


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase			:  Optional[Any]     =logging.get_logger(__name__)

_UpperCAmelCase			:  Optional[Any]     ={
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Optional[Any]:



				for attribute in key.split('''.''' ):
								lowerCAmelCase_ :      Tuple    				=				getattr(lowerCAmelCase_ ,   lowerCAmelCase_ )

				if weight_type is not None:
								lowerCAmelCase_ :      Tuple    				=				getattr(lowerCAmelCase_ ,   lowerCAmelCase_ ).shape
				else:
								lowerCAmelCase_ :      int    				=				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_ :      List[str]    				=				value
				elif weight_type == "weight_g":
								lowerCAmelCase_ :      Tuple    				=				value
				elif weight_type == "weight_v":
								lowerCAmelCase_ :      str    				=				value
				elif weight_type == "bias":
								lowerCAmelCase_ :      Optional[int]    				=				value
				else:
								lowerCAmelCase_ :      str    				=				value

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



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Any:
				lowerCAmelCase_ :      int    				=				[]
				lowerCAmelCase_ :      str    				=				fairseq_model.state_dict()

				lowerCAmelCase_ :      Tuple    				=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCAmelCase_ :      int    				=				False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   hf_model.config.feat_extract_norm == '''group''' ,   )
												lowerCAmelCase_ :      int    				=				True
								else:
												for key, mapped_key in MAPPING.items():
																lowerCAmelCase_ :      List[str]    				=				'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																if key in name or (key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0] and not is_finetuned):
																				lowerCAmelCase_ :      Any    				=				True
																				if "*" in mapped_key:
																								lowerCAmelCase_ :      List[Any]    				=				name.split(lowerCAmelCase_ )[0].split('''.''' )[-2]
																								lowerCAmelCase_ :      str    				=				mapped_key.replace('''*''' ,   lowerCAmelCase_ )
																				if "weight_g" in name:
																								lowerCAmelCase_ :      Tuple    				=				'''weight_g'''
																				elif "weight_v" in name:
																								lowerCAmelCase_ :      Tuple    				=				'''weight_v'''
																				elif "weight" in name:
																								lowerCAmelCase_ :      str    				=				'''weight'''
																				elif "bias" in name:
																								lowerCAmelCase_ :      List[str]    				=				'''bias'''
																				else:
																								lowerCAmelCase_ :      Union[str, Any]    				=				None
																				set_recursively(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
																continue
								if not is_used:
												unused_weights.append(lowerCAmelCase_ )

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



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> List[str]:
				lowerCAmelCase_ :      int    				=				full_name.split('''conv_layers.''' )[-1]
				lowerCAmelCase_ :      Any    				=				name.split('''.''' )
				lowerCAmelCase_ :      str    				=				int(items[0] )
				lowerCAmelCase_ :      Dict    				=				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_ :      Any    				=				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_ :      Tuple    				=				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_ :      List[str]    				=				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_ :      List[str]    				=				value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
				else:
								unused_weights.append(lowerCAmelCase_ )



@torch.no_grad()
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_=None ,   lowerCAmelCase_=None ,   lowerCAmelCase_=True )-> Any:



				if config_path is not None:
								lowerCAmelCase_ :      List[Any]    				=				HubertConfig.from_pretrained(lowerCAmelCase_ )
				else:
								lowerCAmelCase_ :      Dict    				=				HubertConfig()

				if is_finetuned:
								if dict_path:
												lowerCAmelCase_ :      Dict    				=				Dictionary.load(lowerCAmelCase_ )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowerCAmelCase_ :      int    				=				target_dict.pad_index
												lowerCAmelCase_ :      List[str]    				=				target_dict.bos_index
												lowerCAmelCase_ :      Union[str, Any]    				=				target_dict.eos_index
												lowerCAmelCase_ :      Tuple    				=				len(target_dict.symbols )
												lowerCAmelCase_ :      Optional[Any]    				=				os.path.join(lowerCAmelCase_ ,   '''vocab.json''' )
												if not os.path.isdir(lowerCAmelCase_ ):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase_ ) )
																return
												os.makedirs(lowerCAmelCase_ ,   exist_ok=lowerCAmelCase_ )
												with open(lowerCAmelCase_ ,   '''w''' ,   encoding='''utf-8''' ) as vocab_handle:
																json.dump(target_dict.indices ,   lowerCAmelCase_ )
												lowerCAmelCase_ :      Any    				=				WavaVecaCTCTokenizer(
												    lowerCAmelCase_ ,   unk_token=target_dict.unk_word ,   pad_token=target_dict.pad_word ,   bos_token=target_dict.bos_word ,   eos_token=target_dict.eos_word ,   word_delimiter_token='''|''' ,   do_lower_case=lowerCAmelCase_ ,   )
												lowerCAmelCase_ :      Dict    				=				True if config.feat_extract_norm == '''layer''' else False
												lowerCAmelCase_ :      str    				=				WavaVecaFeatureExtractor(
												    feature_size=1 ,   sampling_rate=16_000 ,   padding_value=0 ,   do_normalize=lowerCAmelCase_ ,   return_attention_mask=lowerCAmelCase_ ,   )
												lowerCAmelCase_ :      int    				=				WavaVecaProcessor(feature_extractor=lowerCAmelCase_ ,   tokenizer=lowerCAmelCase_ )
												processor.save_pretrained(lowerCAmelCase_ )

