




'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		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  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		Dict:
			A_		,				A_       		=   emb.weight.shape
			A_       		=   nn.Linear(lowercase_					,lowercase_					,bias=lowercase_    )
			A_       		=   emb.weight.data
			return lin_layer





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_="uclanlp/plbart-base"					,SCREAMING_SNAKE_CASE_=False					,SCREAMING_SNAKE_CASE_=False    )		->		Union[str, Any]:
			A_       		=   torch.load(lowercase_					,map_location='cpu'    )['model']
			remove_ignore_keys_(lowercase_    )
			A_       		=   state_dict['encoder.embed_tokens.weight'].shape[0]

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

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

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

			return model


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE =	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"
							)
							_SCREAMING_SNAKE_CASE =	parser.parse_args()
							_SCREAMING_SNAKE_CASE =	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'''
from unittest.mock import patch

import datasets
from datasets import Dataset





def  lowerCamelCase(       )		->		Any:
			A_       		=   Dataset.from_dict({'col_1': [3, 2, 0, 1]}    )

			with patch('tqdm.auto.tqdm'    ) as mock_tqdm:
						datasets.disable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_not_called()

						mock_tqdm.reset_mock()

						datasets.enable_progress_bar()
						dset.map(lambda SCREAMING_SNAKE_CASE_    : {"col_2": x["col_1"] + 1}    )
						mock_tqdm.assert_called()




