



'''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 				SCREAMING_SNAKE_CASE(							A__							,		A__							,		A__			):







	"""simple docstring"""






	@register_to_config
	def __init__(						self			:     str		,			__snake_case			:     int		,			__snake_case			:     int		,			__snake_case			:     int		,			__snake_case			:     float		,			__snake_case			:     int		,			__snake_case			:     int		,			__snake_case			:     int		,			__snake_case			:     int		,			__snake_case			:     str		,			__snake_case			:     bool = False		,			)			->		int:
								super().__init__()

								UpperCAmelCase :       Any 							=		nn.Embedding(__snake_case		,			__snake_case		)

								UpperCAmelCase :       Tuple 							=		nn.Embedding(__snake_case		,			__snake_case		)
								UpperCAmelCase :       List[Any] 							=		False

								UpperCAmelCase :       Dict 							=		nn.Dropout(p=__snake_case		)

								UpperCAmelCase :       str 							=		TaConfig(
								    vocab_size=__snake_case		,			d_model=__snake_case		,			num_heads=__snake_case		,			d_kv=__snake_case		,			d_ff=__snake_case		,			dropout_rate=__snake_case		,			feed_forward_proj=__snake_case		,			is_decoder=__snake_case		,			is_encoder_decoder=__snake_case		,			)

								UpperCAmelCase :       List[str] 							=		nn.ModuleList()
								for lyr_num in range(__snake_case		):
															UpperCAmelCase :       int 							=		TaBlock(__snake_case		)
															self.encoders.append(__snake_case		)

								UpperCAmelCase :       Dict 							=		TaLayerNorm(__snake_case		)
								UpperCAmelCase :       Dict 							=		nn.Dropout(p=__snake_case		)







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     str		)			->		Dict:
								UpperCAmelCase :       Dict 							=		self.token_embedder(__snake_case		)

								UpperCAmelCase :       Tuple 							=		encoder_input_tokens.shape[1]
								UpperCAmelCase :       Union[str, Any] 							=		torch.arange(__snake_case		,			device=encoder_input_tokens.device		)
								x += self.position_encoding(__snake_case		)

								UpperCAmelCase :       int 							=		self.dropout_pre(__snake_case		)

								# inverted the attention mask
								UpperCAmelCase :       List[str] 							=		encoder_input_tokens.size()
								UpperCAmelCase :       str 							=		self.get_extended_attention_mask(__snake_case		,			__snake_case		)

								for lyr in self.encoders:
															UpperCAmelCase :       Dict 							=		lyr(__snake_case		,			__snake_case		)[0]
								UpperCAmelCase :       Optional[int] 							=		self.layer_norm(__snake_case		)

								return self.dropout_post(__snake_case		), encoder_inputs_mask










'''simple docstring'''






from __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						list[int | float]   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int  )      ->   int | float:
							if len(_lowerCAmelCase  ) == 0:
														raise ValueError('''find_max() arg is an empty sequence'''  )
							if (
							    left >= len(_lowerCAmelCase  )
							    or left < -len(_lowerCAmelCase  )
							    or right >= len(_lowerCAmelCase  )
							    or right < -len(_lowerCAmelCase  )
							):
														raise IndexError('''list index out of range'''  )
							if left == right:
														return nums[left]
							UpperCAmelCase :       List[Any] 							=		(left + right) >> 1  # the middle
							UpperCAmelCase :       Optional[Any] 							=		find_max(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )  # find max in range[left, mid]
							UpperCAmelCase :       Dict 							=		find_max(_lowerCAmelCase   ,      mid + 1   ,      _lowerCAmelCase  )  # find max in range[mid + 1, right]

							return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





