

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  Tuple		       =   [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	    "decoder.output_projection.weight",
	]
	for k in ignore_keys:
		state_dict.pop(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)



def  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[int]		       =   emb.weight.shape
	UpperCAmelCase__:  List[str]		       =   nn.Linear(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,bias=SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  Union[str, Any]		       =   emb.weight.data
	return lin_layer



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE="uclanlp/plbart-base"  ,SCREAMING_SNAKE_CASE=False  ,SCREAMING_SNAKE_CASE=False			):
	UpperCAmelCase__:  List[str]		       =   torch.load(SCREAMING_SNAKE_CASE  ,map_location="cpu"			)["model"]
	remove_ignore_keys_(SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  int		       =   state_dict["encoder.embed_tokens.weight"].shape[0]

	UpperCAmelCase__:  List[Any]		       =   PLBartConfig.from_pretrained(SCREAMING_SNAKE_CASE  ,vocab_size=SCREAMING_SNAKE_CASE			)

	UpperCAmelCase__:  Any		       =   state_dict["decoder.embed_tokens.weight"]
	if not classification:
		UpperCAmelCase__:  Union[str, Any]		       =   PLBartForConditionalGeneration(SCREAMING_SNAKE_CASE			)
		model.model.load_state_dict(SCREAMING_SNAKE_CASE			)
		if finetuned:
			UpperCAmelCase__:  List[str]		       =   make_linear_from_emb(model.model.shared			)

	else:
		UpperCAmelCase__:  Tuple		       =   {}
		for key, value in state_dict.copy().items():
			if key.startswith("classification_heads.sentence_classification_head"			):
				UpperCAmelCase__:  Optional[int]		       =   value
				state_dict.pop(SCREAMING_SNAKE_CASE			)
		UpperCAmelCase__:  List[Any]		       =   PLBartForSequenceClassification(SCREAMING_SNAKE_CASE			)
		model.model.load_state_dict(SCREAMING_SNAKE_CASE			)
		model.classification_head.load_state_dict(SCREAMING_SNAKE_CASE			)

	return model


if __name__ == "__main__":
			_lowerCAmelCase     :			Union[str, Any]    =argparse.ArgumentParser()
			# Required parameters
			parser.add_argument("""fairseq_path""", type=str, help="""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="""uclanlp/plbart-base""",
			    type=str,
			    help="""Which huggingface architecture to use: plbart-base""",
			)
			parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
			parser.add_argument(
			    """--classification""", action="""store_true""", help="""whether the model is a classification checkpoint"""
			)
			_lowerCAmelCase     :			Tuple    =parser.parse_args()
			_lowerCAmelCase     :			List[Any]    =convert_fairseq_plbart_checkpoint_from_disk(
			    args.fairseq_path,
			    hf_config_path=args.hf_config,
			    finetuned=args.finetuned,
			    classification=args.classification,
			)
			model.save_pretrained(args.pytorch_dump_folder_path)


from __future__ import annotations



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	assert n > 1 and a > 0 and greatest_common_divisor(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			) == 1
	((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Optional[Any]		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
	UpperCAmelCase__:  List[str]		       =   (b * s) % n
	return x



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  int		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
	if b < 0:
		UpperCAmelCase__:  List[str]		       =   (b % n + n) % n
	return b



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  Union[str, Any]		       =   invert_modulo(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  Any		       =   (b * s) % n
	return x



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	assert a >= 0 and b >= 0

	if b == 0:
		UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   a, 1, 0
	else:
		((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Tuple		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,a % b			)
		UpperCAmelCase__:  Union[str, Any]		       =   q
		UpperCAmelCase__:  Union[str, Any]		       =   p - q * (a // b)

	assert a % d == 0 and b % d == 0
	assert d == a * x + b * y

	return (d, x, y)



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	if b == 0:
		return (1, 0)
	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  List[str]		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,a % b			)
	UpperCAmelCase__:  Optional[Any]		       =   a // b
	return (y, x - k * y)



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	if a < b:
		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   b, a

	while a % b != 0:
		UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   b, a % b

	return b


if __name__ == "__main__":
					from doctest import testmod

					testmod(name="""modular_division""", verbose=True)
					testmod(name="""modular_division2""", verbose=True)
					testmod(name="""invert_modulo""", verbose=True)
					testmod(name="""extended_gcd""", verbose=True)
					testmod(name="""extended_euclid""", verbose=True)
					testmod(name="""greatest_common_divisor""", verbose=True)