




import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__UpperCamelCase					:       Optional[Any]    					=      logging.get_logger(__name__)





def 		__A		(	__lowerCamelCase    )      ->       Tuple:
	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 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None    )      ->       List[Any]:
	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__":
		__UpperCamelCase					:       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.")
		__UpperCamelCase					:       int    					=      parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase					:       Optional[Any]    					=      {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase					:       List[Any]    					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

