"""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


SCREAMING_SNAKE_CASE__:List[Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[str]			=	{
    """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 				snake_case__     (   snake_case_   ):
 _snake_case   :			int           =		"""gptj"""
 _snake_case   :			Any           =		{
     """max_position_embeddings""": """n_positions""",
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }


 def __init__(				self  ,						lowerCamelCase=50400  ,						lowerCamelCase=2048  ,						lowerCamelCase=4096  ,						lowerCamelCase=28  ,						lowerCamelCase=16  ,						lowerCamelCase=64  ,						lowerCamelCase=None  ,						lowerCamelCase="gelu_new"  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.0  ,						lowerCamelCase=1E-5  ,						lowerCamelCase=0.02  ,						lowerCamelCase=True  ,						lowerCamelCase=50256  ,						lowerCamelCase=50256  ,						lowerCamelCase=False  ,						**lowerCamelCase  ,						):
        __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 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase = "default"  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						):
        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 		a__ (				self		):
        __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 		a__ (				self		):
        return self._config.n_layer


 @property
 def 		a__ (				self		):
        return self._config.n_head


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = -1  ,						lowerCamelCase = -1  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						):
        __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				, __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 		a__ (				self		):
        return 13

"""simple docstring"""



def 					_lowerCamelCase(					a							):
       __a     				=  len(a							)
       for i in range(1 ,  a							):
              __a     				=  collection[i]
              __a     				=  0
              __a     				=  i - 1

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


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