

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


a_    :						str    		=      logging.get_logger(__name__)

a_    :						Tuple    		=      {
    "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      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="gptj"
    __UpperCamelCase         ={
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(			self :      Optional[Any]							,    snake_case__ :      List[Any]=5_0_4_0_0							,    snake_case__ :      List[Any]=2_0_4_8							,    snake_case__ :      str=4_0_9_6							,    snake_case__ :      Dict=2_8							,    snake_case__ :      int=1_6							,    snake_case__ :      Tuple=6_4							,    snake_case__ :      Union[str, Any]=None							,    snake_case__ :      Any="gelu_new"							,    snake_case__ :      List[Any]=0.0							,    snake_case__ :      str=0.0							,    snake_case__ :      Tuple=0.0							,    snake_case__ :      Any=1E-5							,    snake_case__ :      Tuple=0.02							,    snake_case__ :      List[str]=True							,    snake_case__ :      Tuple=5_0_2_5_6							,    snake_case__ :      Union[str, Any]=5_0_2_5_6							,    snake_case__ :      Optional[Any]=False							,    **snake_case__ :      Optional[int]							,    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			bos_token_id
     SCREAMING_SNAKE_CASE								=			eos_token_id

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




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      Optional[Any]							,    snake_case__ :      PretrainedConfig							,    snake_case__ :      str = "default"							,    snake_case__ :      List[PatchingSpec] = None							,    snake_case__ :      bool = False							,    ):

     """simple docstring"""






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

    @property
    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






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

     return common_inputs

    @property
    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     return self._config.n_layer

    @property
    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     return self._config.n_head

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      PreTrainedTokenizer							,    snake_case__ :      int = -1							,    snake_case__ :      int = -1							,    snake_case__ :      bool = False							,    snake_case__ :      Optional[TensorType] = None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			super(snake_case__							,    self    ).generate_dummy_inputs(
         snake_case__							,    batch_size=snake_case__							,    seq_length=snake_case__							,    is_pair=snake_case__							,    framework=snake_case__    )

     # We need to order the input in the way they appears in the forward()
     SCREAMING_SNAKE_CASE								=			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

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

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

     return ordered_inputs




    @property
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     return 1_3




def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			abs(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			0
 while n > 0:
  res += n % 10
  n //= 10
 return res


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			abs(_UpperCamelCase	)
 return n if n < 10 else n % 10 + sum_of_digits(n // 10	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 return sum(int(_UpperCamelCase	) for c in str(abs(_UpperCamelCase	)	)	)


def        __lowerCAmelCase    (       )		->							None:
 '''simple docstring'''


 from collections.abc import Callable
 from timeit import timeit

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

 for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
  for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
   benchmark_a_function(_UpperCamelCase		,							_UpperCamelCase	)
  print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()

