






"""simple docstring"""



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_snake_case       :      List[str]       =					logging.get_logger(__name__)

_snake_case       :      int       =					{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}




def 						lowerCamelCase_				(  __A  :		str			,	__A  :		Union[str, Any]			,	__A  :		int			,	__A  :		int			,	__A  :		List[Any]			):







					for attribute in key.split('.'			):
										lowerCamelCase__				=getattr(__A			,	__A			)

					if weight_type is not None:
										lowerCamelCase__				=getattr(__A			,	__A			).shape
					else:
										lowerCamelCase__				=hf_pointer.shape

					assert hf_shape == value.shape, (
					    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
					    f" {value.shape} for {full_name}"
					)

					if weight_type == "weight":
										lowerCamelCase__				=value
					elif weight_type == "weight_g":
										lowerCamelCase__				=value
					elif weight_type == "weight_v":
										lowerCamelCase__				=value
					elif weight_type == "bias":
										lowerCamelCase__				=value
					else:
										lowerCamelCase__				=value

					logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."			)




def 						lowerCamelCase_				(  __A  :		Optional[int]			,	__A  :		Union[str, Any]			):
					lowerCamelCase__				=[]
					lowerCamelCase__				=fairseq_model.state_dict()

					lowerCamelCase__				=hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										lowerCamelCase__				=False
										if "conv_layers" in name:
															load_conv_layer(
															    __A			,	__A			,	__A			,	__A			,	hf_model.config.feat_extract_norm == 'group'			,	)
															lowerCamelCase__				=True
										else:
															for key, mapped_key in MAPPING.items():
																				lowerCamelCase__				=mapped_key

																				if key in name:
																									lowerCamelCase__				=True
																									if "*" in mapped_key:
																														lowerCamelCase__				=name.split(__A			)[0].split('.'			)[-2]
																														lowerCamelCase__				=mapped_key.replace('*'			,	__A			)
																									if "weight_g" in name:
																														lowerCamelCase__				='weight_g'
																									elif "weight_v" in name:
																														lowerCamelCase__				='weight_v'
																									elif "weight" in name:
																														lowerCamelCase__				='weight'
																									elif "bias" in name:
																														lowerCamelCase__				='bias'
																									else:
																														lowerCamelCase__				=None
																									set_recursively(__A			,	__A			,	__A			,	__A			,	__A			)
																				continue
										if not is_used:
															unused_weights.append(__A			)

					logger.warning(f"Unused weights: {unused_weights}"			)




def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		List[str]			,	__A  :		Tuple			,	__A  :		Optional[Any]			,	__A  :		str			):
					lowerCamelCase__				=full_name.split('conv_layers.'			)[-1]
					lowerCamelCase__				=name.split('.'			)
					lowerCamelCase__				=int(items[0]			)
					lowerCamelCase__				=int(items[1]			)

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    f"{full_name} has size {value.shape}, but"
															    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
															)
															lowerCamelCase__				=value
															logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    f"{full_name} has size {value.shape}, but"
															    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
															)
															lowerCamelCase__				=value
															logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
															    " found."
															)
															lowerCamelCase__				=value
															logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    f"{full_name} has size {value.shape}, but"
															    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
															)
															lowerCamelCase__				=value
															logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
					else:
										unused_weights.append(__A			)




def 						lowerCamelCase_				(  __A  :		List[str]			):
					lowerCamelCase__				=HubertConfig()
					lowerCamelCase__				=model.config

					lowerCamelCase__				=fs_config.activation_dropout
					lowerCamelCase__				=False
					lowerCamelCase__				=fs_config.attention_dropout
					lowerCamelCase__				=False
					lowerCamelCase__				=eval(fs_config.extractor_conv_feature_layers			)
					lowerCamelCase__				=[x[0] for x in conv_layers]
					lowerCamelCase__				=[x[1] for x in conv_layers]
					lowerCamelCase__				=[x[2] for x in conv_layers]
					lowerCamelCase__				='gelu'
					lowerCamelCase__				='layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
					lowerCamelCase__				=False
					lowerCamelCase__				=0.0
					lowerCamelCase__				=0.0
					lowerCamelCase__				=fs_config.activation_fn
					lowerCamelCase__				=fs_config.dropout
					lowerCamelCase__				=fs_config.encoder_embed_dim
					lowerCamelCase__				=0.0_2
					lowerCamelCase__				=fs_config.encoder_ffn_embed_dim
					lowerCamelCase__				=1E-5
					lowerCamelCase__				=0.0
					lowerCamelCase__				=fs_config.encoder_attention_heads
					lowerCamelCase__				=fs_config.conv_pos_groups
					lowerCamelCase__				=fs_config.conv_pos
					lowerCamelCase__				=len(__A			)
					lowerCamelCase__				=fs_config.encoder_layers

					return config




@torch.no_grad()
def 						lowerCamelCase_				(  __A  :		Any			,	__A  :		List[str]=None			):
					lowerCamelCase__				=distilhubert().model.model

					if config_path is not None:
										lowerCamelCase__				=HubertConfig.from_pretrained(__A			)
					else:
										lowerCamelCase__				=convert_config(__A			)
					lowerCamelCase__				=model.eval()

					lowerCamelCase__				=WavaVecaFeatureExtractor(
					    feature_size=1			,	sampling_rate=1_6000			,	padding_value=0			,	do_normalize=__A			,	return_attention_mask=__A			,	)
					lowerCamelCase__				=HubertModel(__A			)

					recursively_load_weights(__A			,	__A			)

					feature_extractor.save_pretrained(__A			)
					hf_model.save_pretrained(__A			)


if __name__ == "__main__":
						_snake_case       :      Any       =					argparse.ArgumentParser()
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						_snake_case       :      List[str]       =					parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""



import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def 						lowerCamelCase_				(  __A  :		int = 1			,	__A  :		int = 1			):
					if isinstance(__A			,	__A			) or isinstance(__A			,	__A			):
										raise TypeError('inputs must be integers.'			)
					if (bit_a < 0) or (bit_a < 0):
										raise ValueError('inputs must be positive.'			)
					if (math.floor(__A			) != bit_a) or (math.floor(__A			) != bit_a):
										raise ValueError('inputs must be exact integers.'			)
					if (bit_a > 1) or (bit_a > 1):
										raise ValueError('inputs must be less or equal to 1.'			)

					# build registers
					lowerCamelCase__				=QuantumRegister(2			,	'qr'			)
					lowerCamelCase__				=ClassicalRegister(2			,	'cr'			)

					lowerCamelCase__				=QuantumCircuit(__A			,	__A			)

					# entanglement the qubits
					quantum_circuit.h(1			)
					quantum_circuit.cx(1			,	0			)

					# send the information
					lowerCamelCase__				=str(__A			) + str(__A			)

					if c_information == "11":
										quantum_circuit.x(1			)
										quantum_circuit.z(1			)
					elif c_information == "10":
										quantum_circuit.z(1			)
					elif c_information == "01":
										quantum_circuit.x(1			)
					else:
										quantum_circuit.i(1			)

					# unentangled the circuit
					quantum_circuit.cx(1			,	0			)
					quantum_circuit.h(1			)

					# measure the circuit
					quantum_circuit.measure(__A			,	__A			)

					lowerCamelCase__				=Aer.get_backend('aer_simulator'			)
					lowerCamelCase__				=execute(__A			,	__A			,	shots=1000			)

					return job.result().get_counts(__A			)


if __name__ == "__main__":
						print(f"Counts for classical state send: {superdense_coding(1,1)}")