"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase		:		Any														=							logging.get_logger(__name__)

__lowercase		:		List[Any]														=							{
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[int]							=					'''wav2vec2'''
    def __init__(		self							:     Any	,      A_							:     str=32	,      A_							:     Dict=768	,      A_							:     int=12	,      A_							:     Tuple=12	,      A_							:     Optional[Any]=3_072	,      A_							:     List[Any]="gelu"	,      A_							:     int=0.1	,      A_							:     Any=0.1	,      A_							:     Optional[int]=0.1	,      A_							:     Optional[Any]=0.0	,      A_							:     Union[str, Any]=0.0	,      A_							:     List[Any]=0.1	,      A_							:     List[Any]=0.1	,      A_							:     Union[str, Any]=0.02	,      A_							:     List[str]=1E-5	,      A_							:     Optional[int]="group"	,      A_							:     str="gelu"	,      A_							:     Union[str, Any]=(512, 512, 512, 512, 512, 512, 512)	,      A_							:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)	,      A_							:     int=(10, 3, 3, 3, 3, 2, 2)	,      A_							:     str=False	,      A_							:     Union[str, Any]=128	,      A_							:     str=16	,      A_							:     str=False	,      A_							:     str=True	,      A_							:     List[Any]=0.05	,      A_							:     List[Any]=10	,      A_							:     Any=2	,      A_							:     Optional[int]=0.0	,      A_							:     Dict=10	,      A_							:     Optional[int]=0	,      A_							:     int=320	,      A_							:     Optional[int]=2	,      A_							:     Tuple=0.1	,      A_							:     Optional[Any]=100	,      A_							:     Tuple=256	,      A_							:     Dict=256	,      A_							:     Union[str, Any]=0.1	,      A_							:     Tuple="sum"	,      A_							:     Any=False	,      A_							:     List[str]=False	,      A_							:     Union[str, Any]=256	,      A_							:     Optional[Any]=(512, 512, 512, 512, 1_500)	,      A_							:     List[Any]=(5, 3, 3, 1, 1)	,      A_							:     Dict=(1, 2, 3, 1, 1)	,      A_							:     Tuple=512	,      A_							:     List[str]=0	,      A_							:     Any=1	,      A_							:     Dict=2	,      A_							:     Optional[Any]=False	,      A_							:     List[Any]=3	,      A_							:     str=2	,      A_							:     Tuple=3	,      A_							:     Dict=None	,      A_							:     Tuple=None	,      **A_							:     Any	,      )    ->       Union[str, Any]:
         super().__init__(**A_	,      pad_token_id=A_	,      bos_token_id=A_	,      eos_token_id=A_    )
         __snake_case				=							hidden_size
         __snake_case				=							feat_extract_norm
         __snake_case				=							feat_extract_activation
         __snake_case				=							list(A_    )
         __snake_case				=							list(A_    )
         __snake_case				=							list(A_    )
         __snake_case				=							conv_bias
         __snake_case				=							num_conv_pos_embeddings
         __snake_case				=							num_conv_pos_embedding_groups
         __snake_case				=							len(self.conv_dim    )
         __snake_case				=							num_hidden_layers
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							num_attention_heads
         __snake_case				=							hidden_dropout
         __snake_case				=							attention_dropout
         __snake_case				=							activation_dropout
         __snake_case				=							feat_proj_dropout
         __snake_case				=							final_dropout
         __snake_case				=							layerdrop
         __snake_case				=							layer_norm_eps
         __snake_case				=							initializer_range
         __snake_case				=							vocab_size
         __snake_case				=							do_stable_layer_norm
         __snake_case				=							use_weighted_layer_sum

         if (
             (len(self.conv_stride    ) != self.num_feat_extract_layers)
             or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
             or (len(self.conv_dim    ) != self.num_feat_extract_layers)
         ):
              raise ValueError(
                  '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                  ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                  f" {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,"
                  f" `len(config.conv_kernel) = {len(self.conv_kernel    )}`."    )

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __snake_case				=							apply_spec_augment
         __snake_case				=							mask_time_prob
         __snake_case				=							mask_time_length
         __snake_case				=							mask_time_min_masks
         __snake_case				=							mask_feature_prob
         __snake_case				=							mask_feature_length
         __snake_case				=							mask_feature_min_masks

         # parameters for pretraining with codevector quantized representations
         __snake_case				=							num_codevectors_per_group
         __snake_case				=							num_codevector_groups
         __snake_case				=							contrastive_logits_temperature
         __snake_case				=							feat_quantizer_dropout
         __snake_case				=							num_negatives
         __snake_case				=							codevector_dim
         __snake_case				=							proj_codevector_dim
         __snake_case				=							diversity_loss_weight

         # ctc loss
         __snake_case				=							ctc_loss_reduction
         __snake_case				=							ctc_zero_infinity

         # adapter
         __snake_case				=							add_adapter
         __snake_case				=							adapter_kernel_size
         __snake_case				=							adapter_stride
         __snake_case				=							num_adapter_layers
         __snake_case				=							output_hidden_size or hidden_size
         __snake_case				=							adapter_attn_dim

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         __snake_case				=							classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         __snake_case				=							list(A_    )
         __snake_case				=							list(A_    )
         __snake_case				=							list(A_    )
         __snake_case				=							xvector_output_dim







    @property
    def  lowercase					(		self							:     Optional[Any]    )    ->       str:
         return functools.reduce(operator.mul	,      self.conv_stride	,      1    )
"""simple docstring"""



import qiskit





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							qiskit.Aer.get_backend('''aer_simulator''')

     # Create a Quantum Circuit acting on the q register
     __snake_case				=							qiskit.QuantumCircuit(snake_case,      snake_case)

     # Map the quantum measurement to the classical bits
     circuit.measure([0],      [0])

     # Execute the circuit on the simulator
     __snake_case				=							qiskit.execute(snake_case,      snake_case,      shots=10_00)

     # Return the histogram data of the results of the experiment.
     return job.result().get_counts(snake_case)


if __name__ == "__main__":
       print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")