


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


snake_case							:	List[Any]					      =				logging.get_logger(__name__)

snake_case							:	str					      =				{
    '''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 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Dict   				=      '''gptj'''
					UpperCAmelCase__ :   List[Any]   				=      {
					    '''max_position_embeddings''': '''n_positions''',
					    '''hidden_size''': '''n_embd''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}
					def __init__(				self       :Dict      ,__snake_case       :List[Any]=5_04_00      ,__snake_case       :int=20_48      ,__snake_case       :List[str]=40_96      ,__snake_case       :List[Any]=28      ,__snake_case       :List[Any]=16      ,__snake_case       :Tuple=64      ,__snake_case       :str=None      ,__snake_case       :str="gelu_new"      ,__snake_case       :Dict=0.0      ,__snake_case       :int=0.0      ,__snake_case       :Optional[Any]=0.0      ,__snake_case       :Tuple=1E-5      ,__snake_case       :Optional[int]=0.02      ,__snake_case       :Union[str, Any]=True      ,__snake_case       :List[str]=5_02_56      ,__snake_case       :Union[str, Any]=5_02_56      ,__snake_case       :str=False      ,**__snake_case       :Union[str, Any]      ,) ->   Dict:
												a__					=					vocab_size
												a__					=					n_positions
												a__					=					n_embd
												a__					=					n_layer
												a__					=					n_head
												a__					=					n_inner
												a__					=					rotary_dim
												a__					=					activation_function
												a__					=					resid_pdrop
												a__					=					embd_pdrop
												a__					=					attn_pdrop
												a__					=					layer_norm_epsilon
												a__					=					initializer_range
												a__					=					use_cache

												a__					=					bos_token_id
												a__					=					eos_token_id

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







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Tuple      ,__snake_case       :PretrainedConfig      ,__snake_case       :str = "default"      ,__snake_case       :List[PatchingSpec] = None      ,__snake_case       :bool = False      ,) ->   str:
												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?
																			a__					=					0
					@property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Mapping[str, Mapping[int, str]]:
												a__					=					OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}				)
												if self.use_past:
																			self.fill_with_past_key_values_(__snake_case      ,direction='inputs'				)
																			a__					=					{0: 'batch', 1: 'past_sequence + sequence'}
												else:
																			a__					=					{0: 'batch', 1: 'sequence'}

												return common_inputs
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   int:
												return self._config.n_layer
					@property
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   int:
												return self._config.n_head
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :PreTrainedTokenizer      ,__snake_case       :int = -1      ,__snake_case       :int = -1      ,__snake_case       :bool = False      ,__snake_case       :Optional[TensorType] = None      ,) ->   Mapping[str, Any]:
												a__					=					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()
												a__					=					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

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

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

												return ordered_inputs



					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   int:
												return 13








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float       ):
							if (voltage, current, resistance).count(0       ) != 1:
														raise ValueError('One and only one argument must be 0'       )
							if resistance < 0:
														raise ValueError('Resistance cannot be negative'       )
							if voltage == 0:
														return {"voltage": float(current * resistance       )}
							elif current == 0:
														return {"current": voltage / resistance}
							elif resistance == 0:
														return {"resistance": voltage / current}
							else:
														raise ValueError('Exactly one argument must be 0'       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




