"""simple docstring"""




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_a      :		Tuple           =   logging.get_logger(__name__)

_a      :		Optional[int]           =   {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		"gptj"
     _UpperCamelCase							:			int							=		{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }






     def __init__(		self				,     a__=50400				,     a__=2048				,     a__=4096				,     a__=28				,     a__=16				,     a__=64				,     a__=None				,     a__="gelu_new"				,     a__=0.0				,     a__=0.0				,     a__=0.0				,     a__=1e-5				,     a__=0.0_2				,     a__=True				,     a__=50256				,     a__=50256				,     a__=False				,     **a__				,     ):
       _lowerCAmelCase						:       Union[str, Any]  			=     vocab_size
       _lowerCAmelCase						:       Dict  			=     n_positions
       _lowerCAmelCase						:       Optional[int]  			=     n_embd
       _lowerCAmelCase						:       str  			=     n_layer
       _lowerCAmelCase						:       Optional[Any]  			=     n_head
       _lowerCAmelCase						:       int  			=     n_inner
       _lowerCAmelCase						:       int  			=     rotary_dim
       _lowerCAmelCase						:       str  			=     activation_function
       _lowerCAmelCase						:       Dict  			=     resid_pdrop
       _lowerCAmelCase						:       str  			=     embd_pdrop
       _lowerCAmelCase						:       int  			=     attn_pdrop
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_epsilon
       _lowerCAmelCase						:       Tuple  			=     initializer_range
       _lowerCAmelCase						:       Dict  			=     use_cache

       _lowerCAmelCase						:       Dict  			=     bos_token_id
       _lowerCAmelCase						:       str  			=     eos_token_id

       super().__init__(
           bos_token_id=a__				,     eos_token_id=a__				,     tie_word_embeddings=a__				,     **a__  )







class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__ = "default"				,     a__ = None				,     a__ = False				,     ):
       super().__init__(a__				,     task=a__				,     patching_specs=a__				,     use_past=a__  )
       if not getattr(self._config				,     """pad_token_id"""				,     a__  ):
         # TODO: how to do that better?
         _lowerCAmelCase						:       List[Any]  			=     0






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}  )
       if self.use_past:
         self.fill_with_past_key_values_(a__				,     direction="""inputs"""  )
         _lowerCAmelCase						:       Any  			=     {0: """batch""", 1: """past_sequence + sequence"""}
       else:
         _lowerCAmelCase						:       Any  			=     {0: """batch""", 1: """sequence"""}

       return common_inputs






     @property
     def     __A  (		self  ):
       return self._config.n_layer






     @property
     def     __A  (		self  ):
       return self._config.n_head






     def     __A  (		self				,     a__				,     a__ = -1				,     a__ = -1				,     a__ = False				,     a__ = None				,     ):
       _lowerCAmelCase						:       str  			=     super(a__				,     self  ).generate_dummy_inputs(
           a__				,     batch_size=a__				,     seq_length=a__				,     is_pair=a__				,     framework=a__  )

       # We need to order the input in the way they appears in the forward()
       _lowerCAmelCase						:       Any  			=     OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}  )

       # Need to add the past_keys
       if self.use_past:
         if not is_torch_available():
           raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
         else:
           import torch

           _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     common_inputs["""input_ids"""].shape
           # Not using the same length for past_key_values
           _lowerCAmelCase						:       Dict  			=     seqlen + 2
           _lowerCAmelCase						:       Union[str, Any]  			=     (
               batch,
               self.num_attention_heads,
               past_key_values_length,
               self._config.hidden_size // self.num_attention_heads,
           )
           _lowerCAmelCase						:       Optional[Any]  			=     [
               (torch.zeros(a__  ), torch.zeros(a__  )) for _ in range(self.num_layers  )
           ]

       _lowerCAmelCase						:       List[str]  			=     common_inputs["""attention_mask"""]
       if self.use_past:
         _lowerCAmelCase						:       Tuple  			=     ordered_inputs["""attention_mask"""].dtype
         _lowerCAmelCase						:       List[Any]  			=     torch.cat(
             [ordered_inputs["""attention_mask"""], torch.ones(a__				,     a__				,     dtype=a__  )]				,     dim=1  )

       return ordered_inputs






     @property
     def     __A  (		self  ):
       return 13





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 50	)       ->      int:
  _lowerCAmelCase						:       int  			=     [1] * (length + 1)

  for row_length in range(3			,length + 1	):
    for block_length in range(3			,row_length + 1	):
      for block_start in range(row_length - block_length	):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


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




