


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''speech_to_text_2'''
   __UpperCAmelCase							       =					['''past_key_values''']
   __UpperCAmelCase							       =					{'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=1_0000      ,   SCREAMING_SNAKE_CASE=6      ,   SCREAMING_SNAKE_CASE=2048      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=0.0      ,   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=1024      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Dict:
      _lowerCamelCase	:					Dict   =		vocab_size
      _lowerCamelCase	:					str   =		d_model
      _lowerCamelCase	:					List[str]   =		decoder_ffn_dim
      _lowerCamelCase	:					Tuple   =		decoder_layers
      _lowerCamelCase	:					str   =		decoder_attention_heads
      _lowerCamelCase	:					Dict   =		dropout
      _lowerCamelCase	:					Union[str, Any]   =		attention_dropout
      _lowerCamelCase	:					int   =		activation_dropout
      _lowerCamelCase	:					str   =		activation_function
      _lowerCamelCase	:					Dict   =		init_std
      _lowerCamelCase	:					Optional[Any]   =		decoder_layerdrop
      _lowerCamelCase	:					Dict   =		use_cache
      _lowerCamelCase	:					str   =		decoder_layers
      _lowerCamelCase	:					str   =		scale_embedding  # scale factor will be sqrt(d_model) if True
      _lowerCamelCase	:					Optional[Any]   =		max_target_positions

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





"""simple docstring"""
from math import isqrt, loga

def 				_snake_case      (   __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		[True] * max_number
   for i in range(2						, isqrt(max_number - 1   ) + 1   ):
      if is_prime[i]:
         for j in range(i**2						, __snake_case						, __snake_case   ):
            _lowerCamelCase	:					Optional[int]   =		False

   return [i for i in range(2						, __snake_case   ) if is_prime[i]]

def 				_snake_case      (   __snake_case							:      int = 800800						, __snake_case							:      int = 800800   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		degree * loga(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		int(__snake_case   )
   _lowerCamelCase	:					Dict   =		calculate_prime_numbers(__snake_case   )

   _lowerCamelCase	:					Optional[int]   =		0
   _lowerCamelCase	:					Any   =		0
   _lowerCamelCase	:					Any   =		len(__snake_case   ) - 1
   while left < right:
      while (
          prime_numbers[right] * loga(prime_numbers[left]   )
          + prime_numbers[left] * loga(prime_numbers[right]   )
          > upper_bound
      ):
         right -= 1
      hybrid_integers_count += right - left
      left += 1

   return hybrid_integers_count


if __name__ == "__main__":
 print(f'''{solution() = }''')

