



'''simple docstring'''


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	:       Optional[Any]      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Optional[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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """gptj"""
    _SCREAMING_SNAKE_CASE	  =  {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }






    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					Dict=5_0_4_0_0  ,						UpperCamelCase__      :					int=2_0_4_8  ,						UpperCamelCase__      :					Tuple=4_0_9_6  ,						UpperCamelCase__      :					Tuple=2_8  ,						UpperCamelCase__      :					Union[str, Any]=1_6  ,						UpperCamelCase__      :					Optional[Any]=6_4  ,						UpperCamelCase__      :					Any=None  ,						UpperCamelCase__      :					int="gelu_new"  ,						UpperCamelCase__      :					Optional[Any]=0.0  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					List[str]=0.0  ,						UpperCamelCase__      :					Any=1E-5  ,						UpperCamelCase__      :					Tuple=0.0_2  ,						UpperCamelCase__      :					Any=True  ,						UpperCamelCase__      :					List[str]=5_0_2_5_6  ,						UpperCamelCase__      :					Dict=5_0_2_5_6  ,						UpperCamelCase__      :					Dict=False  ,						**UpperCamelCase__      :					Union[str, Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  vocab_size
       UpperCamelCase					=  n_positions
       UpperCamelCase					=  n_embd
       UpperCamelCase					=  n_layer
       UpperCamelCase					=  n_head
       UpperCamelCase					=  n_inner
       UpperCamelCase					=  rotary_dim
       UpperCamelCase					=  activation_function
       UpperCamelCase					=  resid_pdrop
       UpperCamelCase					=  embd_pdrop
       UpperCamelCase					=  attn_pdrop
       UpperCamelCase					=  layer_norm_epsilon
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  use_cache

       UpperCamelCase					=  bos_token_id
       UpperCamelCase					=  eos_token_id

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

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Optional[Any]  ,						UpperCamelCase__      :					PretrainedConfig  ,						UpperCamelCase__      :					str = "default"  ,						UpperCamelCase__      :					List[PatchingSpec] = None  ,						UpperCamelCase__      :					bool = False  ,						):


       """simple docstring"""







       super().__init__(UpperCamelCase__  ,						task=UpperCamelCase__  ,						patching_specs=UpperCamelCase__  ,						use_past=UpperCamelCase__ )
       if not getattr(self._config  ,						'pad_token_id'  ,						UpperCamelCase__ ):
          # TODO: how to do that better?
          UpperCamelCase					=  0






    @property
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
       if self.use_past:
          self.fill_with_past_key_values_(UpperCamelCase__  ,						direction='inputs' )
          UpperCamelCase					=  {0: 'batch', 1: 'past_sequence + sequence'}
       else:
          UpperCamelCase					=  {0: 'batch', 1: 'sequence'}

       return common_inputs






    @property
    def  A       (  self      :					int ):


       """simple docstring"""







       return self._config.n_layer






    @property
    def  A       (  self      :					str ):


       """simple docstring"""







       return self._config.n_head






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					PreTrainedTokenizer  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					int = -1  ,						UpperCamelCase__      :					bool = False  ,						UpperCamelCase__      :					Optional[TensorType] = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  super(UpperCamelCase__  ,						self ).generate_dummy_inputs(
           UpperCamelCase__  ,						batch_size=UpperCamelCase__  ,						seq_length=UpperCamelCase__  ,						is_pair=UpperCamelCase__  ,						framework=UpperCamelCase__ )

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

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

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

       return ordered_inputs






    @property
    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       return 1_3






'''simple docstring'''


def 				__lowerCamelCase				(   A__    )       ->	list:






   """simple docstring"""
   UpperCamelCase					=  len(A__    )
   for i in range(1    ,						A__    ):
      UpperCamelCase					=  collection[i]
      UpperCamelCase					=  0
      UpperCamelCase					=  i - 1

      while low <= high:
         UpperCamelCase					=  (low + high) // 2
         if val < collection[mid]:
            UpperCamelCase					=  mid - 1
         else:
            UpperCamelCase					=  mid + 1
      for j in range(A__    ,						A__    ,						-1    ):
         UpperCamelCase					=  collection[j - 1]
      UpperCamelCase					=  val
   return collection


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

