




"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def        _UpperCamelCase     (      UpperCamelCase				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			List[Any]												=       [
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase					,							UpperCamelCase				)

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""
			__UpperCAmelCase						,  __UpperCAmelCase						:			List[Any]												=       emb.weight.shape
			__UpperCAmelCase						:			Optional[Any]												=       nn.Linear(UpperCamelCase					,							UpperCamelCase					,							bias=UpperCamelCase				)
			__UpperCAmelCase						:			Optional[Any]												=       emb.weight.data
			return lin_layer

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Dict:


			"""simple docstring"""
			__UpperCAmelCase						:			Any												=       torch.load(UpperCamelCase					,							map_location="cpu"				)
			__UpperCAmelCase						:			Union[str, Any]												=       mam_aaa["args"] or mam_aaa["cfg"]["model"]
			__UpperCAmelCase						:			Optional[Any]												=       mam_aaa["model"]
			remove_ignore_keys_(UpperCamelCase				)
			__UpperCAmelCase						:			int												=       state_dict["encoder.embed_tokens.weight"].shape[0]

			__UpperCAmelCase						:			int												=       MaMaaaConfig(
			    vocab_size=UpperCamelCase					,							max_position_embeddings=1024					,							encoder_layers=args.encoder_layers					,							decoder_layers=args.decoder_layers					,							encoder_attention_heads=args.encoder_attention_heads					,							decoder_attention_heads=args.decoder_attention_heads					,							encoder_ffn_dim=args.encoder_ffn_embed_dim					,							decoder_ffn_dim=args.decoder_ffn_embed_dim					,							d_model=args.encoder_embed_dim					,							encoder_layerdrop=args.encoder_layerdrop					,							decoder_layerdrop=args.decoder_layerdrop					,							dropout=args.dropout					,							attention_dropout=args.attention_dropout					,							activation_dropout=args.activation_dropout					,							activation_function="relu"					,							)

			__UpperCAmelCase						:			Optional[int]												=       state_dict["decoder.embed_tokens.weight"]
			__UpperCAmelCase						:			List[str]												=       MaMaaaForConditionalGeneration(UpperCamelCase				)
			model.model.load_state_dict(UpperCamelCase					,							strict=UpperCamelCase				)
			__UpperCAmelCase						:			Any												=       make_linear_from_emb(model.model.shared				)

			return model


if __name__ == "__main__":
					A				=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
					parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					A				=							parser.parse_args()
					A				=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)








import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__A								=       {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}







def      lowerCamelCase_						(			UpperCamelCase__							: List[str]					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: Any					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: int )	->			List[Any]:





				"""simple docstring"""


				if got_ver is None or want_ver is None:
								raise ValueError(
								    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
								    F""" reinstalling {pkg}.""" )
				if not ops[op](version.parse(UpperCamelCase__ )					,	version.parse(UpperCamelCase__ ) ):
								raise ImportError(
								    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: Optional[str] = None )	->			None:





				"""simple docstring"""


				__lowerCamelCase									=       F"""\n{hint}""" if hint is not None else ''

				# non-versioned check
				if re.match(R'^[\w_\-\d]+$'					,	UpperCamelCase__ ):
								__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       requirement, None, None
				else:
								__lowerCamelCase									=       re.findall(R'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'					,	UpperCamelCase__ )
								if not match:
												raise ValueError(
												    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
												    F""" got {requirement}""" )
								__lowerCamelCase			,		__lowerCamelCase									=       match[0]
								__lowerCamelCase									=       want_full.split(',' )  # there could be multiple requirements
								__lowerCamelCase									=       {}
								for w in want_range:
												__lowerCamelCase									=       re.findall(R'^([\s!=<>]{1,2})(.+)'					,	UpperCamelCase__ )
												if not match:
																raise ValueError(
																    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
																    F""" but got {requirement}""" )
												__lowerCamelCase			,		__lowerCamelCase									=       match[0]
												__lowerCamelCase									=       want_ver
												if op not in ops:
																raise ValueError(F"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )

    # special case
				if pkg == "python":
								__lowerCamelCase									=       '.'.join([str(UpperCamelCase__ ) for x in sys.version_info[:3]] )
								for op, want_ver in wanted.items():
												_compare_versions(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ )
								return

				# check if any version is installed
				try:
								__lowerCamelCase									=       importlib.metadata.version(UpperCamelCase__ )
				except importlib.metadata.PackageNotFoundError:
								raise importlib.metadata.PackageNotFoundError(
								    F"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )

				# check that the right version is installed if version number or a range was provided
				if want_ver is not None:
								for op, want_ver in wanted.items():
												_compare_versions(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ )







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[int] )	->			Optional[int]:





				"""simple docstring"""


				__lowerCamelCase									=       'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
				return require_version(UpperCamelCase__					,	UpperCamelCase__ )


