






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	:		Optional[Any]										=   {
    """Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
    """Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
    """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
    """Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
    """Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
    """Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
    """Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
    """Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
    """Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
    """Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
    """Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
    """Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}







class  _lowercase   (   _A						):
      _a							:						Dict										=							'codegen'
      _a							:						Tuple										=							{
          '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=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__  :    Tuple							       =n_ctx
       snake_case__  :    Dict							       =n_positions
       snake_case__  :    Tuple							       =n_embd
       snake_case__  :    Tuple							       =n_layer
       snake_case__  :    int							       =n_head
       snake_case__  :    List[Any]							       =n_inner
       snake_case__  :    Any							       =rotary_dim
       snake_case__  :    List[str]							       =activation_function
       snake_case__  :    Any							       =resid_pdrop
       snake_case__  :    Optional[int]							       =embd_pdrop
       snake_case__  :    Optional[Any]							       =attn_pdrop
       snake_case__  :    int							       =layer_norm_epsilon
       snake_case__  :    int							       =initializer_range
       snake_case__  :    List[Any]							       =use_cache

       snake_case__  :    Optional[Any]							       =bos_token_id
       snake_case__  :    int							       =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__  :    Union[str, Any]							       =0
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}   )
       if self.use_past:
        self.fill_with_past_key_values_(a				,			direction="""inputs"""   )
        snake_case__  :    Optional[Any]							       ={0: """batch""", 1: """past_sequence + sequence"""}
       else:
        snake_case__  :    List[Any]							       ={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__  :    Optional[Any]							       =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__  :    int							       =common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         snake_case__  :    Dict							       =seqlen + 2
         snake_case__  :    Dict							       =(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         snake_case__  :    Union[str, Any]							       =[
             (torch.zeros(a   ), torch.zeros(a   )) for _ in range(self.num_layers   )
         ]

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

       return ordered_inputs



      @property
      def 						lowercase__					(     self   ):
       return 1_3













def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) <= 1:
  return [tuple(_a	)]

 snake_case__  :    Optional[int]							       =[]

 def generate(_a  :					int		,  _a  :					list	):
  if k == 1:
   res.append(tuple(arr[:]	)	)
   return

  generate(k - 1		,  _a	)

  for i in range(k - 1	):
   if k % 2 == 0:  # k is even
    snake_case__       ,   snake_case__  :    Dict							       =arr[k - 1], arr[i]
   else:  # k is odd
    snake_case__       ,   snake_case__  :    int							       =arr[k - 1], arr[0]
   generate(k - 1		,  _a	)

 generate(len(_a	)		,  _a	)
 return res


if __name__ == "__main__":
       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma:\n""").strip()
       __lowerCamelCase	:		Any										=   [int(item) for item in user_input.split(""",""")]
       print(heaps(arr))





