





import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def  a_       (							_lowercase     ):
						_UpperCamelCase    :      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(lowercase_ ,		lowercase_     )





def  a_       (							_lowercase     ):
						_UpperCamelCase    :      List[str]						     =					emb.weight.shape
						_UpperCamelCase    :      Dict						     =					nn.Linear(lowercase_ ,		lowercase_ ,		bias=lowercase_     )
						_UpperCamelCase    :      Tuple						     =					emb.weight.data
						return lin_layer





def  a_       (							_lowercase ,		_lowercase="uclanlp/plbart-base" ,		_lowercase=False ,		_lowercase=False     ):
						_UpperCamelCase    :      Optional[int]						     =					torch.load(lowercase_ ,		map_location='''cpu'''     )['''model''']
						remove_ignore_keys_(lowercase_     )
						_UpperCamelCase    :      str						     =					state_dict['''encoder.embed_tokens.weight'''].shape[0]

						_UpperCamelCase    :      Any						     =					PLBartConfig.from_pretrained(lowercase_ ,		vocab_size=lowercase_     )

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

						else:
												_UpperCamelCase    :      List[str]						     =					{}
												for key, value in state_dict.copy().items():
																		if key.startswith('''classification_heads.sentence_classification_head'''     ):
																								_UpperCamelCase    :      Tuple						     =					value
																								state_dict.pop(lowercase_     )
												_UpperCamelCase    :      int						     =					PLBartForSequenceClassification(lowercase_     )
												model.model.load_state_dict(lowercase_     )
												model.classification_head.load_state_dict(lowercase_     )

						return model


if __name__ == "__main__":
						UpperCamelCase_     =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"""
						)
						UpperCamelCase_     =parser.parse_args()
						UpperCamelCase_     =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"""




UpperCamelCase_     =1_000





def  a_       (							_lowercase     ):
						return x * x





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase = 4     ):
						assert callable(
						    _lowercase     ), F"""the function(object) passed should be callable your input : {function}"""
						assert isinstance(_lowercase ,		(float, int)     ), F"""a should be float or integer your input : {a}"""
						assert isinstance(function(_lowercase     ) ,		(float, int)     ), (
						    "the function should return integer or float return type of your function, "
						    F"""{type(_lowercase     )}"""
						)
						assert isinstance(_lowercase ,		(float, int)     ), F"""b should be float or integer your input : {b}"""
						assert (
						    isinstance(_lowercase ,		_lowercase     ) and precision > 0
						), F"""precision should be positive integer your input : {precision}"""

						# just applying the formula of simpson for approximate integration written in
						# mentioned article in first comment of this file and above this function

						_UpperCamelCase    :      str						     =					(b - a) / N_STEPS
						_UpperCamelCase    :      Dict						     =					function(_lowercase     ) + function(_lowercase     )

						for i in range(1 ,		_lowercase     ):
												_UpperCamelCase    :      List[Any]						     =					a + h * i
												result += function(_lowercase     ) * (4 if i % 2 else 2)

						result *= h / 3
						return round(_lowercase ,		_lowercase     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



