




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		:  str	=   logging.get_logger(__name__)

_UpperCAmelCase		:  Dict	=   {
    "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				(							lowerCAmelCase):
       _a           =  '''gptj'''
       _a           =  {
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }






       def __init__(      self:    List[str]				,    _lowerCAmelCase:    List[Any]=5_04_00				,    _lowerCAmelCase:    Tuple=20_48				,    _lowerCAmelCase:    List[str]=40_96				,    _lowerCAmelCase:    Dict=28				,    _lowerCAmelCase:    List[Any]=16				,    _lowerCAmelCase:    str=64				,    _lowerCAmelCase:    Optional[int]=None				,    _lowerCAmelCase:    Tuple="gelu_new"				,    _lowerCAmelCase:    List[str]=0.0				,    _lowerCAmelCase:    Optional[Any]=0.0				,    _lowerCAmelCase:    List[str]=0.0				,    _lowerCAmelCase:    str=1e-5				,    _lowerCAmelCase:    List[Any]=0.02				,    _lowerCAmelCase:    int=True				,    _lowerCAmelCase:    List[Any]=5_02_56				,    _lowerCAmelCase:    Any=5_02_56				,    _lowerCAmelCase:    Tuple=False				,    **_lowerCAmelCase:    Any				,    ):
         lowercase			:Dict							=  vocab_size
         lowercase			:Any							=  n_positions
         lowercase			:Union[str, Any]							=  n_embd
         lowercase			:Union[str, Any]							=  n_layer
         lowercase			:Optional[int]							=  n_head
         lowercase			:Any							=  n_inner
         lowercase			:Dict							=  rotary_dim
         lowercase			:Optional[Any]							=  activation_function
         lowercase			:Tuple							=  resid_pdrop
         lowercase			:Optional[Any]							=  embd_pdrop
         lowercase			:Any							=  attn_pdrop
         lowercase			:str							=  layer_norm_epsilon
         lowercase			:Dict							=  initializer_range
         lowercase			:Union[str, Any]							=  use_cache

         lowercase			:int							=  bos_token_id
         lowercase			:str							=  eos_token_id

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







class       __lowerCAmelCase				(							lowerCAmelCase):






       def __init__(      self:    int				,    _lowerCAmelCase:    PretrainedConfig				,    _lowerCAmelCase:    str = "default"				,    _lowerCAmelCase:    List[PatchingSpec] = None				,    _lowerCAmelCase:    bool = False				,    ):
         super().__init__(_lowerCAmelCase				,    task=_lowerCAmelCase				,    patching_specs=_lowerCAmelCase				,    use_past=_lowerCAmelCase )
         if not getattr(self._config				,    "pad_token_id"				,    _lowerCAmelCase ):
           # TODO: how to do that better?
           lowercase			:Optional[int]							=  0






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

         return common_inputs






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         return self._config.n_layer






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         return self._config.n_head






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple				,    _lowerCAmelCase:    PreTrainedTokenizer				,    _lowerCAmelCase:    int = -1				,    _lowerCAmelCase:    int = -1				,    _lowerCAmelCase:    bool = False				,    _lowerCAmelCase:    Optional[TensorType] = None				,    ):
         lowercase			:List[Any]							=  super(_lowerCAmelCase				,    self ).generate_dummy_inputs(
             _lowerCAmelCase				,    batch_size=_lowerCAmelCase				,    seq_length=_lowerCAmelCase				,    is_pair=_lowerCAmelCase				,    framework=_lowerCAmelCase )

         # We need to order the input in the way they appears in the forward()
         lowercase			:str							=  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

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

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

         return ordered_inputs






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         return 13









_UpperCAmelCase		:  Tuple	=   {str(digit): digit**5 for digit in range(10)}



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase      )      )



def 					UpperCAmelCase__     (   ):
  return sum(
      number
      for number in range(1000,	1000000      )
      if number == digits_fifth_powers_sum(lowerCamelCase      )      )


if __name__ == "__main__":
     print(solution())



