




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

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


_UpperCAmelCase		:  Tuple	=   logging.get_logger(__name__)

_UpperCAmelCase		:  Union[str, Any]	=   {
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  '''gpt_neo'''
       _a           =  ['''past_key_values''']
       _a           =  {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






       def __init__(      self:    int				,    _lowerCAmelCase:    List[str]=5_02_57				,    _lowerCAmelCase:    str=20_48				,    _lowerCAmelCase:    Optional[Any]=20_48				,    _lowerCAmelCase:    Dict=24				,    _lowerCAmelCase:    str=[[["global", "local"], 12]]				,    _lowerCAmelCase:    Any=16				,    _lowerCAmelCase:    Tuple=None				,    _lowerCAmelCase:    Dict=2_56				,    _lowerCAmelCase:    Optional[Any]="gelu_new"				,    _lowerCAmelCase:    Dict=0.0				,    _lowerCAmelCase:    str=0.0				,    _lowerCAmelCase:    List[Any]=0.0				,    _lowerCAmelCase:    List[Any]=0.1				,    _lowerCAmelCase:    Any=1e-5				,    _lowerCAmelCase:    Union[str, Any]=0.02				,    _lowerCAmelCase:    Any=True				,    _lowerCAmelCase:    List[str]=5_02_56				,    _lowerCAmelCase:    List[str]=5_02_56				,    **_lowerCAmelCase:    Union[str, Any]				,    ):
         lowercase			:str							=  vocab_size
         lowercase			:Tuple							=  max_position_embeddings
         lowercase			:Any							=  hidden_size
         lowercase			:List[str]							=  num_layers
         lowercase			:Any							=  num_heads
         lowercase			:int							=  intermediate_size
         lowercase			:Union[str, Any]							=  window_size
         lowercase			:List[str]							=  activation_function
         lowercase			:str							=  resid_dropout
         lowercase			:int							=  embed_dropout
         lowercase			:List[str]							=  attention_dropout
         lowercase			:Union[str, Any]							=  classifier_dropout
         lowercase			:List[Any]							=  layer_norm_epsilon
         lowercase			:Any							=  initializer_range
         lowercase			:Optional[Any]							=  use_cache

         lowercase			:Union[str, Any]							=  bos_token_id
         lowercase			:Any							=  eos_token_id

         lowercase			:str							=  attention_types
         lowercase			:int							=  self.expand_attention_types_params(_lowerCAmelCase )

         if len(self.attention_layers ) != self.num_layers:
           raise ValueError(
               "Configuration for convolutional module is incorrect. "
               "It is required that `len(config.attention_layers)` == `config.num_layers` "
               F"but is `len(config.attention_layers) = {len(self.attention_layers )}`, "
               F"`config.num_layers = {self.num_layers}`. "
               "`config.attention_layers` is prepared using `config.attention_types`. "
               "Please verify the value of `config.attention_types` argument." )

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






       @staticmethod
       def     SCREAMING_SNAKE_CASE			(      _lowerCAmelCase:    str ):
         lowercase			:List[str]							=  []
         for item in attention_types:
           for _ in range(item[1] ):
             attentions.extend(item[0] )
         return attentions







def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  import torch

  lowercase			:Optional[int]							=  input.size()
  lowercase			:int							=  len(lowerCamelCase      )
  lowercase			:List[Any]							=  shape[dimension]

  lowercase			:int							=  torch.arange(0,	lowerCamelCase,	lowerCamelCase      )
  lowercase			:int							=  torch.div(sizedim - size,	lowerCamelCase,	rounding_mode="floor"      ) + 1
  lowercase			:List[str]							=  torch.arange(lowerCamelCase      ) + low_indices[:min_length][:, None]

  lowercase			:Union[str, Any]							=  [slice(lowerCamelCase      )] * rank
  lowercase			:str							=  indices
  lowercase			:Optional[Any]							=  input[s]

  lowercase			:List[str]							=  list(range(0,	rank + 1      )      )
  perm.append(perm.pop(dimension + 1      )      )

  return sliced.permute(lowerCamelCase      )



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  import torch

  lowercase			:Optional[int]							=  torch.arange(1,	lowerCamelCase      )
  lowercase			:List[str]							=  torch.remainder(lowerCamelCase,	lowerCamelCase      )
  lowercase			:Any							=  remainders == 0
  lowercase			:Any							=  candidates[divisor_indices]
  lowercase			:Optional[Any]							=  torch.max(lowerCamelCase      )
  return largest_divisor, torch.div(lowerCamelCase,	lowerCamelCase,	rounding_mode="floor"      )







class       __lowerCAmelCase				(							lowerCAmelCase):






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

         return common_inputs






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         return self._config.num_heads






       def     SCREAMING_SNAKE_CASE			(      self:    Any				,    _lowerCAmelCase:    PreTrainedTokenizer				,    _lowerCAmelCase:    int = -1				,    _lowerCAmelCase:    int = -1				,    _lowerCAmelCase:    bool = False				,    _lowerCAmelCase:    Optional[TensorType] = None				,    ):
         lowercase			:str							=  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			:Any							=  common_inputs["input_ids"].shape
             # Not using the same length for past_key_values
             lowercase			:Tuple							=  seqlen + 2
             lowercase			:List[str]							=  (
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             lowercase			:Optional[Any]							=  [
                 (torch.zeros(_lowerCAmelCase ), torch.zeros(_lowerCAmelCase )) for _ in range(self.num_layers )
             ]

         lowercase			:List[Any]							=  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())