								lowerCAmelCase_ :      List[str]    				=				HubertForCTC(lowerCAmelCase_ )
				else:
								lowerCAmelCase_ :      List[Any]    				=				HubertModel(lowerCAmelCase_ )

				if is_finetuned:
								lowerCAmelCase_ :      Optional[Any]    				=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path] ,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
				else:
								lowerCAmelCase_ :      Dict    				=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

				lowerCAmelCase_ :      Union[str, Any]    				=				model[0].eval()

				recursively_load_weights(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )

				hf_wavavec.save_pretrained(lowerCAmelCase_ )


if __name__ == "__main__":
						_UpperCAmelCase			:  str     =argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						parser.add_argument(
						    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
						)
						_UpperCAmelCase			:  List[str]     =parser.parse_args()
						convert_hubert_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)



import math

import qiskit



def 			lowerCAmelCase (		lowerCAmelCase_ = 1 ,   lowerCAmelCase_ = 1 ,   lowerCAmelCase_ = 1 )-> qiskit.result.counts.Counts:
				if (
				    isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ )
				    or isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ )
				    or isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ )
				):
								raise TypeError('''inputs must be integers.''' )

				if (input_a < 0) or (input_a < 0) or (carry_in < 0):
								raise ValueError('''inputs must be positive.''' )

				if (
				    (math.floor(lowerCAmelCase_ ) != input_a)
				    or (math.floor(lowerCAmelCase_ ) != input_a)
				    or (math.floor(lowerCAmelCase_ ) != carry_in)
				):
								raise ValueError('''inputs must be exact integers.''' )

				if (input_a > 2) or (input_a > 2) or (carry_in > 2):
								raise ValueError('''inputs must be less or equal to 2.''' )

				# build registers
				lowerCAmelCase_ :      str    				=				qiskit.QuantumRegister(4 ,   '''qr''' )
				lowerCAmelCase_ :      str    				=				qiskit.ClassicalRegister(2 ,   '''cr''' )
				# list the entries
				lowerCAmelCase_ :      Any    				=				[input_a, input_a, carry_in]

				lowerCAmelCase_ :      int    				=				qiskit.QuantumCircuit(lowerCAmelCase_ ,   lowerCAmelCase_ )

				for i in range(0 ,   3 ):
								if entry[i] == 2:
												quantum_circuit.h(lowerCAmelCase_ )  # for hadamard entries
								elif entry[i] == 1:
												quantum_circuit.x(lowerCAmelCase_ )  # for 1 entries
								elif entry[i] == 0:
												quantum_circuit.i(lowerCAmelCase_ )  # for 0 entries

    # build the circuit
				quantum_circuit.ccx(0 ,   1 ,   3 )  # ccx = toffoli gate
				quantum_circuit.cx(0 ,   1 )
				quantum_circuit.ccx(1 ,   2 ,   3 )
				quantum_circuit.cx(1 ,   2 )
				quantum_circuit.cx(0 ,   1 )

				quantum_circuit.measure([2, 3] ,   lowerCAmelCase_ )  # measure the last two qbits

				lowerCAmelCase_ :      Tuple    				=				qiskit.Aer.get_backend('''aer_simulator''' )
				lowerCAmelCase_ :      Union[str, Any]    				=				qiskit.execute(lowerCAmelCase_ ,   lowerCAmelCase_ ,   shots=1_000 )

				return job.result().get_counts(lowerCAmelCase_ )


if __name__ == "__main__":
						print(f"""Total sum count for state is: {quantum_full_adder(1, 1, 1)}""")