



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			__snake_case            =   argparse.ArgumentParser(
			    description=(
			        "Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
			        " Distillation"
			    )
			)
			parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
			parser.add_argument("--model_name", default="roberta-large", type=str)
			parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
			parser.add_argument("--vocab_transform", action="store_true")
			__snake_case            =   parser.parse_args()

			if args.model_type == "roberta":
						__snake_case            =   RobertaForMaskedLM.from_pretrained(args.model_name)
						__snake_case            =   "roberta"
			elif args.model_type == "gpt2":
						__snake_case            =   GPTaLMHeadModel.from_pretrained(args.model_name)
						__snake_case            =   "transformer"

			__snake_case            =   model.state_dict()
			__snake_case            =   {}

			# Embeddings #
			if args.model_type == "gpt2":
						for param_name in ["wte.weight", "wpe.weight"]:
									__snake_case            =   state_dict[F'''{prefix}.{param_name}''']
			else:
						for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
									__snake_case            =   F'''{prefix}.embeddings.{w}.weight'''
									__snake_case            =   state_dict[param_name]
						for w in ["weight", "bias"]:
									__snake_case            =   F'''{prefix}.embeddings.LayerNorm.{w}'''
									__snake_case            =   state_dict[param_name]

    # Transformer Blocks #
			__snake_case            =   0
			for teacher_idx in [0, 2, 4, 7, 9, 11]:
						if args.model_type == "gpt2":
									for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
												for w in ["weight", "bias"]:
															__snake_case            =   state_dict[
															    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
															]
									__snake_case            =   state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
						else:
									for layer in [
									    "attention.self.query",
									    "attention.self.key",
									    "attention.self.value",
									    "attention.output.dense",
									    "attention.output.LayerNorm",
									    "intermediate.dense",
									    "output.dense",
									    "output.LayerNorm",
									]:
												for w in ["weight", "bias"]:
															__snake_case            =   state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
															]
						std_idx += 1

			# Language Modeling Head ###s
			if args.model_type == "roberta":
						for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
									__snake_case            =   state_dict[F'''{layer}''']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												__snake_case            =   state_dict[F'''lm_head.dense.{w}''']
												__snake_case            =   state_dict[F'''lm_head.layer_norm.{w}''']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									__snake_case            =   state_dict[F'''{prefix}.ln_f.{w}''']
						__snake_case            =   state_dict["lm_head.weight"]

			print(F'''N layers selected for distillation: {std_idx}''')
			print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

			print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
			torch.save(compressed_sd, args.dump_checkpoint)








import qiskit






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 2	):





			"""simple docstring"""




			UpperCamelCase         =					qubits

			# Using Aer's simulator
			UpperCamelCase         =					qiskit.Aer.get_backend("""aer_simulator"""	)

			# Creating a Quantum Circuit acting on the q register
			UpperCamelCase         =					qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

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

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

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

			# 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
			UpperCamelCase         =					qiskit.execute(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  shots=1_000	)

			return job.result().get_counts(SCREAMING_SNAKE_CASE_	)


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




