

def       __snake_case						( __UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      if collection == []:
            return []

      # get some information about the collection
      A_						  =				len(__UpperCamelCase    )
      A_						  =				max(__UpperCamelCase    )
      A_						  =				min(__UpperCamelCase    )

      # create the counting array
      A_						  =				coll_max + 1 - coll_min
      A_						  =				[0] * counting_arr_length

      # count how much a number appears in the collection
      for number in collection:
            counting_arr[number - coll_min] += 1

      # sum each position with it's predecessors. now, counting_arr[i] tells
      # us how many elements <= i has in the collection
      for i in range(1				,__UpperCamelCase    ):
            A_						  =				counting_arr[i] + counting_arr[i - 1]

      # create the output collection
      A_						  =				[0] * coll_len

      # place the elements in the output, respecting the original order (stable
      # sort) from end to begin, updating counting_arr
      for i in reversed(range(0				,__UpperCamelCase    )    ):
            A_						  =				collection[i]
            counting_arr[collection[i] - coll_min] -= 1

      return ordered

def       __snake_case						( __UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

      return "".join([chr(__UpperCamelCase    ) for i in counting_sort([ord(__UpperCamelCase    ) for c in string]    )]    )


if __name__ == "__main__":
      # Test string sort
      assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

      __a    :Tuple      =			input('Enter numbers separated by a comma:\n').strip()
      __a    :Dict      =			[int(item) for item in user_input.split(',')]
      print(counting_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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




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

		A_		:    Tuple						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)

		A_		:    List[str]						     = nn.Embedding(lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_		:    Any						     = False

		A_		:    Tuple						     = nn.Dropout(p=lowerCAmelCase_				)

		A_		:    List[str]						     = TaConfig(
		    vocab_size=lowerCAmelCase_  ,  d_model=lowerCAmelCase_  ,  num_heads=lowerCAmelCase_  ,  d_kv=lowerCAmelCase_  ,  d_ff=lowerCAmelCase_  ,  dropout_rate=lowerCAmelCase_  ,  feed_forward_proj=lowerCAmelCase_  ,  is_decoder=lowerCAmelCase_  ,  is_encoder_decoder=lowerCAmelCase_  ,  )

		A_		:    Optional[Any]						     = nn.ModuleList()
		for lyr_num in range(lowerCAmelCase_				):
			A_		:    Tuple						     = TaBlock(lowerCAmelCase_				)
			self.encoders.append(lowerCAmelCase_				)

		A_		:    Any						     = TaLayerNorm(lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = nn.Dropout(p=lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.token_embedder(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = encoder_input_tokens.shape[1]
		A_		:    Any						     = torch.arange(lowerCAmelCase_  ,  device=encoder_input_tokens.device				)
		x += self.position_encoding(lowerCAmelCase_				)

		A_		:    Optional[Any]						     = self.dropout_pre(lowerCAmelCase_				)

		# inverted the attention mask
		A_		:    int						     = encoder_input_tokens.size()
		A_		:    Optional[Any]						     = self.get_extended_attention_mask(lowerCAmelCase_  ,  lowerCAmelCase_				)

		for lyr in self.encoders:
			A_		:    int						     = lyr(lowerCAmelCase_  ,  lowerCAmelCase_				)[0]
		A_		:    List[str]						     = self.layer_norm(lowerCAmelCase_				)

		return self.dropout_post(lowerCAmelCase_				), encoder_inputs_mask

