





'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification




def  __a			(  UpperCAmelCase     )   ->Any:



	"""simple docstring"""







	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(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE     )




def  __a			(  UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A	,     A					=   emb.weight.shape
	A					=   nn.Linear(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      bias=_SCREAMING_SNAKE_CASE     )
	A					=   emb.weight.data
	return lin_layer




def  __a			(  UpperCAmelCase							,      UpperCAmelCase="uclanlp/plbart-base"							,      UpperCAmelCase=False							,      UpperCAmelCase=False     )   ->Optional[int]:



	"""simple docstring"""







	A					=   torch.load(_SCREAMING_SNAKE_CASE							,      map_location="""cpu"""     )["""model"""]
	remove_ignore_keys_(_SCREAMING_SNAKE_CASE     )
	A					=   state_dict["""encoder.embed_tokens.weight"""].shape[0]

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

	A					=   state_dict["""decoder.embed_tokens.weight"""]
	if not classification:
		A					=   PLBartForConditionalGeneration(_SCREAMING_SNAKE_CASE     )
		model.model.load_state_dict(_SCREAMING_SNAKE_CASE     )
		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(_SCREAMING_SNAKE_CASE     )
		A					=   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__":
				_lowerCamelCase					:						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'
				)
				_lowerCamelCase					:						Any					     =							parser.parse_args()
				_lowerCamelCase					:						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 doctest

import projectq
from projectq.ops import H, Measure







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					int:
							snake_case_     							=		quantum_engine.allocate_qubit()
							H | qubit
							Measure | qubit
							return int(_SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				__SCREAMING_SNAKE_CASE				:     Dict		=     projectq.MainEngine()

				# Generate a list of 10 random numbers
				__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     [get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print('Random numbers', random_numbers_list)



