



"""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


__a      =				logging.get_logger(__name__)

__a      =				{
    "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				):






					'''simple docstring'''





					_A		:			List[Any]						=     """gptj"""
					_A		:			Union[str, Any]						=     {
					    """max_position_embeddings""": """n_positions""",
					    """hidden_size""": """n_embd""",
					    """num_attention_heads""": """n_head""",
					    """num_hidden_layers""": """n_layer""",
					}






					def __init__(       self:							int						,     snake_case:							int=50_400						,     snake_case:							Optional[Any]=2_048						,     snake_case:							Any=4_096						,     snake_case:							Dict=28						,     snake_case:							Union[str, Any]=16						,     snake_case:							Optional[int]=64						,     snake_case:							List[Any]=None						,     snake_case:							List[str]="gelu_new"						,     snake_case:							Dict=0.0						,     snake_case:							Union[str, Any]=0.0						,     snake_case:							List[Any]=0.0						,     snake_case:							List[Any]=1E-5						,     snake_case:							Any=0.0_2						,     snake_case:							Union[str, Any]=True						,     snake_case:							int=50_256						,     snake_case:							int=50_256						,     snake_case:							List[Any]=False						,     **snake_case:							List[str]						,     )   ->	Optional[Any]:
											snake_case_ :Optional[Any]						=			vocab_size
											snake_case_ :List[Any]						=			n_positions
											snake_case_ :List[str]						=			n_embd
											snake_case_ :List[str]						=			n_layer
											snake_case_ :int						=			n_head
											snake_case_ :int						=			n_inner
											snake_case_ :List[str]						=			rotary_dim
											snake_case_ :Optional[Any]						=			activation_function
											snake_case_ :int						=			resid_pdrop
											snake_case_ :List[str]						=			embd_pdrop
											snake_case_ :str						=			attn_pdrop
											snake_case_ :Union[str, Any]						=			layer_norm_epsilon
											snake_case_ :Optional[Any]						=			initializer_range
											snake_case_ :Any						=			use_cache

											snake_case_ :Tuple						=			bos_token_id
											snake_case_ :Any						=			eos_token_id

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

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __init__(       self:							int						,     snake_case:							PretrainedConfig						,     snake_case:							str = "default"						,     snake_case:							List[PatchingSpec] = None						,     snake_case:							bool = False						,     )   ->	Any:
											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?
																	snake_case_ :Optional[Any]						=			0






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Mapping[str, Mapping[int, str]]:
											snake_case_ :Tuple						=			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}						)
											if self.use_past:
																	self.fill_with_past_key_values_(snake_case						,     direction="""inputs"""						)
																	snake_case_ :Optional[Any]						=			{0: """batch""", 1: """past_sequence + sequence"""}
											else:
																	snake_case_ :Tuple						=			{0: """batch""", 1: """sequence"""}

											return common_inputs






					@property
					def    lowerCAmelCase_				(       self:							Tuple						)   ->	int:
											return self._config.n_layer






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	int:
											return self._config.n_head






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							PreTrainedTokenizer						,     snake_case:							int = -1						,     snake_case:							int = -1						,     snake_case:							bool = False						,     snake_case:							Optional[TensorType] = None						,     )   ->	Mapping[str, Any]:
											snake_case_ :Tuple						=			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()
											snake_case_ :int						=			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

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

											snake_case_ :Dict						=			common_inputs["""attention_mask"""]
											if self.use_past:
																	snake_case_ :Optional[int]						=			ordered_inputs["""attention_mask"""].dtype
																	snake_case_ :List[str]						=			torch.cat(
																	    [ordered_inputs["""attention_mask"""], torch.ones(snake_case						,     snake_case						,     dtype=snake_case						)]						,     dim=1						)

											return ordered_inputs






					@property
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	int:
											return 13


import os




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					os.path.join(os.path.dirname(snake_case__					)						,   '''num.txt'''					)
				with open(snake_case__					) as file_hand:
								return str(sum(int(snake_case__					) for line in file_hand					)					)[:10]


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




