





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


_lowerCamelCase			       =      logging.get_logger(__name__)

_lowerCamelCase			       =      {
    '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       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      """speech_to_text"""
      UpperCAmelCase__						=      ["""past_key_values"""]
      UpperCAmelCase__						=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


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



             """simple docstring"""





             _lowerCamelCase							:     str	=		vocab_size
             _lowerCamelCase							:     Optional[int]	=		d_model
             _lowerCamelCase							:     List[str]	=		encoder_ffn_dim
             _lowerCamelCase							:     Any	=		encoder_layers
             _lowerCamelCase							:     Union[str, Any]	=		encoder_attention_heads
             _lowerCamelCase							:     Dict	=		decoder_ffn_dim
             _lowerCamelCase							:     List[Any]	=		decoder_layers
             _lowerCamelCase							:     Optional[int]	=		decoder_attention_heads
             _lowerCamelCase							:     Dict	=		dropout
             _lowerCamelCase							:     List[Any]	=		attention_dropout
             _lowerCamelCase							:     Dict	=		activation_dropout
             _lowerCamelCase							:     Dict	=		activation_function
             _lowerCamelCase							:     Tuple	=		init_std
             _lowerCamelCase							:     List[str]	=		encoder_layerdrop
             _lowerCamelCase							:     Tuple	=		decoder_layerdrop
             _lowerCamelCase							:     str	=		use_cache
             _lowerCamelCase							:     Dict	=		encoder_layers
             _lowerCamelCase							:     int	=		scale_embedding  # scale factor will be sqrt(d_model) if True
             _lowerCamelCase							:     Dict	=		max_source_positions
             _lowerCamelCase							:     Tuple	=		max_target_positions
             _lowerCamelCase							:     Dict	=		num_conv_layers
             _lowerCamelCase							:     List[str]	=		list(a__)
             _lowerCamelCase							:     str	=		conv_channels
             _lowerCamelCase							:     Tuple	=		input_feat_per_channel
             _lowerCamelCase							:     Any	=		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=a__      ,							bos_token_id=a__      ,							eos_token_id=a__      ,							is_encoder_decoder=a__      ,							decoder_start_token_id=a__      ,							**a__      ,							)








import math




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[str]	=		len(lowercase_      )
       _lowerCamelCase							:     Optional[int]	=		int(math.floor(math.sqrt(lowercase_      )      )      )
       _lowerCamelCase							:     Tuple	=		0
       while arr[min(lowercase_ ,    lowercase_      ) - 1] < x:
              _lowerCamelCase							:     Optional[Any]	=		step
              step += int(math.floor(math.sqrt(lowercase_      )      )      )
              if prev >= n:
                     return -1

       while arr[prev] < x:
              _lowerCamelCase							:     int	=		prev + 1
              if prev == min(lowercase_ ,    lowercase_      ):
                     return -1
       if arr[prev] == x:
              return prev
       return -1


if __name__ == "__main__":
       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       _lowerCamelCase			       =      int(input('Enter the number to be searched:\n'))
       _lowerCamelCase			       =      jump_search(arr, x)
       if res == -1:
              print('Number not found!')
       else:
              print(F'''Number {x} is at index {res}''')

