





'''simple docstring'''


import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def    __a (     UpperCAmelCase = np.pi / 2       ,			UpperCAmelCase = np.pi / 2       ,			UpperCAmelCase = np.pi / 2    )      ->str:



			"""simple docstring"""







			A							  =      QuantumRegister(3       ,			"""qr"""    )  # Define the number of quantum bits
			A							  =      ClassicalRegister(1       ,			"""cr"""    )  # Define the number of classical bits

			A							  =      QuantumCircuit(UpperCamelCase__       ,			UpperCamelCase__    )  # Define the quantum circuit.

			# Build the circuit
			quantum_circuit.u(UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			0    )  # Quantum State to teleport
			quantum_circuit.h(1    )  # add hadamard gate
			quantum_circuit.cx(
			    1       ,			2    )  # add control gate with qubit 1 as control and 2 as target.
			quantum_circuit.cx(0       ,			1    )
			quantum_circuit.h(0    )
			quantum_circuit.cz(0       ,			2    )  # add control z gate.
			quantum_circuit.cx(1       ,			2    )

			quantum_circuit.measure([2]       ,			[0]    )  # measure the qubit.

			# Simulate the circuit using qasm simulator
			A							  =      Aer.get_backend("""aer_simulator"""    )
			A							  =      execute(UpperCamelCase__       ,			UpperCamelCase__       ,			shots=1000    )

			return job.result().get_counts(UpperCamelCase__    )


if __name__ == "__main__":
				print(
				    'Total count for teleported state is: '
				    f"{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}"
				)








'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




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



	"""simple docstring"""







	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )

	A							  =      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

	# Encoder
	for layer_index in range(config.num_layers    ):
		A							  =      f"""layers_{str(UpperCAmelCase    )}"""

		# Self-Attention
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

		if split_mlp_wi:
			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
		else:
			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

		# Assigning
		A							  =      tax_attention_key
		A							  =      tax_attention_out
		A							  =      tax_attention_query
		A							  =      tax_attention_value

		A							  =      tax_attention_layer_norm

		if split_mlp_wi:
			A							  =      tax_mlp_wi_a
			A							  =      tax_mlp_wi_a
		else:
			A							  =      tax_mlp_wi

		A							  =      tax_mlp_wo
		A							  =      tax_mlp_layer_norm

	# Only for layer 0:
	A							  =      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
	A							  =      tax_encoder_rel_embedding

	# Assigning
	A							  =      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
	A							  =      tax_encoder_norm

	# Decoder
	for layer_index in range(config.num_decoder_layers    ):
		A							  =      f"""layers_{str(UpperCAmelCase    )}"""

		# Self-Attention
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
		    """scale"""
		]

		# Encoder-Decoder-Attention
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
		    """kernel"""
		]
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
		    """kernel"""
		]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

		# MLP
		if split_mlp_wi:
			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
		else:
			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

		# Layer Normalization
		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

		# Assigning
		A							  =      tax_attention_key
		A							  =      tax_attention_out
		A							  =      tax_attention_query
		A							  =      tax_attention_value

		A							  =      tax_pre_attention_layer_norm

		A							  =      tax_enc_dec_attention_key
		A							  =      tax_enc_dec_attention_out
		A							  =      tax_enc_dec_attention_query
		A							  =      tax_enc_dec_attention_value

		A							  =      tax_cross_layer_norm

		if split_mlp_wi:
			A							  =      tax_mlp_wi_a
			A							  =      tax_mlp_wi_a
		else:
			A							  =      tax_mlp_wi

		A							  =      tax_mlp_wo

		A							  =      txa_mlp_layer_norm

	# Decoder Normalization
	A							  =      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
	A							  =      txa_decoder_norm

	# Only for layer 0:
	A							  =      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
	A							  =      tax_decoder_rel_embedding

	# Token Embeddings
	A							  =      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
	A							  =      txa_token_embeddings

	# LM Head (only in v1.1 checkpoints)
	if "logits_dense" in tax_model["target"]["decoder"]:
		A							  =      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

	flax_model.save_pretrained(UpperCAmelCase    )
	print("""T5X Model was sucessfully converted!"""    )


if __name__ == "__main__":
				_lowerCamelCase				:	Tuple				    =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
				)
				parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
				parser.add_argument(
				    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
				)
				_lowerCamelCase				:	Union[str, Any]				    =       parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)

