
'''simple docstring'''





def snake_case_    (				lowercase__     :   int		):

  for insert_index, insert_value in enumerate(collection[1:]		):
    UpperCAmelCase__			:				str												=       insert_index
    while insert_index >= 0 and insert_value < collection[insert_index]:
      UpperCAmelCase__			:				Dict												=       collection[insert_index]
      insert_index -= 1
    if insert_index != temp_index:
      UpperCAmelCase__			:				Optional[Any]												=       insert_value
  return collection


if __name__ == "__main__":
      from doctest import testmod

      testmod()

      SCREAMING_SNAKE_CASE    =					input("""Enter numbers separated by a comma:\n""").strip()
      SCREAMING_SNAKE_CASE    =					[int(item) for item in user_input.split(""",""")]
      print(F'{insertion_sort(unsorted) = }')





'''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    =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =					{
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}






class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						Optional[int]				=       "gpt2"
 lowercase_				:						int				=       ["past_key_values"]
 lowercase_				:						Any				=       {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }







 def __init__(				self		:     Union[str, Any] ,							snake_case__		:     Optional[int]=5_02_57 ,							snake_case__		:     List[Any]=10_24 ,							snake_case__		:     Tuple=7_68 ,							snake_case__		:     List[Any]=12 ,							snake_case__		:     List[str]=12 ,							snake_case__		:     Union[str, Any]=None ,							snake_case__		:     str="gelu_new" ,							snake_case__		:     Any=0.1 ,							snake_case__		:     Union[str, Any]=0.1 ,							snake_case__		:     Tuple=0.1 ,							snake_case__		:     List[str]=1e-5 ,							snake_case__		:     Optional[int]=0.02 ,							snake_case__		:     int="cls_index" ,							snake_case__		:     Tuple=True ,							snake_case__		:     List[Any]=None ,							snake_case__		:     Optional[int]=True ,							snake_case__		:     Optional[Any]=0.1 ,							snake_case__		:     Tuple=True ,							snake_case__		:     int=True ,							snake_case__		:     Optional[int]=5_02_56 ,							snake_case__		:     List[Any]=5_02_56 ,							snake_case__		:     Union[str, Any]=False ,							snake_case__		:     Optional[int]=False ,							**snake_case__		:     List[Any] ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				List[str]												=       vocab_size
   UpperCAmelCase__			:				Any												=       n_positions
   UpperCAmelCase__			:				str												=       n_embd
   UpperCAmelCase__			:				List[Any]												=       n_layer
   UpperCAmelCase__			:				Any												=       n_head
   UpperCAmelCase__			:				Any												=       n_inner
   UpperCAmelCase__			:				Tuple												=       activation_function
   UpperCAmelCase__			:				List[str]												=       resid_pdrop
   UpperCAmelCase__			:				Union[str, Any]												=       embd_pdrop
   UpperCAmelCase__			:				int												=       attn_pdrop
   UpperCAmelCase__			:				Optional[Any]												=       layer_norm_epsilon
   UpperCAmelCase__			:				Optional[Any]												=       initializer_range
   UpperCAmelCase__			:				Optional[Any]												=       summary_type
   UpperCAmelCase__			:				Tuple												=       summary_use_proj
   UpperCAmelCase__			:				int												=       summary_activation
   UpperCAmelCase__			:				List[str]												=       summary_first_dropout
   UpperCAmelCase__			:				Optional[Any]												=       summary_proj_to_labels
   UpperCAmelCase__			:				Optional[Any]												=       scale_attn_weights
   UpperCAmelCase__			:				List[Any]												=       use_cache
   UpperCAmelCase__			:				Union[str, Any]												=       scale_attn_by_inverse_layer_idx
   UpperCAmelCase__			:				Tuple												=       reorder_and_upcast_attn

   UpperCAmelCase__			:				Dict												=       bos_token_id
   UpperCAmelCase__			:				Union[str, Any]												=       eos_token_id

   super().__init__(bos_token_id=snake_case__ ,							eos_token_id=snake_case__ ,							**snake_case__   )






class        UpperCAmelCase_       (						A							):

 '''simple docstring'''







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



   '''simple docstring'''




   super().__init__(snake_case__ ,							task=snake_case__ ,							patching_specs=snake_case__ ,							use_past=snake_case__   )
   if not getattr(self._config ,							"pad_token_id" ,							snake_case__   ):
     # TODO: how to do that better?
     UpperCAmelCase__			:				List[str]												=       0







 @property
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}   )
   if self.use_past:
     self.fill_with_past_key_values_(snake_case__ ,							direction="inputs"   )
     UpperCAmelCase__			:				Dict												=       {0: "batch", 1: "past_sequence + sequence"}
   else:
     UpperCAmelCase__			:				Dict												=       {0: "batch", 1: "sequence"}

   return common_inputs







 @property
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   return self._config.n_layer







 @property
 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   return self._config.n_head







 def        UpperCamelCase     (				self		:     int ,							snake_case__		:     PreTrainedTokenizer ,							snake_case__		:     int = -1 ,							snake_case__		:     int = -1 ,							snake_case__		:     bool = False ,							snake_case__		:     Optional[TensorType] = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       super(snake_case__ ,							self   ).generate_dummy_inputs(
       snake_case__ ,							batch_size=snake_case__ ,							seq_length=snake_case__ ,							is_pair=snake_case__ ,							framework=snake_case__   )

   # We need to order the input in the way they appears in the forward()
   UpperCAmelCase__			:				Dict												=       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__			:				int												=       common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       UpperCAmelCase__			:				int												=       seqlen + 2
       UpperCAmelCase__			:				Union[str, Any]												=       (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       UpperCAmelCase__			:				Optional[Any]												=       [
           (torch.zeros(snake_case__   ), torch.zeros(snake_case__   )) for _ in range(self.num_layers   )
       ]

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

   return ordered_inputs







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



   '''simple docstring'''




   return 13



