



"""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_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			torch.load(_lowercase,					map_location="""cpu"""	)
						if "model" in sd.keys():
												snake_case_ :str						=			torch.load(_lowercase,					map_location="""cpu"""	)["""model"""]

						# pop unnecessary weights
						snake_case_ :Tuple						=			[
						    """decoder.version""",
						    """decoder.output_projection.weight""",
						]
						for key in keys_to_delete:
												if key in sd:
																		sd.pop(_lowercase	)

						snake_case_ :str						=			{
						    """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_ :List[Any]						=			sd.pop(_lowercase	)

						snake_case_ :Tuple						=			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_ :Dict						=			key.replace(""".qkv_proj.""",					""".q_proj."""	)
																		snake_case_ :Optional[Any]						=			key.replace(""".qkv_proj.""",					""".k_proj."""	)
																		snake_case_ :Optional[Any]						=			key.replace(""".qkv_proj.""",					""".v_proj."""	)

																		snake_case_ :Dict						=			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_,       snake_case_,       snake_case_ :Any						=			torch.split(_lowercase,					depth // 3,					dim=0	)

																		snake_case_ :List[Any]						=			q
																		snake_case_ :Union[str, Any]						=			k
																		snake_case_ :Optional[int]						=			v
																		del sd[key]

						return sd
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase,					_lowercase=None	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			load_checkpoint(_lowercase	)

						if config is not None:
												snake_case_ :List[str]						=			OPTConfig.from_pretrained(_lowercase	)
						else:
												snake_case_ :List[Any]						=			OPTConfig()

						snake_case_ :str						=			OPTModel(_lowercase	).half().eval()
						model.load_state_dict(_lowercase	)

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


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)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowerCAmelCase__      		= {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechEncoderDecoderModel']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
    from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
    import sys

    lowerCAmelCase__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
