





"""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


__magic_name__       :					List[str]						=			logging.get_logger(__name__)

__magic_name__       :					Any						=			{
    """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 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[int]										=  """gptj"""
			__lowerCAmelCase     :				Optional[Any]										=  {
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}







			def __init__(      self						,   _A=5_0_4_0_0						,   _A=2_0_4_8						,   _A=4_0_9_6						,   _A=2_8						,   _A=1_6						,   _A=6_4						,   _A=None						,   _A="gelu_new"						,   _A=0.0						,   _A=0.0						,   _A=0.0						,   _A=1e-5						,   _A=0.02						,   _A=True						,   _A=5_0_2_5_6						,   _A=5_0_2_5_6						,   _A=False						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Tuple				   =			vocab_size
									UpperCamelCase				:		Any				   =			n_positions
									UpperCamelCase				:		List[str]				   =			n_embd
									UpperCamelCase				:		List[str]				   =			n_layer
									UpperCamelCase				:		Optional[int]				   =			n_head
									UpperCamelCase				:		int				   =			n_inner
									UpperCamelCase				:		Optional[Any]				   =			rotary_dim
									UpperCamelCase				:		Optional[int]				   =			activation_function
									UpperCamelCase				:		str				   =			resid_pdrop
									UpperCamelCase				:		Union[str, Any]				   =			embd_pdrop
									UpperCamelCase				:		Optional[Any]				   =			attn_pdrop
									UpperCamelCase				:		Optional[int]				   =			layer_norm_epsilon
									UpperCamelCase				:		Any				   =			initializer_range
									UpperCamelCase				:		Optional[int]				   =			use_cache

									UpperCamelCase				:		List[Any]				   =			bos_token_id
									UpperCamelCase				:		List[str]				   =			eos_token_id

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






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







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






									'''simple docstring'''







									super().__init__(_A						,   task=_A						,   patching_specs=_A						,   use_past=_A    )
									if not getattr(self._config						,   """pad_token_id"""						,   _A    ):
															# TODO: how to do that better?
															UpperCamelCase				:		Optional[Any]				   =			0







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
									if self.use_past:
															self.fill_with_past_key_values_(_A						,   direction="""inputs"""    )
															UpperCamelCase				:		Any				   =			{0: """batch""", 1: """past_sequence + sequence"""}
									else:
															UpperCamelCase				:		List[Any]				   =			{0: """batch""", 1: """sequence"""}

									return common_inputs







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return self._config.n_layer







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return self._config.n_head







			def   _a      (      self						,   _A						,   _A = -1						,   _A = -1						,   _A = False						,   _A = None						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			super(_A						,   self    ).generate_dummy_inputs(
									    _A						,   batch_size=_A						,   seq_length=_A						,   is_pair=_A						,   framework=_A    )

									# We need to order the input in the way they appears in the forward()
									UpperCamelCase				:		Optional[Any]				   =			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				:		Dict				   =			common_inputs["""input_ids"""].shape
																					# Not using the same length for past_key_values
																					UpperCamelCase				:		Dict				   =			seqlen + 2
																					UpperCamelCase				:		Union[str, Any]				   =			(
																					    batch,
																					    self.num_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // self.num_attention_heads,
																					)
																					UpperCamelCase				:		List[Any]				   =			[
																					    (torch.zeros(_A    ), torch.zeros(_A    )) for _ in range(self.num_layers    )
																					]

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

									return ordered_inputs







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return 1_3









'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     __snake_case            =	abs(_lowerCamelCase						)
     __snake_case            =	0
     while n > 0:
          res += n % 10
          n //= 10
     return res







def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     __snake_case            =	abs(_lowerCamelCase						)
     return n if n < 10 else n % 10 + sum_of_digits(n // 10						)







def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





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







def       _UpperCamelCase			()-> None:




     '''simple docstring'''





     from collections.abc import Callable
     from timeit import timeit

     def benchmark_a_function(_lowerCamelCase				:  Callable ,						_lowerCamelCase				:  int						) -> None:
          __snake_case            =	f'''{func.__name__}({value})'''
          __snake_case            =	timeit(f'''__main__.{call}''' ,						setup='''import __main__'''						)
          print(f'''{call:56} = {func(_lowerCamelCase						)} -- {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(_lowerCamelCase ,						_lowerCamelCase						)
          print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()






