

'''simple docstring'''


from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


lowerCAmelCase_   :   List[str]       =					logging.get_logger(__name__)


lowerCAmelCase_   :   Dict       =					TypeVar("DatasetType", Dataset, IterableDataset)







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     str = None					,  __lowerCamelCase  :     Tuple = None					,  __lowerCamelCase  :     Optional[int] = None					,  __lowerCamelCase  :     int = None					,  __lowerCamelCase  :     str = "first_exhausted"					,  )   ->    str:
			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."     )
			for i, dataset in enumerate(__lowerCamelCase     ):
						if not isinstance(__lowerCamelCase					,  (Dataset, IterableDataset)     ):
									if isinstance(__lowerCamelCase					,  (DatasetDict, IterableDatasetDict)     ):
												if not dataset:
															raise ValueError(
															    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
															    "is an empty dataset dictionary."     )
												raise ValueError(
												    f'''Dataset at position {i} has at least one split: {list(__lowerCamelCase     )}\n'''
												    f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowerCamelCase     )     )}\']'''     )
									raise ValueError(
									    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCamelCase     ).__name__}.'''     )
						if i == 0:
									a__   ,				a__     = (
									    (Dataset, IterableDataset) if isinstance(__lowerCamelCase					,  __lowerCamelCase     ) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
									raise ValueError(
									    f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''     )
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(f'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'''     )
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  info=__lowerCamelCase					,  split=__lowerCamelCase					,  stopping_strategy=__lowerCamelCase     )
			else:
						return _interleave_iterable_datasets(
						    __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  info=__lowerCamelCase					,  split=__lowerCamelCase					,  stopping_strategy=__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Any = None					,  __lowerCamelCase  :     int = None					,  __lowerCamelCase  :     List[Any] = 0					,  )   ->    str:

			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."     )
			for i, dataset in enumerate(__lowerCamelCase     ):
						if not isinstance(__lowerCamelCase					,  (Dataset, IterableDataset)     ):
									if isinstance(__lowerCamelCase					,  (DatasetDict, IterableDatasetDict)     ):
												if not dataset:
															raise ValueError(
															    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '''
															    "is an empty dataset dictionary."     )
												raise ValueError(
												    f'''Dataset at position {i} has at least one split: {list(__lowerCamelCase     )}\n'''
												    f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowerCamelCase     )     )}\']'''     )
									raise ValueError(
									    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCamelCase     ).__name__}.'''     )
						if i == 0:
									a__   ,				a__     = (
									    (Dataset, IterableDataset) if isinstance(__lowerCamelCase					,  __lowerCamelCase     ) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
									raise ValueError(
									    f'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'''     )
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(__lowerCamelCase					,  info=__lowerCamelCase					,  split=__lowerCamelCase					,  axis=__lowerCamelCase     )
			else:
						return _concatenate_iterable_datasets(__lowerCamelCase					,  info=__lowerCamelCase					,  split=__lowerCamelCase					,  axis=__lowerCamelCase     )










'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    Optional[int]:
			a__     = [
			    "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    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[Any]:
			a__   ,				a__     = emb.weight.shape
			a__     = nn.Linear(__lowerCamelCase					,  __lowerCamelCase					,  bias=__lowerCamelCase     )
			a__     = emb.weight.data
			return lin_layer







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"] or mam_aaa["cfg"]["model"]
			a__     = mam_aaa["model"]
			remove_ignore_keys_(__lowerCamelCase     )
			a__     = state_dict["encoder.embed_tokens.weight"].shape[0]

			a__     = 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"					,  )

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

			return model


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



