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


__magic_name__								=					logging.get_logger(__name__)

__magic_name__								=					{
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Any						="speech_to_text"
  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=["past_key_values"]
  SCREAMING_SNAKE_CASE_	:	Dict						={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=1_0000   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=6   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_="relu"   ,				SCREAMING_SNAKE_CASE_=256   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=1   ,				SCREAMING_SNAKE_CASE_=0   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=6000   ,				SCREAMING_SNAKE_CASE_=1024   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=(5, 5)   ,				SCREAMING_SNAKE_CASE_=1024   ,				SCREAMING_SNAKE_CASE_=80   ,				SCREAMING_SNAKE_CASE_=1   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      vocab_size
       UpperCamelCase__										=      d_model
       UpperCamelCase__										=      encoder_ffn_dim
       UpperCamelCase__										=      encoder_layers
       UpperCamelCase__										=      encoder_attention_heads
       UpperCamelCase__										=      decoder_ffn_dim
       UpperCamelCase__										=      decoder_layers
       UpperCamelCase__										=      decoder_attention_heads
       UpperCamelCase__										=      dropout
       UpperCamelCase__										=      attention_dropout
       UpperCamelCase__										=      activation_dropout
       UpperCamelCase__										=      activation_function
       UpperCamelCase__										=      init_std
       UpperCamelCase__										=      encoder_layerdrop
       UpperCamelCase__										=      decoder_layerdrop
       UpperCamelCase__										=      use_cache
       UpperCamelCase__										=      encoder_layers
       UpperCamelCase__										=      scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCamelCase__										=      max_source_positions
       UpperCamelCase__										=      max_target_positions
       UpperCamelCase__										=      num_conv_layers
       UpperCamelCase__										=      list(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      conv_channels
       UpperCamelCase__										=      input_feat_per_channel
       UpperCamelCase__										=      input_channels

       if len(self.conv_kernel_sizes	) != self.num_conv_layers:
            raise ValueError(
                '''Configuration for convolutional module is incorrect. '''
                '''It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '''
                F"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes	)}`, "
                F"`config.num_conv_layers = {self.num_conv_layers}`."	)

       super().__init__(
           pad_token_id=SCREAMING_SNAKE_CASE_   ,				bos_token_id=SCREAMING_SNAKE_CASE_   ,				eos_token_id=SCREAMING_SNAKE_CASE_   ,				is_encoder_decoder=SCREAMING_SNAKE_CASE_   ,				decoder_start_token_id=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_   ,				)




import math

import tensorflow as tf
from packaging import version







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							) ,							x.dtype							)							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(math.pi ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(A ,							3							))							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)

     return x * tf.tanh(tf.math.softplus(A							)							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.79_78_84_56_08 ,							x.dtype							)

     return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)							))







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(1.7_02 ,							x.dtype							)
     return x * tf.math.sigmoid(coeff * x							)







def  __UpperCamelCase    (						A							):
     return tf.clip_by_value(_gelu(A							) ,							-10 ,							10							)







def  __UpperCamelCase    (						A ,							A=-1							):
     UpperCamelCase__						,       UpperCamelCase__										=      tf.split(A ,							2 ,							axis=A							)
     return a * tf.math.sigmoid(A							)


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):







     def  __UpperCamelCase    (						A							):
          return tf.keras.activations.gelu(A ,							approximate=A							)

     __magic_name__								=tf.keras.activations.gelu



     __magic_name__								=approximate_gelu_wrap
else:
     __magic_name__								=_gelu
     __magic_name__								=_gelu_new


__magic_name__								={
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}







def  __UpperCamelCase    (						A							):
     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}"							)



