




"""simple docstring"""


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer
class snake_case      (  tf.keras.layers.Layer):







		def __init__(					self      :       str    ,   a__      :       Dict[str, int]    ,   a__      :       List[str]    ,   a__      :       int = None    ,   a__      :       int = None		)       ->						Optional[Any]:





						'''simple docstring'''







						super().__init__()
						_A					  =			pad_token_id
						_A					  =			max_length
						_A					  =			vocab
						_A					  =			merges
						_A					  =			BytePairTokenizer(a__    ,   a__    ,   sequence_length=a__		)







		@classmethod
		def  a_ (					cls      :       Optional[Any]    ,   a__      :       GPTaTokenizer    ,   *a__      :       Dict    ,   **a__      :       Tuple		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			[" ".join(a__		) for m in tokenizer.bpe_ranks.keys()]
						_A					  =			tokenizer.get_vocab()
						return cls(a__    ,   a__    ,   *a__    ,   **a__		)







		@classmethod
		def  a_ (					cls      :       List[str]    ,   a__      :       Union[str, os.PathLike]    ,   *a__      :       Tuple    ,   **a__      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			GPTaTokenizer.from_pretrained(a__    ,   *a__    ,   **a__		)
						return cls.from_tokenizer(a__    ,   *a__    ,   **a__		)







		@classmethod
		def  a_ (					cls      :       List[Any]    ,   a__      :       int		)       ->						Dict:





						'''simple docstring'''







						return cls(**a__		)







		def  a_ (					self      :       int		)       ->						int:





						'''simple docstring'''







						return {
						    "vocab": self.vocab,
						    "merges": self.merges,
						    "max_length": self.max_length,
						    "pad_token_id": self.pad_token_id,
						}







		def  a_ (					self      :       Optional[Any]    ,   a__      :       int    ,   a__      :       int = None		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			self.tf_tokenizer(a__		)
						_A					  =			tf.ones_like(a__		)

						if self.pad_token_id is not None:
										# pad the tokens up to max length
										_A					  =			max_length if max_length is not None else self.max_length

										if max_length is not None:
														_A  ,		_A					  =			pad_model_inputs(
														    a__    ,   max_seq_length=a__    ,   pad_value=self.pad_token_id		)

						return {"attention_mask": attention_mask, "input_ids": input_ids}



"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def      a__ (				__lowercase   )	->	Optional[int]:
				_A					  =			[
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				    "decoder.output_projection.weight",
				]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				_A  ,		_A					  =			emb.weight.shape
				_A					  =			nn.Linear(__lowercase     ,			__lowercase     ,			bias=__lowercase   )
				_A					  =			emb.weight.data
				return lin_layer






def      a__ (				__lowercase     ,			__lowercase="facebook/mbart-large-en-ro"     ,			__lowercase=False     ,			__lowercase=False   )	->	List[str]:
				_A					  =			torch.load(__lowercase     ,			map_location="cpu"   )["model"]
				remove_ignore_keys_(__lowercase   )
				_A					  =			state_dict["encoder.embed_tokens.weight"].shape[0]

				_A					  =			MBartConfig.from_pretrained(__lowercase     ,			vocab_size=__lowercase   )
				if mbart_aa and finetuned:
								_A					  =			"relu"

				_A					  =			state_dict["decoder.embed_tokens.weight"]
				_A					  =			MBartForConditionalGeneration(__lowercase   )
				model.model.load_state_dict(__lowercase   )

				if finetuned:
								_A					  =			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="bart.large, bart.large.cnn or a 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.")
		parser.add_argument(
		    "--hf_config",
		    default="facebook/mbart-large-cc25",
		    type=str,
		    help="Which huggingface architecture to use: mbart-large",
		)
		parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
		parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
		a_       							=    parser.parse_args()
		a_       							=    convert_fairseq_mbart_checkpoint_from_disk(
		    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
		)
		model.save_pretrained(args.pytorch_dump_folder_path)