





"""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


_snake_case	:						Union[str, Any]					=					logging.get_logger(__name__)

_snake_case	:						List[str]					=					{
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''gpt2'''
					UpperCamelCase						=				['''past_key_values''']
					UpperCamelCase						=				{
					    '''hidden_size''': '''n_embd''',
					    '''max_position_embeddings''': '''n_positions''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}


					def __init__(   self     :Any	,			__UpperCamelCase     :int=5_02_57	,			__UpperCamelCase     :List[str]=10_24	,			__UpperCamelCase     :str=7_68	,			__UpperCamelCase     :str=12	,			__UpperCamelCase     :Optional[Any]=12	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :Optional[Any]="gelu_new"	,			__UpperCamelCase     :int=0.1	,			__UpperCamelCase     :int=0.1	,			__UpperCamelCase     :List[str]=0.1	,			__UpperCamelCase     :Optional[Any]=1e-5	,			__UpperCamelCase     :Union[str, Any]=0.02	,			__UpperCamelCase     :Any="cls_index"	,			__UpperCamelCase     :Dict=True	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :Optional[int]=0.1	,			__UpperCamelCase     :Optional[int]=True	,			__UpperCamelCase     :Optional[int]=True	,			__UpperCamelCase     :Any=5_02_56	,			__UpperCamelCase     :List[Any]=5_02_56	,			__UpperCamelCase     :Optional[int]=False	,			__UpperCamelCase     :int=False	,			**__UpperCamelCase     :Any	,			):
												A						=    vocab_size
												A						=    n_positions
												A						=    n_embd
												A						=    n_layer
												A						=    n_head
												A						=    n_inner
												A						=    activation_function
												A						=    resid_pdrop
												A						=    embd_pdrop
												A						=    attn_pdrop
												A						=    layer_norm_epsilon
												A						=    initializer_range
												A						=    summary_type
												A						=    summary_use_proj
												A						=    summary_activation
												A						=    summary_first_dropout
												A						=    summary_proj_to_labels
												A						=    scale_attn_weights
												A						=    use_cache
												A						=    scale_attn_by_inverse_layer_idx
												A						=    reorder_and_upcast_attn

												A						=    bos_token_id
												A						=    eos_token_id

												super().__init__(bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			**__UpperCamelCase						)







class    _UpperCAmelCase  (      lowercase_					):


					def __init__(   self     :Tuple	,			__UpperCamelCase     :PretrainedConfig	,			__UpperCamelCase     :str = "default"	,			__UpperCamelCase     :List[PatchingSpec] = None	,			__UpperCamelCase     :bool = False	,			):
												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?
																			A						=    0


					@property
					def       lowerCamelCase			(   self     :str						):
												A						=    OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
												if self.use_past:
																			self.fill_with_past_key_values_(__UpperCamelCase	,			direction="inputs"						)
																			A						=    {0: "batch", 1: "past_sequence + sequence"}
												else:
																			A						=    {0: "batch", 1: "sequence"}

												return common_inputs


					@property
					def       lowerCamelCase			(   self     :str						):
												return self._config.n_layer


					@property
					def       lowerCamelCase			(   self     :Tuple						):
												return self._config.n_head


					def       lowerCamelCase			(   self     :Union[str, Any]	,			__UpperCamelCase     :PreTrainedTokenizer	,			__UpperCamelCase     :int = -1	,			__UpperCamelCase     :int = -1	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :Optional[TensorType] = None	,			):
												A						=    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()
												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(__UpperCamelCase						), torch.zeros(__UpperCamelCase						)) 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(__UpperCamelCase	,			__UpperCamelCase	,			dtype=__UpperCamelCase						)]	,			dim=1						)

												return ordered_inputs


					@property
					def       lowerCamelCase			(   self     :Dict						):
												return 13









"""simple docstring"""
from __future__ import annotations






def    A__							(     UpperCamelCase			):
							A						=    42
							A						=    list(UpperCamelCase			)
							A,      A						=    _vector[:-1], int(_vector[-1]			)
							A						=    [int(UpperCamelCase			) for digit in __vector]

							vector.reverse()
							for i, digit in enumerate(UpperCamelCase			):
														if i & 1 == 0:
																					A						=    digit * 2
																					if doubled > 9:
																												doubled -= 9
																					check_digit += doubled
														else:
																					check_digit += digit

							return check_digit % 10 == 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		assert is_luhn('79927398713')
		assert not is_luhn('79927398714')


