
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


__SCREAMING_SNAKE_CASE	:      int			   =   logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:      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 						__magic_name__      (  __SCREAMING_SNAKE_CASE			):
		_lowerCAmelCase						=		"gpt2"
		_lowerCAmelCase						=		["past_key_values"]
		_lowerCAmelCase						=		{
		    "hidden_size": "n_embd",
		    "max_position_embeddings": "n_positions",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}





		def __init__(							self			:		List[str]  ,     lowerCamelCase__			:		Any=5_0_2_5_7  ,     lowerCamelCase__			:		Tuple=1_0_2_4  ,     lowerCamelCase__			:		Dict=7_6_8  ,     lowerCamelCase__			:		int=1_2  ,     lowerCamelCase__			:		List[str]=1_2  ,     lowerCamelCase__			:		Optional[int]=None  ,     lowerCamelCase__			:		int="gelu_new"  ,     lowerCamelCase__			:		Tuple=0.1  ,     lowerCamelCase__			:		int=0.1  ,     lowerCamelCase__			:		List[str]=0.1  ,     lowerCamelCase__			:		Union[str, Any]=1E-5  ,     lowerCamelCase__			:		Dict=0.0_2  ,     lowerCamelCase__			:		Optional[Any]="cls_index"  ,     lowerCamelCase__			:		str=True  ,     lowerCamelCase__			:		int=None  ,     lowerCamelCase__			:		Tuple=True  ,     lowerCamelCase__			:		Any=0.1  ,     lowerCamelCase__			:		Tuple=True  ,     lowerCamelCase__			:		Dict=True  ,     lowerCamelCase__			:		int=5_0_2_5_6  ,     lowerCamelCase__			:		Tuple=5_0_2_5_6  ,     lowerCamelCase__			:		str=False  ,     lowerCamelCase__			:		Optional[Any]=False  ,     **lowerCamelCase__			:		Optional[Any]  ,     ):
							lowerCAmelCase						:    Any     		=   vocab_size
							lowerCAmelCase						:    List[str]     		=   n_positions
							lowerCAmelCase						:    Optional[int]     		=   n_embd
							lowerCAmelCase						:    Optional[Any]     		=   n_layer
							lowerCAmelCase						:    Union[str, Any]     		=   n_head
							lowerCAmelCase						:    Union[str, Any]     		=   n_inner
							lowerCAmelCase						:    Union[str, Any]     		=   activation_function
							lowerCAmelCase						:    int     		=   resid_pdrop
							lowerCAmelCase						:    str     		=   embd_pdrop
							lowerCAmelCase						:    List[str]     		=   attn_pdrop
							lowerCAmelCase						:    Any     		=   layer_norm_epsilon
							lowerCAmelCase						:    Tuple     		=   initializer_range
							lowerCAmelCase						:    Tuple     		=   summary_type
							lowerCAmelCase						:    List[str]     		=   summary_use_proj
							lowerCAmelCase						:    Any     		=   summary_activation
							lowerCAmelCase						:    str     		=   summary_first_dropout
							lowerCAmelCase						:    Optional[int]     		=   summary_proj_to_labels
							lowerCAmelCase						:    int     		=   scale_attn_weights
							lowerCAmelCase						:    Dict     		=   use_cache
							lowerCAmelCase						:    Optional[int]     		=   scale_attn_by_inverse_layer_idx
							lowerCAmelCase						:    Optional[int]     		=   reorder_and_upcast_attn

							lowerCAmelCase						:    Optional[int]     		=   bos_token_id
							lowerCAmelCase						:    List[str]     		=   eos_token_id

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



class 						__magic_name__      (  __SCREAMING_SNAKE_CASE			):





		def __init__(							self			:		int  ,     lowerCamelCase__			:		Dict  ,     lowerCamelCase__			:		Optional[int] = "default"  ,     lowerCamelCase__			:		Optional[Any] = None  ,     lowerCamelCase__			:		Union[str, Any] = False  ,     ):
							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?
												lowerCAmelCase						:    Optional[int]     		=   0





		@property
		def 					_A							(							self			:		int ):
							lowerCAmelCase						:    Any     		=   OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
							if self.use_past:
												self.fill_with_past_key_values_(_snake_case  ,     direction='''inputs''' )
												lowerCAmelCase						:    str     		=   {0: "batch", 1: "past_sequence + sequence"}
							else:
												lowerCAmelCase						:    List[str]     		=   {0: "batch", 1: "sequence"}

							return common_inputs





		@property
		def 					_A							(							self			:		Union[str, Any] ):
							return self._config.n_layer





		@property
		def 					_A							(							self			:		Optional[Any] ):
							return self._config.n_head





		def 					_A							(							self			:		Any  ,     lowerCamelCase__			:		int  ,     lowerCamelCase__			:		Union[str, Any] = -1  ,     lowerCamelCase__			:		Tuple = -1  ,     lowerCamelCase__			:		Optional[Any] = False  ,     lowerCamelCase__			:		Tuple = None  ,     ):
							lowerCAmelCase						:    str     		=   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()
							lowerCAmelCase						:    Optional[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

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

							lowerCAmelCase						:    Union[str, Any]     		=   common_inputs["attention_mask"]
							if self.use_past:
												lowerCAmelCase						:    Union[str, Any]     		=   ordered_inputs["attention_mask"].dtype
												lowerCAmelCase						:    Tuple     		=   torch.cat(
												    [ordered_inputs['''attention_mask'''], torch.ones(_snake_case  ,     _snake_case  ,     dtype=_snake_case )]  ,     dim=1 )

							return ordered_inputs





		@property
		def 					_A							(							self			:		Optional[Any] ):
							return 1_3



'''simple docstring'''
from __future__ import annotations


def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	Dict      =   {1}
	UpperCAmelCase_			:	int      =   [1]
	for _ in range(2     ,							depth + 1							):
		UpperCAmelCase_			:	Dict      =   [*previous_coefficients, 0]
		UpperCAmelCase_			:	Tuple      =   [0, *previous_coefficients]
		UpperCAmelCase_			:	Dict      =   []
		for x, y in zip(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							):
			coefficients.add(x + y							)
			previous_coefficients.append(x + y							)
	return coefficients


def       a__    (  _SCREAMING_SNAKE_CASE					:    set[int]							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   set()
	for number in unique_coefficients:
		UpperCAmelCase_			:	Tuple      =   2
		UpperCAmelCase_			:	Tuple      =   number
		while divisor**2 <= copy_number:
			UpperCAmelCase_			:	List[Any]      =   0
			while copy_number % divisor == 0:
				copy_number //= divisor
				multiplicity += 1
			if multiplicity >= 2:
				non_squarefrees.add(_SCREAMING_SNAKE_CASE							)
				break
			divisor += 1

	return unique_coefficients.difference(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    int = 51							)			-> int:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   get_pascal_triangle_unique_coefficients(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Union[str, Any]      =   get_squarefrees(_SCREAMING_SNAKE_CASE							)
	return sum(_SCREAMING_SNAKE_CASE							)


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




