import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase     :					Union[str, Any]				  =						logging.get_logger(__name__)



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[str]					,	__UpperCamelCase       :			List[Any]					,	__UpperCamelCase       :			List[Any]      )				->   List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= WavLMForSequenceClassification.from_pretrained(snake_case__					,	config=snake_case__      )
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""projector.weight"""]
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""projector.bias"""]
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""model.post_net.linear.weight"""]
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""model.post_net.linear.bias"""]
  return model



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Tuple					,	__UpperCamelCase       :			int					,	__UpperCamelCase       :			List[Any]      )				->   Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= WavLMForAudioFrameClassification.from_pretrained(snake_case__					,	config=snake_case__      )
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""model.linear.weight"""]
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""model.linear.bias"""]
  return model



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			int					,	__UpperCamelCase       :			Optional[int]					,	__UpperCamelCase       :			Union[str, Any]      )				->   Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= WavLMForXVector.from_pretrained(snake_case__					,	config=snake_case__      )
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""connector.weight"""]
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
    SCREAMING_SNAKE_CASE__     				= downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    SCREAMING_SNAKE_CASE__     				= downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

  SCREAMING_SNAKE_CASE__     				= downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  SCREAMING_SNAKE_CASE__     				= downstream_dict["""objective.W"""]
  return model



@torch.no_grad()
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[Any]					,	__UpperCamelCase       :			Union[str, Any]					,	__UpperCamelCase       :			int					,	__UpperCamelCase       :			int      )				->   List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= torch.load(snake_case__					,	map_location="""cpu"""      )

  SCREAMING_SNAKE_CASE__     				= checkpoint["""Downstream"""]

  SCREAMING_SNAKE_CASE__     				= WavLMConfig.from_pretrained(snake_case__      )
  SCREAMING_SNAKE_CASE__     				= WavaVecaFeatureExtractor.from_pretrained(
      snake_case__					,	return_attention_mask=snake_case__					,	do_normalize=snake_case__      )

  SCREAMING_SNAKE_CASE__     				= hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""      ):
    SCREAMING_SNAKE_CASE__     				= convert_classification(snake_case__					,	snake_case__					,	snake_case__      )
  elif arch.endswith("""ForAudioFrameClassification"""      ):
    SCREAMING_SNAKE_CASE__     				= convert_diarization(snake_case__					,	snake_case__					,	snake_case__      )
  elif arch.endswith("""ForXVector"""      ):
    SCREAMING_SNAKE_CASE__     				= convert_xvector(snake_case__					,	snake_case__					,	snake_case__      )
  else:
    raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""      )

  if hf_config.use_weighted_layer_sum:
    SCREAMING_SNAKE_CASE__     				= checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(snake_case__      )
  hf_model.save_pretrained(snake_case__      )


if __name__ == "__main__":
   __lowerCamelCase     :					str				  =						argparse.ArgumentParser()
   parser.add_argument(
       '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
   )
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
   parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
   __lowerCamelCase     :					Any				  =						parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


import math

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





def        lowerCAmelCase       (				snake_case__   :      int = 1				,					snake_case__   :      int = 1 )->			qiskit.result.counts.Counts:
    if isinstance(snake_case__				,					snake_case__ ) or isinstance(snake_case__				,					snake_case__ ):
        raise TypeError("inputs must be integers." )
    if (bit_a < 0) or (bit_a < 0):
        raise ValueError("inputs must be positive." )
    if (math.floor(snake_case__ ) != bit_a) or (math.floor(snake_case__ ) != 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
    A_								=					QuantumRegister(2				,					"qr" )
    A_								=					ClassicalRegister(2				,					"cr" )

    A_								=					QuantumCircuit(snake_case__				,					snake_case__ )

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

    # send the information
    A_								=					str(snake_case__ ) + str(snake_case__ )

    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(snake_case__				,					snake_case__ )

    A_								=					Aer.get_backend("aer_simulator" )
    A_								=					execute(snake_case__				,					snake_case__				,					shots=1000 )

    return job.result().get_counts(snake_case__ )


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






