





"""simple docstring"""






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 				__lowercase			(			snake_case__   ,      snake_case__   ,      snake_case__							):







					'''simple docstring'''




					@register_to_config
					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = False	,			):
									super().__init__()

									__a :						Dict			       =  nn.Embedding(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE     )

									__a :						List[Any]			       =  nn.Embedding(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE     )
									__a :						int			       =  False

									__a :						Union[str, Any]			       =  nn.Dropout(p=_SCREAMING_SNAKE_CASE     )

									__a :						Any			       =  TaConfig(
									    vocab_size=_SCREAMING_SNAKE_CASE	,			d_model=_SCREAMING_SNAKE_CASE	,			num_heads=_SCREAMING_SNAKE_CASE	,			d_kv=_SCREAMING_SNAKE_CASE	,			d_ff=_SCREAMING_SNAKE_CASE	,			dropout_rate=_SCREAMING_SNAKE_CASE	,			feed_forward_proj=_SCREAMING_SNAKE_CASE	,			is_decoder=_SCREAMING_SNAKE_CASE	,			is_encoder_decoder=_SCREAMING_SNAKE_CASE	,			)

									__a :						List[Any]			       =  nn.ModuleList()
									for lyr_num in range(_SCREAMING_SNAKE_CASE     ):
													__a :						str			       =  TaBlock(_SCREAMING_SNAKE_CASE     )
													self.encoders.append(_SCREAMING_SNAKE_CASE     )

									__a :						List[str]			       =  TaLayerNorm(_SCREAMING_SNAKE_CASE     )
									__a :						List[str]			       =  nn.Dropout(p=_SCREAMING_SNAKE_CASE     )







					def  _lowerCamelCase     (						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						str			       =  self.token_embedder(_SCREAMING_SNAKE_CASE     )

									__a :						Optional[int]			       =  encoder_input_tokens.shape[1]
									__a :						Tuple			       =  torch.arange(_SCREAMING_SNAKE_CASE	,			device=encoder_input_tokens.device     )
									x += self.position_encoding(_SCREAMING_SNAKE_CASE     )

									__a :						List[str]			       =  self.dropout_pre(_SCREAMING_SNAKE_CASE     )

									# inverted the attention mask
									__a :						List[str]			       =  encoder_input_tokens.size()
									__a :						Optional[Any]			       =  self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE     )

									for lyr in self.encoders:
													__a :						int			       =  lyr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE     )[0]
									__a :						str			       =  self.layer_norm(_SCREAMING_SNAKE_CASE     )

									return self.dropout_post(_SCREAMING_SNAKE_CASE     ), encoder_inputs_mask






"""simple docstring"""






from __future__ import annotations

import typing
from collections import Counter

def     __A      (			a_						:int)		->		typing.Counter[int]:
				__a :						typing.Counter[int]			       =  Counter()
				for base in range(1   ,       max_perimeter + 1):
								for perpendicular in range(a_   ,       max_perimeter + 1):
												__a :						Any			       =  (base * base + perpendicular * perpendicular) ** 0.5
												if hypotenuse == int(a_):
																__a :						List[Any]			       =  int(base + perpendicular + hypotenuse)
																if perimeter > max_perimeter:
																				continue
																triplets[perimeter] += 1
				return triplets

def     __A      (			a_						:int = 10_00)		->		int:
				__a :						Dict			       =  pythagorean_triple(a_)
				return triplets.most_common(1)[0][0]


if __name__ == "__main__":
		print(F'Perimeter {solution()} has maximum solutions')