_proj."		,".v_proj."			)

                  _UpperCamelCase      :	Optional[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
                  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	str						=       torch.split(lowercase_		,depth // 3		,dim=0			)

                  _UpperCamelCase      :	int						=       q
                  _UpperCamelCase      :	Optional[Any]						=       k
                  _UpperCamelCase      :	int						=       v
                  del sd[key]

      return sd




@torch.no_grad()
def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_=None			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       load_checkpoint(lowercase_			)

      if config is not None:
            _UpperCamelCase      :	Tuple						=       OPTConfig.from_pretrained(lowercase_			)
      else:
            _UpperCamelCase      :	Union[str, Any]						=       OPTConfig()

      _UpperCamelCase      :	List[Any]						=       OPTModel(lowercase_			).half().eval()
      model.load_state_dict(lowercase_			)

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


if __name__ == "__main__":
  lowerCamelCase__				  =   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__				  =   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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase__				  =   {
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   ["OwlViTFeatureExtractor"]
  lowerCamelCase__				  =   ["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   [
      "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OwlViTModel",
      "OwlViTPreTrainedModel",
      "OwlViTTextModel",
      "OwlViTVisionModel",
      "OwlViTForObjectDetection",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

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