





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def  A_(      A ):
	if "model" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""model."""					,   """""" )
	if "norm1" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""norm1"""					,   """attention.output.LayerNorm""" )
	if "norm2" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""norm2"""					,   """output.LayerNorm""" )
	if "norm" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""norm"""					,   """LayerNorm""" )
	if "transformer" in orig_key:
		UpperCAmelCase_		    =  orig_key.split(""".""" )[0].split("""_""" )[-1]
		UpperCAmelCase_		    =  orig_key.replace(f"""transformer_{layer_num}"""					,   f"""encoder.layer.{layer_num}""" )
	if "mha.attn" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""mha.attn"""					,   """attention.self""" )
	if "mha" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""mha"""					,   """attention""" )
	if "W_q" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""W_q"""					,   """self.query""" )
	if "W_k" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""W_k"""					,   """self.key""" )
	if "W_v" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""W_v"""					,   """self.value""" )
	if "ff.0" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""ff.0"""					,   """intermediate.dense""" )
	if "ff.2" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""ff.2"""					,   """output.dense""" )
	if "ff" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""ff"""					,   """output.dense""" )
	if "mlm_class" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""mlm.mlm_class"""					,   """cls.predictions.decoder""" )
	if "mlm" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""mlm"""					,   """cls.predictions.transform""" )
	if "backbone.backbone.encoders" in orig_key:
		UpperCAmelCase_		    =  orig_key.replace("""backbone.backbone.encoders"""					,   """encoder.layer""" )
	if "cls" not in orig_key:
		UpperCAmelCase_		    =  """mra.""" + orig_key

	return orig_key




def  A_(      A					,   A ):
	for key in orig_state_dict.copy().keys():
		UpperCAmelCase_		    =  orig_state_dict.pop(A )

		if ("pooler" in key) or ("sen_class" in key):
			continue
		else:
			UpperCAmelCase_		    =  val

	UpperCAmelCase_		    =  orig_state_dict["""cls.predictions.decoder.bias"""]
	UpperCAmelCase_		    =  torch.arange(A ).expand((1, -1) ) + 2

	return orig_state_dict




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  torch.load(A					,   map_location="""cpu""" )["""model_state_dict"""]
	UpperCAmelCase_		    =  MraConfig.from_json_file(A )
	UpperCAmelCase_		    =  MraForMaskedLM(A )

	UpperCAmelCase_		    =  convert_checkpoint_helper(config.max_position_embeddings					,   A )

	print(model.load_state_dict(A ) )
	model.eval()
	model.save_pretrained(A )

	print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
				UpperCamelCase__ :			str       	=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
				)
				parser.add_argument(
				    """--config_file""",
				    default=None,
				    type=str,
				    required=True,
				    help="""The json file for Mra model config.""",
				)
				parser.add_argument(
				    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				UpperCamelCase__ :			Tuple       	=			parser.parse_args()
				convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









import doctest

import projectq
from projectq.ops import H, Measure




def  A_(      A ):
	UpperCAmelCase_		    =  quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(A )


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

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

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

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

				print("""Random numbers""", random_numbers_list)


