
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 						UpperCAmelCase      (     a_						)		->     Dict:







    """simple docstring"""



    __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(a_		,       a_						)


def 						UpperCAmelCase      (     a_						)		->     int:







    """simple docstring"""



    __A      ,							__A             =			emb.weight.shape
    __A             =			nn.Linear(a_		,       a_		,       bias=a_						)
    __A             =			emb.weight.data
    return lin_layer


def 						UpperCAmelCase      (     a_						)		->     Tuple:







    """simple docstring"""



    __A             =			torch.load(a_		,       map_location="cpu"						)
    __A             =			mam_aaa["args"] or mam_aaa["cfg"]["model"]
    __A             =			mam_aaa["model"]
    remove_ignore_keys_(a_						)
    __A             =			state_dict["encoder.embed_tokens.weight"].shape[0]

    __A             =			MaMaaaConfig(
        vocab_size=a_		,       max_position_embeddings=1_0_2_4		,       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(a_						)
    model.model.load_state_dict(a_		,       strict=a_						)
    __A             =			make_linear_from_emb(model.model.shared						)

    return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE							:Optional[Any]										=    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.')
     SCREAMING_SNAKE_CASE							:Union[str, Any]										=    parser.parse_args()
     SCREAMING_SNAKE_CASE							:List[Any]										=    convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="text-classification"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"text": Value("string"  )}  )
	snake_case_    =					Features({"labels": ClassLabel}  )
	snake_case_    =					"text"
	snake_case_    =					"labels"






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Dict			):
					if self.label_column not in features:
									raise ValueError(f'''Column {self.label_column} is not present in features.'''			)
					if not isinstance(features[self.label_column]							,A			):
									raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.label_schema.copy()
					__A             =			features[self.label_column]
					__A             =			label_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			Dict			):
					return {
					    self.text_column: "text",
					    self.label_column: "labels",
					}
