



'''simple docstring'''


import re
def 							UpperCAmelCase_  (						__lowerCamelCase : str			):
   return [char.split() for char in re.split(r"[^ a-z A-Z 0-9 \s]"     ,str_			)]
def 							UpperCAmelCase_  (						__lowerCamelCase : str			):
   lowercase_      :Dict         =			split_input(str_			)
   return "".join(
       ["".join([char.capitalize() for char in sub_str]			) for sub_str in string_split]			)
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : bool     ,__lowerCamelCase : str			):


   try:
      lowercase_      :Optional[Any]         =			split_input(lowerCAmelCase__			)
      if upper:
         lowercase_      :str         =			"".join(
             [
                 separator.join([char.upper() for char in sub_str]			)
                 for sub_str in string_split
             ]			)
      else:
         lowercase_      :int         =			"".join(
             [
                 separator.join([char.lower() for char in sub_str]			)
                 for sub_str in string_split
             ]			)
      return res_str
   except IndexError:
      return "not valid string"
def 							UpperCAmelCase_  (						__lowerCamelCase : str			):
   return to_simple_case(lowerCAmelCase__			)
def 							UpperCAmelCase_  (						__lowerCamelCase : str			):


   try:
      lowercase_      :List[Any]         =			to_simple_case(lowerCAmelCase__			)
      return res_str[0].lower() + res_str[1:]
   except IndexError:
      return "not valid string"
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : bool			):
   return to_complex_case(lowerCAmelCase__     ,lowerCAmelCase__     ,"_"			)
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : bool			):
   return to_complex_case(lowerCAmelCase__     ,lowerCAmelCase__     ,"-"			)


if __name__ == "__main__":
 __import__('''doctest''').testmod()









'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase :					Union[str, Any] 							=logging.get_logger(__name__)

lowerCAmelCase :					Optional[Any] 							={
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "gpt_neo"
     __A								=      ["past_key_values"]
     __A								=      {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





     def __init__(			self		: Union[str, Any]     ,				lowercase		: Tuple=50_257     ,				lowercase		: Optional[Any]=2_048     ,				lowercase		: Union[str, Any]=2_048     ,				lowercase		: int=24     ,				lowercase		: Optional[Any]=[[["global", "local"], 12]]     ,				lowercase		: List[Any]=16     ,				lowercase		: List[str]=None     ,				lowercase		: Union[str, Any]=256     ,				lowercase		: Optional[Any]="gelu_new"     ,				lowercase		: Any=0.0     ,				lowercase		: List[Any]=0.0     ,				lowercase		: Any=0.0     ,				lowercase		: str=0.1     ,				lowercase		: Dict=1e-5     ,				lowercase		: List[str]=0.02     ,				lowercase		: Union[str, Any]=True     ,				lowercase		: int=50_256     ,				lowercase		: Union[str, Any]=50_256     ,				**lowercase		: Dict     ,				):





        """simple docstring"""




        lowercase_      :str         =			vocab_size
        lowercase_      :Tuple         =			max_position_embeddings
        lowercase_      :Tuple         =			hidden_size
        lowercase_      :List[str]         =			num_layers
        lowercase_      :int         =			num_heads
        lowercase_      :Union[str, Any]         =			intermediate_size
        lowercase_      :Tuple         =			window_size
        lowercase_      :Any         =			activation_function
        lowercase_      :Tuple         =			resid_dropout
        lowercase_      :Any         =			embed_dropout
        lowercase_      :str         =			attention_dropout
        lowercase_      :List[str]         =			classifier_dropout
        lowercase_      :List[Any]         =			layer_norm_epsilon
        lowercase_      :List[str]         =			initializer_range
        lowercase_      :int         =			use_cache

        lowercase_      :Tuple         =			bos_token_id
        lowercase_      :Optional[Any]         =			eos_token_id

        lowercase_      :int         =			attention_types
        lowercase_      :Tuple         =			self.expand_attention_types_params(lowercase						)

        if len(self.attention_layers						) != self.num_layers:
           raise ValueError(
               "Configuration for convolutional module is incorrect. "
               "It is required that `len(config.attention_layers)` == `config.num_layers` "
               F'but is `len(config.attention_layers) = {len(self.attention_layers						)}`, '
               F'`config.num_layers = {self.num_layers}`. '
               "`config.attention_layers` is prepared using `config.attention_types`. "
               "Please verify the value of `config.attention_types` argument."						)

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







     @staticmethod
     def 							lowercase__				(			lowercase		: str						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			[]
        for item in attention_types:
           for _ in range(item[1]						):
              attentions.extend(item[0]						)
        return attentions







def 							UpperCAmelCase_  (						__lowerCamelCase : Union[str, Any]     ,__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : Dict			):
   import torch

   lowercase_      :List[str]         =			input.size()
   lowercase_      :Union[str, Any]         =			len(__lowerCamelCase			)
   lowercase_      :Any         =			shape[dimension]

   lowercase_      :str         =			torch.arange(0     ,__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			torch.div(sizedim - size     ,__lowerCamelCase     ,rounding_mode="floor"			) + 1
   lowercase_      :int         =			torch.arange(__lowerCamelCase			) + low_indices[:min_length][:, None]

   lowercase_      :List[Any]         =			[slice(__lowerCamelCase			)] * rank
   lowercase_      :int         =			indices
   lowercase_      :Dict         =			input[s]

   lowercase_      :List[str]         =			list(range(0     ,rank + 1			)			)
   perm.append(perm.pop(dimension + 1			)			)

   return sliced.permute(__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Any			):
   import torch

   lowercase_      :List[Any]         =			torch.arange(1     ,__lowerCamelCase			)
   lowercase_      :int         =			torch.remainder(__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :Optional[int]         =			remainders == 0
   lowercase_      :int         =			candidates[divisor_indices]
   lowercase_      :Tuple         =			torch.max(__lowerCamelCase			)
   return largest_divisor, torch.div(__lowerCamelCase     ,__lowerCamelCase     ,rounding_mode="floor"			)


class 				a_       (    _lowerCAmelCase							):





     @property
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :int         =			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
        if self.use_past:
           self.fill_with_past_key_values_(lowercase     ,				direction="inputs"						)
           lowercase_      :Union[str, Any]         =			{0: "batch", 1: "past_sequence + sequence"}
        else:
           lowercase_      :str         =			{0: "batch", 1: "sequence"}

        return common_inputs





     @property
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        return self._config.num_heads





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





        """simple docstring"""




        lowercase_      :List[str]         =			super(lowercase     ,				self						).generate_dummy_inputs(
            lowercase     ,				batch_size=lowercase     ,				seq_length=lowercase     ,				is_pair=lowercase     ,				framework=lowercase						)

        # We need to order the input in the way they appears in the forward()
        lowercase_      :Tuple         =			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

              lowercase_      ,							lowercase_      :Tuple         =			common_inputs["input_ids"].shape
              # Not using the same length for past_key_values
              lowercase_      :Any         =			seqlen + 2
              lowercase_      :List[Any]         =			(
                  batch,
                  self.num_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // self.num_attention_heads,
              )
              lowercase_      :Dict         =			[
                  (torch.zeros(lowercase						), torch.zeros(lowercase						)) for _ in range(self.num_layers						)
              ]

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

        return ordered_inputs







     @property
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        return 13


