



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


__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			{
    '''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 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   Union[str, Any]  	=    "gptj"
       UpperCAmelCase__							:   Optional[int]  	=    {
           "max_position_embeddings": "n_positions",
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }
       def __init__(     self,     SCREAMING_SNAKE_CASE_=5_0400,     SCREAMING_SNAKE_CASE_=2048,     SCREAMING_SNAKE_CASE_=4096,     SCREAMING_SNAKE_CASE_=28,     SCREAMING_SNAKE_CASE_=16,     SCREAMING_SNAKE_CASE_=64,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_="gelu_new",     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=0.0,     SCREAMING_SNAKE_CASE_=1e-5,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=5_0256,     SCREAMING_SNAKE_CASE_=5_0256,     SCREAMING_SNAKE_CASE_=False,     **SCREAMING_SNAKE_CASE_,     )	->						Optional[Any]:
          UpperCamelCase       :    Optional[int]		      =      vocab_size
          UpperCamelCase       :    Dict		      =      n_positions
          UpperCamelCase       :    Any		      =      n_embd
          UpperCamelCase       :    Tuple		      =      n_layer
          UpperCamelCase       :    Dict		      =      n_head
          UpperCamelCase       :    str		      =      n_inner
          UpperCamelCase       :    str		      =      rotary_dim
          UpperCamelCase       :    Any		      =      activation_function
          UpperCamelCase       :    Union[str, Any]		      =      resid_pdrop
          UpperCamelCase       :    Dict		      =      embd_pdrop
          UpperCamelCase       :    Tuple		      =      attn_pdrop
          UpperCamelCase       :    str		      =      layer_norm_epsilon
          UpperCamelCase       :    Tuple		      =      initializer_range
          UpperCamelCase       :    Any		      =      use_cache

          UpperCamelCase       :    Optional[int]		      =      bos_token_id
          UpperCamelCase       :    List[str]		      =      eos_token_id

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






class 							lowerCAmelCase_						(    a__				):
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = "default",     SCREAMING_SNAKE_CASE_ = None,     SCREAMING_SNAKE_CASE_ = False,     )	->						Optional[int]:
          super().__init__(SCREAMING_SNAKE_CASE_,     task=SCREAMING_SNAKE_CASE_,     patching_specs=SCREAMING_SNAKE_CASE_,     use_past=SCREAMING_SNAKE_CASE_			)
          if not getattr(self._config,     'pad_token_id',     SCREAMING_SNAKE_CASE_			):
             # TODO: how to do that better?
             UpperCamelCase       :    Optional[Any]		      =      0
       @property
       def    snake_case_			(     self			)	->						Mapping[str, Mapping[int, str]]:
          UpperCamelCase       :    List[Any]		      =      OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
          if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,     direction='inputs'			)
             UpperCamelCase       :    List[Any]		      =      {0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase       :    List[Any]		      =      {0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def    snake_case_			(     self			)	->						int:
          return self._config.n_layer
       @property
       def    snake_case_			(     self			)	->						int:
          return self._config.n_head
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = -1,     SCREAMING_SNAKE_CASE_ = -1,     SCREAMING_SNAKE_CASE_ = False,     SCREAMING_SNAKE_CASE_ = None,     )	->						Mapping[str, Any]:
          UpperCamelCase       :    Union[str, Any]		      =      super(SCREAMING_SNAKE_CASE_,     self			).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,     batch_size=SCREAMING_SNAKE_CASE_,     seq_length=SCREAMING_SNAKE_CASE_,     is_pair=SCREAMING_SNAKE_CASE_,     framework=SCREAMING_SNAKE_CASE_			)

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase       :    Optional[int]		      =      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

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

          UpperCamelCase       :    Dict		      =      common_inputs['attention_mask']
          if self.use_past:
             UpperCamelCase       :    Optional[Any]		      =      ordered_inputs['attention_mask'].dtype
             UpperCamelCase       :    List[str]		      =      torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     dtype=SCREAMING_SNAKE_CASE_			)],     dim=1			)

          return ordered_inputs






       @property
       def    snake_case_			(     self			)	->						int:
          return 13





'''simple docstring'''

from math import factorial

snake_case									=    {str(digit): factorial(digit) for digit in range(10)}


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			):
  raise TypeError("Parameter number must be int"			)

 if number < 0:
  raise ValueError("Parameter number must be greater than or equal to 0"			)

 # Converts number in string to iterate on its digits and adds its factorial.
 return sum(DIGIT_FACTORIAL[digit] for digit in str(lowerCamelCase_			)			)


def        UpperCAmelCase_ (  lowerCamelCase_ = 6_0      ,			lowerCamelCase_ = 1_0_0_0_0_0_0			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not isinstance(lowerCamelCase_      ,			lowerCamelCase_			):
  raise TypeError("Parameters chain_length and number_limit must be int"			)

 if chain_length <= 0 or number_limit <= 0:
  raise ValueError(
      "Parameters chain_length and number_limit must be greater than 0"			)

 # the counter for the chains with the exact desired length
 lowerCAmelCase__     :   str     		=	0
 # the cached sizes of the previous chains
 lowerCAmelCase__     :   dict[int, int]     		=	{}

 for start_chain_element in range(1      ,			lowerCamelCase_			):
  # The temporary set will contain the elements of the chain
  lowerCAmelCase__     :   Any     		=	set()
  lowerCAmelCase__     :   int     		=	0

  # Stop computing the chain when you find a cached size, a repeating item or the
  # length is greater then the desired one.
  lowerCAmelCase__     :   Dict     		=	start_chain_element
  while (
      chain_element not in chain_sets_lengths
      and chain_element not in chain_set
      and chain_set_length <= chain_length
  ):
   chain_set.add(lowerCamelCase_			)
   chain_set_length += 1
   lowerCAmelCase__     :   Dict     		=	digit_factorial_sum(lowerCamelCase_			)

  if chain_element in chain_sets_lengths:
   chain_set_length += chain_sets_lengths[chain_element]

  lowerCAmelCase__     :   Optional[Any]     		=	chain_set_length

  # If chain contains the exact amount of elements increase the counter
  if chain_set_length == chain_length:
   chains_counter += 1

 return chains_counter


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f'{solution()}')





