


"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Any:
							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(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							snake_case_							,    snake_case_     							=		emb.weight.shape
							snake_case_     							=		nn.Linear(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			bias=_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		emb.weight.data
							return lin_layer







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE="uclanlp/plbart-base"					,			_SCREAMING_SNAKE_CASE=False					,			_SCREAMING_SNAKE_CASE=False					)     ->					Optional[int]:
							snake_case_     							=		torch.load(_SCREAMING_SNAKE_CASE					,			map_location="""cpu"""					)["""model"""]
							remove_ignore_keys_(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		state_dict["""encoder.embed_tokens.weight"""].shape[0]

							snake_case_     							=		PLBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE					,			vocab_size=_SCREAMING_SNAKE_CASE					)

							snake_case_     							=		state_dict["""decoder.embed_tokens.weight"""]
							if not classification:
														snake_case_     							=		PLBartForConditionalGeneration(_SCREAMING_SNAKE_CASE					)
														model.model.load_state_dict(_SCREAMING_SNAKE_CASE					)
														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(_SCREAMING_SNAKE_CASE					)
														snake_case_     							=		PLBartForSequenceClassification(_SCREAMING_SNAKE_CASE					)
														model.model.load_state_dict(_SCREAMING_SNAKE_CASE					)
														model.classification_head.load_state_dict(_SCREAMING_SNAKE_CASE					)

							return model


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     Optional[int]		=     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				:     Any		=     parser.parse_args()
				__SCREAMING_SNAKE_CASE				:     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 math







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					list[int]:
							if not isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		f"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE					)
							if number < 1:
														snake_case_     							=		f"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		[number]
							while number != 1:
														if number % 2 == 0:
																					snake_case_     							=		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														else:
																					snake_case_     							=		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														sequence.append(_SCREAMING_SNAKE_CASE					)
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



