




'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification






def 	_lowercase	(			lowercase_						):



		'''simple docstring'''







		_snake_case			 = [
		    "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 	_lowercase	(			lowercase_						):



		'''simple docstring'''







		_snake_case ,					_snake_case			 = emb.weight.shape
		_snake_case			 = nn.Linear(lowercase_		,							lowercase_		,							bias=lowercase_						)
		_snake_case			 = emb.weight.data
		return lin_layer






def 	_lowercase	(			lowercase_		,							lowercase_="uclanlp/plbart-base"		,							lowercase_=False		,							lowercase_=False						):



		'''simple docstring'''







		_snake_case			 = torch.load(lowercase_		,							map_location="cpu"						)["model"]
		remove_ignore_keys_(lowercase_						)
		_snake_case			 = state_dict["encoder.embed_tokens.weight"].shape[0]

		_snake_case			 = PLBartConfig.from_pretrained(lowercase_		,							vocab_size=lowercase_						)

		_snake_case			 = state_dict["decoder.embed_tokens.weight"]
		if not classification:
				_snake_case			 = PLBartForConditionalGeneration(lowercase_						)
				model.model.load_state_dict(lowercase_						)
				if finetuned:
						_snake_case			 = make_linear_from_emb(model.model.shared						)

		else:
				_snake_case			 = {}
				for key, value in state_dict.copy().items():
						if key.startswith("classification_heads.sentence_classification_head"						):
								_snake_case			 = value
								state_dict.pop(lowercase_						)
				_snake_case			 = PLBartForSequenceClassification(lowercase_						)
				model.model.load_state_dict(lowercase_						)
				model.classification_head.load_state_dict(lowercase_						)

		return model


if __name__ == "__main__":
						_lowerCamelCase							:		Dict										=						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							:		Any										=						parser.parse_args()
						_lowerCamelCase							:		str										=						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)











'''simple docstring'''







import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir





@dataclass
class   lowerCamelCase_							:




					'''simple docstring'''






					lowercase__					=				42





class   lowerCamelCase_							( unittest.TestCase							):




					'''simple docstring'''






					def        __SCREAMING_SNAKE_CASE			(					self				):
							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece.model"				)
							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"				)

							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)

							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
									_snake_case			 = SpmConverter(__UpperCamelCase				)
							self.assertEqual(len(__UpperCamelCase				),						0				)

							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)

							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
									_snake_case			 = SpmConverter(__UpperCamelCase				)
							self.assertEqual(len(__UpperCamelCase				),						1				)

							self.assertIn(
							    "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
							    " which is not implemented in the fast tokenizers.",						str(w[0].message				),						)





