


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[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(UpperCamelCase__     ,   UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__       ,     __magic_name__				:					Union[str, Any]						       =       emb.weight.shape
				__magic_name__				:					Optional[int]						       =       nn.Linear(UpperCamelCase__     ,   UpperCamelCase__     ,   bias=UpperCamelCase__		)
				__magic_name__				:					Tuple						       =       emb.weight.data
				return lin_layer



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__="uclanlp/plbart-base"     ,   UpperCamelCase__=False     ,   UpperCamelCase__=False		):






				"""simple docstring"""



				__magic_name__				:					Tuple						       =       torch.load(UpperCamelCase__     ,   map_location="cpu"		)["model"]
				remove_ignore_keys_(UpperCamelCase__		)
				__magic_name__				:					int						       =       state_dict["encoder.embed_tokens.weight"].shape[0]

				__magic_name__				:					List[Any]						       =       PLBartConfig.from_pretrained(UpperCamelCase__     ,   vocab_size=UpperCamelCase__		)

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

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

				return model


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE       :   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"
		)
		_SCREAMING_SNAKE_CASE       :   Optional[Any]        = parser.parse_args()
		_SCREAMING_SNAKE_CASE       :   Union[str, 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)



'''simple docstring'''







import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	List[Any]:
										return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"     )







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										__magic_name__				:					Tuple						       =       self.tokenizer_ab
										__magic_name__				:					Optional[int]						       =       " I am a small frog."
										__magic_name__				:					Optional[int]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					int						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Any:
										__magic_name__				:					Tuple						       =       self.rust_tokenizer_ab
										__magic_name__				:					str						       =       " I am a small frog."
										__magic_name__				:					Union[str, Any]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					Any						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	str:
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]







						def lowerCAmelCase__ (  self:	List[str]     )							->	List[Any]:
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]