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,
)


A_      =      {
    "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:
      A_      =      ["OwlViTFeatureExtractor"]
      A_      =      ["OwlViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_      =      [
          "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

      A_      =      _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()
A_      =      logging.get_logger(__name__)






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







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

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

			lowercase							=		{
			    '''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:
									lowercase							=		sd.pop(UpperCAmelCase       )

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

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

									lowercase							=		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
									lowercase		,lowercase		,lowercase							=		torch.split(UpperCAmelCase,		depth // 3,		dim=0       )

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

			return sd






@torch.no_grad()
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None       )->       int:



			"""simple docstring"""







			lowercase							=		load_checkpoint(UpperCAmelCase       )

			if config is not None:
						lowercase							=		OPTConfig.from_pretrained(UpperCAmelCase       )
			else:
						lowercase							=		OPTConfig()

			lowercase							=		OPTModel(UpperCAmelCase       ).half().eval()
			model.load_state_dict(UpperCAmelCase       )

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


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)





