
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    _snake_case      (  snake_case       ,		snake_case       ,		snake_case       ):







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

				__magic_name__	:	Tuple			     =  nn.Linear(_a		,     _a		,     bias=_a	)

				__magic_name__	:	List[str]			     =  nn.Embedding(_a		,     _a	)
				__magic_name__	:	Tuple			     =  False

				__magic_name__	:	Optional[Any]			     =  nn.Dropout(p=_a	)

				__magic_name__	:	Optional[int]			     =  TaConfig(
				    d_model=_a		,     num_heads=_a		,     d_kv=_a		,     d_ff=_a		,     feed_forward_proj=_a		,     dropout_rate=_a		,     is_decoder=_a		,     is_encoder_decoder=_a		,     )
				__magic_name__	:	Any			     =  nn.ModuleList()
				for lyr_num in range(_a	):
					__magic_name__	:	Optional[int]			     =  TaBlock(_a	)
					self.encoders.append(_a	)

				__magic_name__	:	Union[str, Any]			     =  TaLayerNorm(_a	)
				__magic_name__	:	Tuple			     =  nn.Dropout(p=_a	)







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
				__magic_name__	:	int			     =  self.input_proj(_a	)

				# terminal relative positional encodings
				__magic_name__	:	str			     =  encoder_inputs.shape[1]
				__magic_name__	:	List[Any]			     =  torch.arange(_a		,     device=encoder_inputs.device	)

				__magic_name__	:	Tuple			     =  encoder_inputs_mask.sum(-1	)
				__magic_name__	:	Tuple			     =  torch.roll(input_positions.unsqueeze(0	)		,     tuple(seq_lens.tolist()	)		,     dims=0	)
				x += self.position_encoding(_a	)

				__magic_name__	:	Optional[int]			     =  self.dropout_pre(_a	)

				# inverted the attention mask
				__magic_name__	:	Any			     =  encoder_inputs.size()
				__magic_name__	:	List[str]			     =  self.get_extended_attention_mask(_a		,     _a	)

				for lyr in self.encoders:
					__magic_name__	:	List[Any]			     =  lyr(_a		,     _a	)[0]
				__magic_name__	:	Optional[int]			     =  self.layer_norm(_a	)

				return self.dropout_post(_a	), encoder_inputs_mask


from __future__ import annotations

snake_case				:	List[str]							   =						"Alexander Joslin"






def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:







	'''simple docstring'''


	if number <= 0:
		__magic_name__	:	int			     =  F'''n must be greater than 0. Got n = {number}'''
		raise ValueError(_snake_case     )

	__magic_name__	:	List[str]			     =  [number + 1] * (number + 1)

	# starting position
	__magic_name__	:	Any			     =  0
	for i in range(1   ,     _snake_case     ):
		__magic_name__	:	Optional[int]			     =  min(table[i + 1]   ,     table[i] + 1     )
		# check if out of bounds
		if i * 2 <= number:
			__magic_name__	:	str			     =  min(table[i * 2]   ,     table[i] + 1     )
		# check if out of bounds
		if i * 3 <= number:
			__magic_name__	:	Any			     =  min(table[i * 3]   ,     table[i] + 1     )
	return table[number]


if __name__ == "__main__":
				import doctest

				doctest.testmod()
