



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[Any]:
				a__	:							Optional[int]      						= [
				    "decoder.version",
				    "decoder.output_projection.weight",
				    "_float_tensor",
				    "decoder.embed_positions._float_tensor",
				]
				for k in ignore_keys:
								state_dict.pop(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						int:
				a__						,   a__	:							str      						= emb.weight.shape
				a__	:							List[str]      						= nn.Linear(__UpperCamelCase	,				__UpperCamelCase	,				bias=__UpperCamelCase )
				a__	:							Any      						= emb.weight.data
				return lin_layer






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Dict:
				a__	:							Tuple      						= torch.load(__UpperCamelCase	,				map_location="cpu" )
				a__	:							List[Any]      						= Namespace(**checkpoint["cfg"]["model"] )
				a__	:							List[str]      						= checkpoint["model"]
				remove_ignore_keys_(__UpperCamelCase )
				a__	:							Any      						= state_dict["decoder.embed_tokens.weight"].shape[0]

				a__	:							List[str]      						= {key.replace("decoder"	,				"model" ): val for key, val in state_dict.items()}

				a__	:							Dict      						= XGLMConfig(
				    vocab_size=__UpperCamelCase	,				max_position_embeddings=args.max_target_positions	,				num_layers=args.decoder_layers	,				attention_heads=args.decoder_attention_heads	,				ffn_dim=args.decoder_ffn_embed_dim	,				d_model=args.decoder_embed_dim	,				layerdrop=args.decoder_layerdrop	,				dropout=args.dropout	,				attention_dropout=args.attention_dropout	,				activation_dropout=args.activation_dropout	,				activation_function="gelu"	,				scale_embedding=not args.no_scale_embedding	,				tie_word_embeddings=args.share_decoder_input_output_embed	,				)

				a__	:							Tuple      						= XGLMForCausalLM(__UpperCamelCase )
				a__	:							List[Any]      						= model.load_state_dict(__UpperCamelCase	,				strict=__UpperCamelCase )
				print(__UpperCamelCase )
				a__	:							Tuple      						= make_linear_from_emb(model.model.embed_tokens )

				return model


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
						parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						lowerCamelCase		       =      parser.parse_args()
						lowerCamelCase		       =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_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)}')


