

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def 			_UpperCAmelCase							(		a__) ->    Any:

			'''simple docstring'''



			a_		:		Any				    =				[
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """_float_tensor""",
			    """decoder.output_projection.weight""",
			]
			for k in ignore_keys:
						state_dict.pop(a__       ,					a__)


def 			_UpperCAmelCase							(		a__) ->    Optional[int]:

			'''simple docstring'''



			a_    ,			a_		:		Optional[Any]				    =				emb.weight.shape
			a_		:		List[Any]				    =				nn.Linear(a__       ,					a__       ,					bias=a__)
			a_		:		int				    =				emb.weight.data
			return lin_layer


def 			_UpperCAmelCase							(		a__       ,					a__="uclanlp/plbart-base"       ,					a__=False       ,					a__=False) ->    Union[str, Any]:

			'''simple docstring'''



			a_		:		Union[str, Any]				    =				torch.load(a__       ,					map_location="""cpu""")["""model"""]
			remove_ignore_keys_(a__)
			a_		:		Optional[Any]				    =				state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_		:		Tuple				    =				PLBartConfig.from_pretrained(a__       ,					vocab_size=a__)

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

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

			return model


if __name__ == "__main__":
	__snake_case							:     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"""
	)
	__snake_case							:     int 						=	parser.parse_args()
	__snake_case							:     Optional[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)





import sys

from transformers import AutoTokenizer


__snake_case							:     int 						=	sys.argv[1]
__snake_case							:     Dict 						=	sys.argv[2]
__snake_case							:     Dict 						=	int(sys.argv[3])

__snake_case							:     Union[str, Any] 						=	0

__snake_case							:     Any 						=	AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, """rt""") as f_p:
	for line in f_p:
		__snake_case							:     str 						=	line.rstrip()

		if not line:
			print(line)
			__snake_case							:     Optional[int] 						=	0
			continue

		__snake_case							:     Dict 						=	line.split()[0]

		__snake_case							:     str 						=	len(tokenizer.tokenize(token))

		# Token contains strange control characters like \x96 or \x95
		# Just filter out the complete line
		if current_subwords_len == 0:
			continue

		if (subword_len_counter + current_subwords_len) > max_len:
			print("""""")
			print(line)
			__snake_case							:     Optional[int] 						=	current_subwords_len
			continue

		subword_len_counter += current_subwords_len

		print(line)


