



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


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{
    """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 	lowerCamelCase__	(       _a):



 """simple docstring"""

 a__					: Any =  "gptj"
 a__					: Dict =  {
     "max_position_embeddings": "n_positions",
     "hidden_size": "n_embd",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }

 def __init__(    self					:					List[Any]			,						__lowerCAmelCase					:					Dict=5_04_00			,						__lowerCAmelCase					:					List[Any]=20_48			,						__lowerCAmelCase					:					Tuple=40_96			,						__lowerCAmelCase					:					Optional[Any]=28			,						__lowerCAmelCase					:					Union[str, Any]=16			,						__lowerCAmelCase					:					List[str]=64			,						__lowerCAmelCase					:					Any=None			,						__lowerCAmelCase					:					Optional[int]="gelu_new"			,						__lowerCAmelCase					:					int=0.0			,						__lowerCAmelCase					:					str=0.0			,						__lowerCAmelCase					:					Dict=0.0			,						__lowerCAmelCase					:					List[str]=1E-5			,						__lowerCAmelCase					:					int=0.02			,						__lowerCAmelCase					:					Union[str, Any]=True			,						__lowerCAmelCase					:					Optional[Any]=5_02_56			,						__lowerCAmelCase					:					Any=5_02_56			,						__lowerCAmelCase					:					Any=False			,						**__lowerCAmelCase					:					Optional[int]			,						)   ->	Tuple:
    _A      =       vocab_size
    _A      =       n_positions
    _A      =       n_embd
    _A      =       n_layer
    _A      =       n_head
    _A      =       n_inner
    _A      =       rotary_dim
    _A      =       activation_function
    _A      =       resid_pdrop
    _A      =       embd_pdrop
    _A      =       attn_pdrop
    _A      =       layer_norm_epsilon
    _A      =       initializer_range
    _A      =       use_cache

    _A      =       bos_token_id
    _A      =       eos_token_id

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


class 	lowerCamelCase__	(       _a):



 """simple docstring"""

 def __init__(    self					:					str			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Dict = "default"			,						__lowerCAmelCase					:					Any = None			,						__lowerCAmelCase					:					str = False			,						)   ->	Tuple:
    super().__init__(lowerCamelCase__			,						task=lowerCamelCase__			,						patching_specs=lowerCamelCase__			,						use_past=lowerCamelCase__      )
    if not getattr(self._config			,						'''pad_token_id'''			,						lowerCamelCase__      ):
       # TODO: how to do that better?
       _A      =       0

 @property
 def     snake_case_      (    self					:					List[Any]      )   ->	int:
    _A      =       OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}      )
    if self.use_past:
       self.fill_with_past_key_values_(lowerCamelCase__			,						direction='''inputs'''      )
       _A      =       {0: "batch", 1: "past_sequence + sequence"}
    else:
       _A      =       {0: "batch", 1: "sequence"}

    return common_inputs

 @property
 def     snake_case_      (    self					:					List[str]      )   ->	Union[str, Any]:
    return self._config.n_layer

 @property
 def     snake_case_      (    self					:					Any      )   ->	str:
    return self._config.n_head

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					List[Any] = -1			,						__lowerCAmelCase					:					str = -1			,						__lowerCAmelCase					:					Optional[int] = False			,						__lowerCAmelCase					:					int = None			,						)   ->	Tuple:
    _A      =       super(lowerCamelCase__			,						self      ).generate_dummy_inputs(
        lowerCamelCase__			,						batch_size=lowerCamelCase__			,						seq_length=lowerCamelCase__			,						is_pair=lowerCamelCase__			,						framework=lowerCamelCase__      )

    # We need to order the input in the way they appears in the forward()
    _A      =       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

          _A      =       common_inputs["input_ids"].shape
          # Not using the same length for past_key_values
          _A      =       seqlen + 2
          _A      =       (
              batch,
              self.num_attention_heads,
              past_key_values_length,
              self._config.hidden_size // self.num_attention_heads,
          )
          _A      =       [
              (torch.zeros(lowerCamelCase__      ), torch.zeros(lowerCamelCase__      )) for _ in range(self.num_layers      )
          ]

    _A      =       common_inputs["attention_mask"]
    if self.use_past:
       _A      =       ordered_inputs["attention_mask"].dtype
       _A      =       torch.cat(
           [ordered_inputs['''attention_mask'''], torch.ones(lowerCamelCase__			,						lowerCamelCase__			,						dtype=lowerCamelCase__      )]			,						dim=1      )

    return ordered_inputs

 @property
 def     snake_case_      (    self					:					Optional[int]      )   ->	List[str]:
    return 13






def 			_A	(      SCREAMING_SNAKE_CASE      ):
	stooge(SCREAMING_SNAKE_CASE	,0	,len(SCREAMING_SNAKE_CASE      ) - 1      )
	return arr



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		UpperCAmelCase__:	str									= (int)((h - i + 1) / 3      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )

		# Recursively sort last 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,i + t	,(SCREAMING_SNAKE_CASE)      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )


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