



"""simple docstring"""
def    _snake_case   (		UpperCamelCase		:	str    ,	UpperCamelCase		:	Dict		):
 UpperCAmelCase			:      Union[str, Any]			=			len(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			len(UpperCamelCase		)
 UpperCAmelCase			:      int			=			(
     first_str_length if first_str_length > second_str_length else second_str_length
 )
 UpperCAmelCase			:      Optional[Any]			=			[]
 for char_count in range(UpperCamelCase		):
  if char_count < first_str_length:
   output_list.append(first_str[char_count]		)
  if char_count < second_str_length:
   output_list.append(second_str[char_count]		)
 return "".join(UpperCamelCase		)


if __name__ == "__main__":
      print(alternative_string_arrange("AB", "XYZ"), end=" ")





"""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


A:     Optional[int]										=	logging.get_logger(__name__)

A:     Union[str, 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    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     List[str]		=		'gpt_neo'
   __lowerCAmelCase						:     Any		=		['past_key_values']
   __lowerCAmelCase						:     int		=		{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=50257   ,  _SCREAMING_SNAKE_CASE=2048   ,  _SCREAMING_SNAKE_CASE=2048   ,  _SCREAMING_SNAKE_CASE=24   ,  _SCREAMING_SNAKE_CASE=[[["global", "local"], 12]]   ,  _SCREAMING_SNAKE_CASE=16   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE="gelu_new"   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=1E-5   ,  _SCREAMING_SNAKE_CASE=0.02   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=50256   ,  _SCREAMING_SNAKE_CASE=50256   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   str:





    '''simple docstring'''




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

    UpperCAmelCase			:      Union[str, Any]			=			bos_token_id
    UpperCAmelCase			:      str			=			eos_token_id

    UpperCAmelCase			:      Union[str, Any]			=			attention_types
    UpperCAmelCase			:      int			=			self.expand_attention_types_params(_SCREAMING_SNAKE_CASE		)

    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=_SCREAMING_SNAKE_CASE   ,  eos_token_id=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)




   @staticmethod
   def 			SCREAMING_SNAKE_CASE  (     _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			[]
    for item in attention_types:
     for _ in range(item[1]		):
      attentions.extend(item[0]		)
    return attentions

def    _snake_case		(		UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Optional[Any]    ,	UpperCamelCase		:	Dict		):
 import torch

 UpperCAmelCase			:      Optional[Any]			=			input.size()
 UpperCAmelCase			:      int			=			len(UpperCamelCase		)
 UpperCAmelCase			:      Optional[Any]			=			shape[dimension]

 UpperCAmelCase			:      Union[str, Any]			=			torch.arange(0    ,	UpperCamelCase    ,	UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			torch.div(sizedim - size    ,	UpperCamelCase    ,	rounding_mode="""floor"""		) + 1
 UpperCAmelCase			:      Optional[Any]			=			torch.arange(UpperCamelCase		) + low_indices[:min_length][:, None]

 UpperCAmelCase			:      List[Any]			=			[slice(UpperCamelCase		)] * rank
 UpperCAmelCase			:      Optional[Any]			=			indices
 UpperCAmelCase			:      Optional[Any]			=			input[s]

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

 return sliced.permute(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Dict		):
 import torch

 UpperCAmelCase			:      List[str]			=			torch.arange(1    ,	UpperCamelCase		)
 UpperCAmelCase			:      Any			=			torch.remainder(UpperCamelCase    ,	UpperCamelCase		)
 UpperCAmelCase			:      Union[str, Any]			=			remainders == 0
 UpperCAmelCase			:      str			=			candidates[divisor_indices]
 UpperCAmelCase			:      Optional[int]			=			torch.max(UpperCamelCase		)
 return largest_divisor, torch.div(UpperCamelCase    ,	UpperCamelCase    ,	rounding_mode="""floor"""		)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Mapping[str, Mapping[int, str]]:





    '''simple docstring'''




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

    return common_inputs

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    return self._config.num_heads

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = -1   ,  _SCREAMING_SNAKE_CASE = -1   ,  _SCREAMING_SNAKE_CASE = False   ,  _SCREAMING_SNAKE_CASE = None   ,  )						->   Mapping[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			super(_SCREAMING_SNAKE_CASE   ,  self		).generate_dummy_inputs(
        _SCREAMING_SNAKE_CASE   ,  batch_size=_SCREAMING_SNAKE_CASE   ,  seq_length=_SCREAMING_SNAKE_CASE   ,  is_pair=_SCREAMING_SNAKE_CASE   ,  framework=_SCREAMING_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			:      List[Any]			=			common_inputs["""input_ids"""].shape
      # Not using the same length for past_key_values
      UpperCAmelCase			:      Any			=			seqlen + 2
      UpperCAmelCase			:      str			=			(
          batch,
          self.num_attention_heads,
          past_key_values_length,
          self._config.hidden_size // self.num_attention_heads,
      )
      UpperCAmelCase			:      Optional[int]			=			[
          (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(self.num_layers		)
      ]

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

    return ordered_inputs




   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    return 13
