



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						int:
				a__	:							str      						= AutoConfig.from_pretrained(__UpperCamelCase )
				a__	:							Optional[Any]      						= FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCamelCase )
				a__	:							Tuple      						= checkpoints.load_tax_checkpoint(__UpperCamelCase )

				a__	:							str      						= "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

				if config.model_type == "t5":
								a__	:							Optional[int]      						= "SelfAttention"
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								a__	:							Union[str, Any]      						= "LocalSelfAttention"
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								a__	:							Tuple      						= "TransientGlobalSelfAttention"
				else:
								raise ValueError(
								    "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
								    " attribute with a value from ['local', 'transient-global]." )

				# Encoder
				for layer_index in range(config.num_layers ):
								a__	:							Dict      						= F'layers_{str(__UpperCamelCase )}'

								# Self-Attention
								a__	:							Union[str, Any]      						= tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
								a__	:							List[Any]      						= tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
								a__	:							str      						= tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
								a__	:							str      						= tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												a__	:							Optional[int]      						= tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

								# Layer Normalization
								a__	:							int      						= tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

								if split_mlp_wi:
												a__	:							int      						= tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
												a__	:							Any      						= tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
								else:
												a__	:							Dict      						= tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

								a__	:							Optional[Any]      						= tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

								# Layer Normalization
								a__	:							Tuple      						= tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

								# Assigning
								a__	:							List[Any]      						= flax_model.params["encoder"]["block"][str(__UpperCamelCase )]["layer"]
								a__	:							int      						= tax_attention_key
								a__	:							Dict      						= tax_attention_out
								a__	:							str      						= tax_attention_query
								a__	:							Any      						= tax_attention_value

								a__	:							Optional[int]      						= tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												a__	:							List[str]      						= tax_global_layer_norm

								if split_mlp_wi:
												a__	:							Union[str, Any]      						= tax_mlp_wi_a
												a__	:							Optional[int]      						= tax_mlp_wi_a
								else:
												a__	:							List[str]      						= tax_mlp_wi

								a__	:							Dict      						= tax_mlp_wo
								a__	:							Dict      						= tax_mlp_layer_norm

								a__	:							str      						= flax_model_encoder_layer_block

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

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								a__	:							List[str]      						= tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
								a__	:							List[Any]      						= tax_encoder_global_rel_embedding

				# Assigning
				a__	:							Optional[int]      						= tax_model["target"]["encoder"]["encoder_norm"]["scale"]
				a__	:							Dict      						= tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers ):
								a__	:							str      						= F'layers_{str(__UpperCamelCase )}'

								# Self-Attention
								a__	:							int      						= tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
								a__	:							List[Any]      						= tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
								a__	:							List[str]      						= tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
								a__	:							Optional[Any]      						= tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

								# Layer Normalization
								a__	:							Optional[Any]      						= tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
								    "scale"
								]

								# Encoder-Decoder-Attention
								a__	:							int      						= tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
								a__	:							Any      						= tax_enc_dec_attention_module["key"]["kernel"]
								a__	:							Dict      						= tax_enc_dec_attention_module["out"]["kernel"]
								a__	:							Any      						= tax_enc_dec_attention_module["query"]["kernel"]
								a__	:							Optional[int]      						= tax_enc_dec_attention_module["value"]["kernel"]

								# Layer Normalization
								a__	:							Tuple      						= tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

								a__	:							str      						= tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

								# Layer Normalization
								a__	:							Any      						= tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

								# Assigning
								a__	:							Union[str, Any]      						= flax_model.params["decoder"]["block"][str(__UpperCamelCase )]["layer"]
								a__	:							str      						= tax_attention_key
								a__	:							Dict      						= tax_attention_out
								a__	:							Dict      						= tax_attention_query
								a__	:							Union[str, Any]      						= tax_attention_value

								a__	:							int      						= tax_pre_attention_layer_norm

								a__	:							int      						= tax_enc_dec_attention_key
								a__	:							Union[str, Any]      						= tax_enc_dec_attention_out
								a__	:							Optional[Any]      						= tax_enc_dec_attention_query
								a__	:							Any      						= tax_enc_dec_attention_value

								a__	:							List[str]      						= tax_cross_layer_norm

								if split_mlp_wi:
												a__	:							Optional[Any]      						= tax_mlp_wi_a
												a__	:							Union[str, Any]      						= tax_mlp_wi_a
								else:
												a__	:							Union[str, Any]      						= tax_mlp_wi

								a__	:							str      						= tax_mlp_wo

								a__	:							List[Any]      						= txa_mlp_layer_norm

								a__	:							Optional[int]      						= flax_model_decoder_layer_block

				# Decoder Normalization
				a__	:							List[str]      						= tax_model["target"]["decoder"]["decoder_norm"]["scale"]
				a__	:							Optional[int]      						= txa_decoder_norm

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

				# Token Embeddings
				a__	:							Optional[int]      						= tax_model["target"]["token_embedder"]["embedding"]
				a__	:							Union[str, Any]      						= txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								a__	:							List[Any]      						= tax_model["target"]["decoder"]["logits_dense"]["kernel"]

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


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
						)
						parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
						parser.add_argument(
						    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
						)
						lowerCamelCase		       =      parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)







import qiskit






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = 2 )	->						qiskit.result.counts.Counts:
				a__	:							Any      						= qubits

				# Using Aer's simulator
				a__	:							Dict      						= qiskit.Aer.get_backend("aer_simulator" )

				# Creating a Quantum Circuit acting on the q register
				a__	:							Dict      						= qiskit.QuantumCircuit(__UpperCamelCase	,				__UpperCamelCase )

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0 )

				for i in range(1	,				__UpperCamelCase ):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1	,				__UpperCamelCase )

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(__UpperCamelCase ) )	,				list(range(__UpperCamelCase ) ) )

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				a__	:							Any      						= qiskit.execute(__UpperCamelCase	,				__UpperCamelCase	,				shots=10_00 )

				return job.result().get_counts(__UpperCamelCase )


if __name__ == "__main__":
						print(F'Total count for various states are: {quantum_entanglement(3)}')


