





'''simple docstring'''






def      __A						(							a_						:  Tuple	):
		lowerCAmelCase     :    Optional[Any]						=   len(a_	)
		while cur > 1:
				# Find the maximum number in arr
				lowerCAmelCase     :    int						=   arr.index(max(arr[0:cur]	)	)
				# Reverse from 0 to mi
				lowerCAmelCase     :    Tuple						=   arr[mi::-1] + arr[mi + 1 : len(a_	)]
				# Reverse whole list
				lowerCAmelCase     :    Union[str, Any]						=   arr[cur - 1 :: -1] + arr[cur : len(a_	)]
				cur -= 1
		return arr


if __name__ == "__main__":
					lowerCAmelCase       		=      input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase       		=      [int(item) for item in user_input.split(""",""")]
					print(pancake_sort(unsorted))










'''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 		lowerCamelCase  (      _A      ,   _A      ,   _A       ):



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

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)
			lowerCAmelCase     :    int						=   False

			lowerCAmelCase     :    int						=   nn.Dropout(p=a_	)

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

			lowerCAmelCase     :    Any						=   nn.ModuleList()
			for lyr_num in range(a_	):
					lowerCAmelCase     :    Optional[Any]						=   TaBlock(a_	)
					self.encoders.append(a_	)

			lowerCAmelCase     :    List[str]						=   TaLayerNorm(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   nn.Dropout(p=a_	)





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   self.token_embedder(a_	)

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

			lowerCAmelCase     :    List[str]						=   self.dropout_pre(a_	)

			# inverted the attention mask
			lowerCAmelCase     :    List[Any]						=   encoder_input_tokens.size()
			lowerCAmelCase     :    Dict						=   self.get_extended_attention_mask(a_		,      a_	)

			for lyr in self.encoders:
					lowerCAmelCase     :    Dict						=   lyr(a_		,      a_	)[0]
			lowerCAmelCase     :    int						=   self.layer_norm(a_	)

			return self.dropout_post(a_	), encoder_inputs_mask



