"""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					:					Dict=			logging.get_logger(__name__)
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      Tuple:

	'''simple docstring'''





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

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

	__snake_case      :							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:
			__snake_case      :							Tuple						=			sd.pop(__SCREAMING_SNAKE_CASE  )

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

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

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

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

	return sd
@torch.no_grad()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    Union[str, Any]=None  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Optional[Any]						=			load_checkpoint(__SCREAMING_SNAKE_CASE  )

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

	__snake_case      :							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					:					Dict=			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					:					Tuple=			parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)

"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


_a					:					Tuple=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):



				def __init__(self    :				int     ,  *_A    :				str     ,  **_A    :				List[str])   ->     None:
					warnings.warn(
					    'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use VideoMAEImageProcessor instead.'     ,  _A     ,  )
					super().__init__(*_A     ,  **_A)





