


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__       					=      logging.get_logger(__name__)

__magic_name__       					=      {
    '''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    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		Dict  					=	'''gpt2'''
						__UpperCAmelCase   :		Dict  					=	['''past_key_values''']
						__UpperCAmelCase   :		List[str]  					=	{
						    '''hidden_size''': '''n_embd''',
						    '''max_position_embeddings''': '''n_positions''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}




						def __init__(   self				,							a_=5_0257				,							a_=1024				,							a_=768				,							a_=12				,							a_=12				,							a_=None				,							a_="gelu_new"				,							a_=0.1				,							a_=0.1				,							a_=0.1				,							a_=1E-5				,							a_=0.02				,							a_="cls_index"				,							a_=True				,							a_=None				,							a_=True				,							a_=0.1				,							a_=True				,							a_=True				,							a_=5_0256				,							a_=5_0256				,							a_=False				,							a_=False				,							**a_				,							):
							lowerCamelCase_			:							Optional[Any]      =       vocab_size
							lowerCamelCase_			:							Union[str, Any]      =       n_positions
							lowerCamelCase_			:							int      =       n_embd
							lowerCamelCase_			:							List[Any]      =       n_layer
							lowerCamelCase_			:							Optional[Any]      =       n_head
							lowerCamelCase_			:							Union[str, Any]      =       n_inner
							lowerCamelCase_			:							Dict      =       activation_function
							lowerCamelCase_			:							List[str]      =       resid_pdrop
							lowerCamelCase_			:							Any      =       embd_pdrop
							lowerCamelCase_			:							int      =       attn_pdrop
							lowerCamelCase_			:							Any      =       layer_norm_epsilon
							lowerCamelCase_			:							int      =       initializer_range
							lowerCamelCase_			:							List[Any]      =       summary_type
							lowerCamelCase_			:							str      =       summary_use_proj
							lowerCamelCase_			:							Any      =       summary_activation
							lowerCamelCase_			:							Optional[int]      =       summary_first_dropout
							lowerCamelCase_			:							List[Any]      =       summary_proj_to_labels
							lowerCamelCase_			:							str      =       scale_attn_weights
							lowerCamelCase_			:							List[Any]      =       use_cache
							lowerCamelCase_			:							Optional[Any]      =       scale_attn_by_inverse_layer_idx
							lowerCamelCase_			:							Any      =       reorder_and_upcast_attn

							lowerCamelCase_			:							Optional[int]      =       bos_token_id
							lowerCamelCase_			:							int      =       eos_token_id

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





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_ = "default"				,							a_ = None				,							a_ = False				,							):
							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?
								lowerCamelCase_			:							Optional[Any]      =       0




						@property
						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							str      =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
							if self.use_past:
								self.fill_with_past_key_values_(a_				,							direction="inputs"     )
								lowerCamelCase_			:							Optional[Any]      =       {0: "batch", 1: "past_sequence + sequence"}
							else:
								lowerCamelCase_			:							str      =       {0: "batch", 1: "sequence"}

							return common_inputs




						@property
						def 					_UpperCamelCase			(   self     ):
							return self._config.n_layer




						@property
						def 					_UpperCamelCase			(   self     ):
							return self._config.n_head




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = -1				,							a_ = -1				,							a_ = False				,							a_ = None				,							):
							lowerCamelCase_			:							str      =       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()
							lowerCamelCase_			:							str      =       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

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

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

							return ordered_inputs




						@property
						def 					_UpperCamelCase			(   self     ):
							return 13





__magic_name__       					=      {
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 2_4,
    '''5''': 1_2_0,
    '''6''': 7_2_0,
    '''7''': 5_0_4_0,
    '''8''': 4_0_3_2_0,
    '''9''': 3_6_2_8_8_0,
}

__magic_name__       					=      {1_4_5: 1_4_5}

__magic_name__       					=      {
    1_4_5: 0,
    1_6_9: 3,
    3_6_3_0_1: 3,
    1_4_5_4: 3,
    8_7_1: 2,
    4_5_3_6_1: 2,
    8_7_2: 2,
}







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	if n in CACHE_SUM_DIGIT_FACTORIALS:
		return CACHE_SUM_DIGIT_FACTORIALS[n]
	lowerCamelCase_			:							List[str]      =       sum(DIGIT_FACTORIALS[let] for let in str(lowerCAmelCase_))
	lowerCamelCase_			:							List[str]      =       ret
	return ret







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_ = None):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       previous or set()
	if n in CHAIN_LENGTH_CACHE:
		return CHAIN_LENGTH_CACHE[n]
	lowerCamelCase_			:							str      =       sum_digit_factorials(lowerCAmelCase_)
	if next_number in previous:
		lowerCamelCase_			:							List[str]      =       0
		return 0
	else:
		previous.add(lowerCAmelCase_)
		lowerCamelCase_			:							List[Any]      =       1 + chain_length(lowerCAmelCase_      ,					lowerCAmelCase_)
		lowerCamelCase_			:							Optional[int]      =       ret
		return ret







def     __magic_name__  (      lowerCAmelCase_ = 60      ,					lowerCAmelCase_ = 100_0000):
	'''simple docstring'''


	return sum(1 for i in range(1      ,					lowerCAmelCase_) if chain_length(lowerCAmelCase_) == num_terms)


if __name__ == "__main__":
							print(f'''{solution() = }''')

