
"""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


UpperCamelCase     :    Any       =			logging.get_logger(__name__)

UpperCamelCase     :    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 __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"gptj"
     lowercase						       =						{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }





     def __init__(     self     ,					__UpperCAmelCase=5_0400     ,					__UpperCAmelCase=2048     ,					__UpperCAmelCase=4096     ,					__UpperCAmelCase=28     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=64     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase="gelu_new"     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=5_0256     ,					__UpperCAmelCase=5_0256     ,					__UpperCAmelCase=False     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




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

      __UpperCamelCase      =	bos_token_id
      __UpperCamelCase      =	eos_token_id

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


class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = "default"     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase     ,					task=__UpperCAmelCase     ,					patching_specs=__UpperCAmelCase     ,					use_past=__UpperCAmelCase				)
      if not getattr(self._config     ,					'pad_token_id'     ,					__UpperCAmelCase				):
       # TODO: how to do that better?
       __UpperCamelCase      =	0





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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

      return common_inputs





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return self._config.n_layer





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return self._config.n_head





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	super(__UpperCAmelCase     ,					self				).generate_dummy_inputs(
          __UpperCAmelCase     ,					batch_size=__UpperCAmelCase     ,					seq_length=__UpperCAmelCase     ,					is_pair=__UpperCAmelCase     ,					framework=__UpperCAmelCase				)

      # We need to order the input in the way they appears in the forward()
      __UpperCamelCase      =	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      =	common_inputs['input_ids'].shape
        # Not using the same length for past_key_values
        __UpperCamelCase      =	seqlen + 2
        __UpperCamelCase      =	(
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        __UpperCamelCase      =	[
            (torch.zeros(__UpperCAmelCase				), torch.zeros(__UpperCAmelCase				)) for _ in range(self.num_layers				)
        ]

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

      return ordered_inputs







     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return 13





"""simple docstring"""

def 							A						(    snake_case				:int		)				->     int:
 __UpperCamelCase      =	abs(snake_case		)
 __UpperCamelCase      =	0
 while n > 0:
  res += n % 1_0
  n //= 1_0
 return res






def 							A						(    snake_case				:int		)				->     int:
 __UpperCamelCase      =	abs(snake_case		)
 return n if n < 1_0 else n % 1_0 + sum_of_digits(n // 1_0		)






def 							A						(    snake_case				:int		)				->     int:
 return sum(int(snake_case		) for c in str(abs(snake_case		)		)		)






def 							A						(    )				->     None:
 from collections.abc import Callable
 from timeit import timeit

 def benchmark_a_function(snake_case				:Callable    , snake_case				:int		) -> None:
  __UpperCamelCase      =	f'{func.__name__}({value})'
  __UpperCamelCase      =	timeit(f'__main__.{call}'    , setup='import __main__'		)
  print(f'{call:56} = {func(snake_case		)} -- {timing:.4f} seconds'		)

 for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
  for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
   benchmark_a_function(snake_case    , snake_case		)
  print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



