
'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification




def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : Dict       =						[
					    '''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			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_, lowerCamelCase_   : int       =						emb.weight.shape
					lowerCamelCase_   : str       =						nn.Linear(_lowercase     ,    _lowercase     ,    bias=_lowercase			)
					lowerCamelCase_   : Optional[int]       =						emb.weight.data
					return lin_layer




def lowercase_		(				_lowercase     ,    _lowercase="uclanlp/plbart-base"     ,    _lowercase=False     ,    _lowercase=False			)       ->							Dict:
					'''simple docstring'''

					lowerCamelCase_   : Dict       =						torch.load(_lowercase     ,    map_location='''cpu'''			)['''model''']
					remove_ignore_keys_(_lowercase			)
					lowerCamelCase_   : Dict       =						state_dict['''encoder.embed_tokens.weight'''].shape[0]

					lowerCamelCase_   : Dict       =						PLBartConfig.from_pretrained(_lowercase     ,    vocab_size=_lowercase			)

					lowerCamelCase_   : str       =						state_dict['''decoder.embed_tokens.weight''']
					if not classification:
										lowerCamelCase_   : Any       =						PLBartForConditionalGeneration(_lowercase			)
										model.model.load_state_dict(_lowercase			)
										if finetuned:
															lowerCamelCase_   : Optional[int]       =						make_linear_from_emb(model.model.shared			)

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

					return model


if __name__ == "__main__":
					__lowercase							:				str					=       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'''
					)
					__lowercase							:				Tuple					=       parser.parse_args()
					__lowercase							:				List[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 argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowercase							:				Optional[Any]					=       logging.getLogger()




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						argparse.ArgumentParser()
					parser.add_argument('''-f'''			)
					lowerCamelCase_   : Optional[int]       =						parser.parse_args()
					return args.f






class 		__lowercase			(					_lowercase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						logging.StreamHandler(sys.stdout )
								logger.addHandler(A )

								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(A     ,				'''argv'''     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						run_glue_with_pabee.main()
													for value in result.values():
																		self.assertGreaterEqual(A     ,				0.75 )


