

'''simple docstring'''




import math

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


_a					:			Tuple							=    logging.get_logger(__name__)

_a					:			List[Any]							=    {
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			str     	= "data2vec-audio"
  def __init__(  self ,			a_=3_2 ,			a_=7_6_8 ,			a_=1_2 ,			a_=1_2 ,			a_=3_0_7_2 ,			a_="gelu" ,			a_=0.1 ,			a_=0.1 ,			a_=0.1 ,			a_=0.0 ,			a_=0.1 ,			a_=0.1 ,			a_=0.02 ,			a_=1E-5 ,			a_="gelu" ,			a_=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) ,			a_=(5, 2, 2, 2, 2, 2, 2) ,			a_=(1_0, 3, 3, 3, 3, 2, 2) ,			a_=False ,			a_=1_6 ,			a_=1_9 ,			a_=5 ,			a_=0.05 ,			a_=1_0 ,			a_=2 ,			a_=0.0 ,			a_=1_0 ,			a_=0 ,			a_="sum" ,			a_=False ,			a_=False ,			a_=2_5_6 ,			a_=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) ,			a_=(5, 3, 3, 1, 1) ,			a_=(1, 2, 3, 1, 1) ,			a_=5_1_2 ,			a_=0 ,			a_=1 ,			a_=2 ,			a_=False ,			a_=3 ,			a_=2 ,			a_=3 ,			a_=None ,			**a_ ,			)				->					List[str]:


    """simple docstring"""
    super().__init__(**a_ ,			pad_token_id=a_ ,			bos_token_id=a_ ,			eos_token_id=a_  )
    UpperCAmelCase    				=						hidden_size
    UpperCAmelCase    				=						feat_extract_activation
    UpperCAmelCase    				=						list(a_  )
    UpperCAmelCase    				=						list(a_  )
    UpperCAmelCase    				=						list(a_  )
    UpperCAmelCase    				=						conv_bias
    UpperCAmelCase    				=						num_conv_pos_embeddings
    UpperCAmelCase    				=						num_conv_pos_embedding_groups
    UpperCAmelCase    				=						conv_pos_kernel_size
    UpperCAmelCase    				=						len(self.conv_dim  )
    UpperCAmelCase    				=						num_hidden_layers
    UpperCAmelCase    				=						intermediate_size
    UpperCAmelCase    				=						hidden_act
    UpperCAmelCase    				=						num_attention_heads
    UpperCAmelCase    				=						hidden_dropout
    UpperCAmelCase    				=						attention_dropout
    UpperCAmelCase    				=						activation_dropout
    UpperCAmelCase    				=						feat_proj_dropout
    UpperCAmelCase    				=						final_dropout
    UpperCAmelCase    				=						layerdrop
    UpperCAmelCase    				=						layer_norm_eps
    UpperCAmelCase    				=						initializer_range
    UpperCAmelCase    				=						vocab_size
    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						mask_time_prob
    UpperCAmelCase    				=						mask_time_length
    UpperCAmelCase    				=						mask_time_min_masks
    UpperCAmelCase    				=						mask_feature_prob
    UpperCAmelCase    				=						mask_feature_length
    UpperCAmelCase    				=						mask_feature_min_masks

    # ctc loss
    UpperCAmelCase    				=						ctc_loss_reduction
    UpperCAmelCase    				=						ctc_zero_infinity

    # adapter
    UpperCAmelCase    				=						add_adapter
    UpperCAmelCase    				=						adapter_kernel_size
    UpperCAmelCase    				=						adapter_stride
    UpperCAmelCase    				=						num_adapter_layers
    UpperCAmelCase    				=						output_hidden_size or hidden_size

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

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





  @property
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    return math.prod(self.conv_stride  )





'''simple docstring'''




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 							lowercase_						(		nn.Module			):

  '''simple docstring'''





  def __init__(  self ,			a_ = 1_6 ,			a_ = 8_8 ,			a_ = None ,			a_ = 1 ,			a_ = 0.0 ,			a_ = 3_2 ,			a_ = None ,			a_ = False ,			a_ = None ,			a_ = None ,			a_ = "geglu" ,			a_ = None ,			)				->					List[str]:


    """simple docstring"""
    super().__init__()
    UpperCAmelCase    				=						nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=a_ ,			attention_head_dim=a_ ,			in_channels=a_ ,			num_layers=a_ ,			dropout=a_ ,			norm_num_groups=a_ ,			cross_attention_dim=a_ ,			attention_bias=a_ ,			sample_size=a_ ,			num_vector_embeds=a_ ,			activation_fn=a_ ,			num_embeds_ada_norm=a_ ,			)
            for _ in range(2  )
        ]  )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase    				=						0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase    				=						[7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase    				=						[1, 0]





  def     snake_case_      (  self ,			a_ ,			a_ ,			a_=None ,			a_=None ,			a_=None ,			a_ = True ,			)				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						hidden_states

    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						0
    # attention_mask is not used yet
    for i in range(2  ):
      # for each of the two transformers, pass the corresponding condition tokens
      UpperCAmelCase    				=						encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
      UpperCAmelCase    				=						self.transformer_index_for_condition[i]
      UpperCAmelCase    				=						self.transformers[transformer_index](
          a_ ,			encoder_hidden_states=a_ ,			timestep=a_ ,			cross_attention_kwargs=a_ ,			return_dict=a_ ,			)[0]
      encoded_states.append(encoded_state - input_states  )
      tokens_start += self.condition_lengths[i]

    UpperCAmelCase    				=						encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase    				=						output_states + input_states

    if not return_dict:
      return (output_states,)

    return TransformeraDModelOutput(sample=a_  )


