
"""simple docstring"""






def 						_snake_case       (	lowercase__		:  int = 1_0		)  ->				str:


			'''simple docstring'''





			if not isinstance(lowercase__      ,     lowercase__		) or n < 0:
						raise ValueError("""Invalid input"""		)
			lowerCAmelCase_    :List[str]			     =  1_0**n
			lowerCAmelCase_    :int			     =  2_8_4_3_3 * (pow(2      ,     7_8_3_0_4_5_7      ,     lowercase__		)) + 1
			return str(number % modulus		)


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				print(F"""{solution(10) = }""")











'''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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "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			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'gptj'
 __lowerCamelCase    :       List[str]          =       {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self ,   A=50_400 ,   A=2_048 ,   A=4_096 ,   A=28 ,   A=16 ,   A=64 ,   A=None ,   A="gelu_new" ,   A=0.0 ,   A=0.0 ,   A=0.0 ,   A=1E-5 ,   A=0.02 ,   A=True ,   A=50_256 ,   A=50_256 ,   A=False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  vocab_size
      _a      =  n_positions
      _a      =  n_embd
      _a      =  n_layer
      _a      =  n_head
      _a      =  n_inner
      _a      =  rotary_dim
      _a      =  activation_function
      _a      =  resid_pdrop
      _a      =  embd_pdrop
      _a      =  attn_pdrop
      _a      =  layer_norm_epsilon
      _a      =  initializer_range
      _a      =  use_cache

      _a      =  bos_token_id
      _a      =  eos_token_id

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






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A = "default" ,   A = None ,   A = False ,   )    ->     List[str]:


      """simple docstring"""



      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?
           _a      =  0



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      _a      =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
      if self.use_past:
           self.fill_with_past_key_values_(A ,   direction='''inputs'''       )
           _a      =  {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_head



 def 					a__  (self ,   A ,   A = -1 ,   A = -1 ,   A = False ,   A = None ,   )    ->     Mapping[str, Any]:


      """simple docstring"""



      _a      =  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()
      _a      =  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

                _a  ,    _a      =  common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                _a      =  seqlen + 2
                _a      =  (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                _a      =  [
                    (torch.zeros(A       ), torch.zeros(A       )) for _ in range(self.num_layers       )
                ]

      _a      =  common_inputs['''attention_mask''']
      if self.use_past:
           _a      =  ordered_inputs['''attention_mask'''].dtype
           _a      =  torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A ,   A ,   dtype=A       )] ,   dim=1       )

      return ordered_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 13



