




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


_snake_case            = logging.get_logger(__name__)

_snake_case            = {
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}







class 		lowercase							(     UpperCamelCase__	):
					_a											=		"mctct"

					def __init__(						self	,  _a=8065	,  _a=1536	,  _a=36	,  _a=6144	,  _a=4	,  _a=384	,  _a=920	,  _a=1e-5	,  _a=0.3	,  _a="relu"	,  _a=0.02	,  _a=0.3	,  _a=0.3	,  _a=1	,  _a=0	,  _a=2	,  _a=1	,  _a=0.3	,  _a=1	,  _a=(7,)	,  _a=(3,)	,  _a=80	,  _a=1	,  _a=None	,  _a="sum"	,  _a=False	,  **_a	,  )		->							List[str]:
											super().__init__(**_a	,  pad_token_id=_a	,  bos_token_id=_a	,  eos_token_id=_a       )
											_A							:  Dict    =		vocab_size
											_A							:  Tuple    =		hidden_size
											_A							:  Optional[int]    =		num_hidden_layers
											_A							:  List[str]    =		intermediate_size
											_A							:  int    =		num_attention_heads
											_A							:  Union[str, Any]    =		attention_head_dim
											_A							:  Tuple    =		max_position_embeddings
											_A							:  int    =		layer_norm_eps
											_A							:  int    =		layerdrop
											_A							:  Optional[int]    =		hidden_act
											_A							:  List[Any]    =		initializer_range
											_A							:  Optional[int]    =		hidden_dropout_prob
											_A							:  Union[str, Any]    =		attention_probs_dropout_prob
											_A							:  str    =		pad_token_id
											_A							:  int    =		bos_token_id
											_A							:  int    =		eos_token_id
											_A							:  List[str]    =		conv_glu_dim
											_A							:  Optional[int]    =		conv_dropout
											_A							:  Any    =		num_conv_layers
											_A							:  Optional[Any]    =		input_feat_per_channel
											_A							:  List[Any]    =		input_channels
											_A							:  List[str]    =		conv_channels
											_A							:  int    =		ctc_loss_reduction
											_A							:  Optional[Any]    =		ctc_zero_infinity

											# prevents config testing fail with exporting to json
											_A							:  str    =		list(_a       )
											_A							:  Union[str, Any]    =		list(_a       )

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







def        lowerCAmelCase_			(  snake_case_ = 1000000  ):
      _A							:  Any    =		limit + 1
      _A							:  Tuple    =		[0] * limit
      for first_term in range(1,snake_case_  ):
            for n in range(snake_case_,snake_case_,snake_case_  ):
                  _A							:  Optional[int]    =		first_term + n / first_term
                  if common_difference % 4:  # d must be divisble by 4
                        continue
                  else:
                        common_difference /= 4
                        if (
                            first_term > common_difference
                            and first_term < 4 * common_difference
                        ):  # since x,y,z are positive integers
                              frequency[n] += 1  # so z>0 and a>d ,also 4d<a

      _A							:  List[str]    =		sum(1 for x in frequency[1:limit] if x == 10  )

      return count


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

