
'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def A				(__lowerCamelCase				:int		):
						_lowerCAmelCase  						=			[
						    """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(__lowerCamelCase			,     __lowerCamelCase		)


def A				(__lowerCamelCase				:Optional[Any]		):
						_lowerCAmelCase						,   _lowerCAmelCase  						=			emb.weight.shape
						_lowerCAmelCase  						=			nn.Linear(__lowerCamelCase			,     __lowerCamelCase			,     bias=__lowerCamelCase		)
						_lowerCAmelCase  						=			emb.weight.data
						return lin_layer


def A				(__lowerCamelCase				:Tuple		):
						_lowerCAmelCase  						=			torch.load(__lowerCamelCase			,     map_location="""cpu"""		)
						_lowerCAmelCase  						=			mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
						_lowerCAmelCase  						=			mam_aaa["""model"""]
						remove_ignore_keys_(__lowerCamelCase		)
						_lowerCAmelCase  						=			state_dict["""encoder.embed_tokens.weight"""].shape[0]

						_lowerCAmelCase  						=			MaMaaaConfig(
						    vocab_size=__lowerCamelCase			,     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"""			,     )

						_lowerCAmelCase  						=			state_dict["""decoder.embed_tokens.weight"""]
						_lowerCAmelCase  						=			MaMaaaForConditionalGeneration(__lowerCamelCase		)
						model.model.load_state_dict(__lowerCamelCase			,     strict=__lowerCamelCase		)
						_lowerCAmelCase  						=			make_linear_from_emb(model.model.shared		)

						return model


if __name__ == "__main__":
						_lowercase    =		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.""")
						_lowercase    =		parser.parse_args()
						_lowercase    =		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)



'''simple docstring'''



# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_lowercase    =		"""2.13.1"""

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("""3.7"""):
						raise ImportWarning(
						    """To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    """To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
						    """If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_lowercase    =		concatenate_datasets
_lowercase    =		DownloadConfig
_lowercase    =		DownloadManager
_lowercase    =		DownloadMode
_lowercase    =		DownloadConfig
_lowercase    =		DownloadMode
_lowercase    =		DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager
