






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowerCamelCase	:		Tuple										=   logging.get_logger(__name__)

__lowerCamelCase	:		Any										=   {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							'gptj'
      _a							:						Optional[int]										=							{
          'max_position_embeddings': 'n_positions',
          'hidden_size': 'n_embd',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }
      def __init__(     self				,			a=5_0_4_0_0				,			a=2_0_4_8				,			a=4_0_9_6				,			a=2_8				,			a=1_6				,			a=6_4				,			a=None				,			a="gelu_new"				,			a=0.0				,			a=0.0				,			a=0.0				,			a=1e-5				,			a=0.02				,			a=True				,			a=5_0_2_5_6				,			a=5_0_2_5_6				,			a=False				,			**a				,			):
       snake_case__  :    Union[str, Any]							       =vocab_size
       snake_case__  :    Union[str, Any]							       =n_positions
       snake_case__  :    Tuple							       =n_embd
       snake_case__  :    List[str]							       =n_layer
       snake_case__  :    str							       =n_head
       snake_case__  :    Any							       =n_inner
       snake_case__  :    Optional[int]							       =rotary_dim
       snake_case__  :    List[Any]							       =activation_function
       snake_case__  :    List[Any]							       =resid_pdrop
       snake_case__  :    int							       =embd_pdrop
       snake_case__  :    Optional[Any]							       =attn_pdrop
       snake_case__  :    Any							       =layer_norm_epsilon
       snake_case__  :    Optional[int]							       =initializer_range
       snake_case__  :    Optional[Any]							       =use_cache

       snake_case__  :    Tuple							       =bos_token_id
       snake_case__  :    Optional[Any]							       =eos_token_id

       super().__init__(
           bos_token_id=a				,			eos_token_id=a				,			tie_word_embeddings=a				,			**a   )







class  _lowercase   (   _A						):
      def __init__(     self				,			a				,			a = "default"				,			a = None				,			a = False				,			):
       super().__init__(a				,			task=a				,			patching_specs=a				,			use_past=a   )
       if not getattr(self._config				,			"""pad_token_id"""				,			a   ):
        # TODO: how to do that better?
        snake_case__  :    str							       =0
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}   )
       if self.use_past:
        self.fill_with_past_key_values_(a				,			direction="""inputs"""   )
        snake_case__  :    Dict							       ={0: """batch""", 1: """past_sequence + sequence"""}
       else:
        snake_case__  :    List[str]							       ={0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def 						lowercase__					(     self   ):
       return self._config.n_layer
      @property
      def 						lowercase__					(     self   ):
       return self._config.n_head
      def 						lowercase__					(     self				,			a				,			a = -1				,			a = -1				,			a = False				,			a = None				,			):
       snake_case__  :    Dict							       =super(a				,			self   ).generate_dummy_inputs(
           a				,			batch_size=a				,			seq_length=a				,			is_pair=a				,			framework=a   )

       # We need to order the input in the way they appears in the forward()
       snake_case__  :    int							       =OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}   )

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
        else:
         import torch

         snake_case__  :    Tuple							       =common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         snake_case__  :    Any							       =seqlen + 2
         snake_case__  :    Union[str, Any]							       =(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         snake_case__  :    Any							       =[
             (torch.zeros(a   ), torch.zeros(a   )) for _ in range(self.num_layers   )
         ]

       snake_case__  :    Dict							       =common_inputs["""attention_mask"""]
       if self.use_past:
        snake_case__  :    Tuple							       =ordered_inputs["""attention_mask"""].dtype
        snake_case__  :    List[Any]							       =torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(a				,			a				,			dtype=a   )]				,			dim=1   )

       return ordered_inputs



      @property
      def 						lowercase__					(     self   ):
       return 1_3













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





