



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


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''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 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Dict        =  "gpt_neo"
       UpperCAmelCase__				:					List[str]        =  ["past_key_values"]
       UpperCAmelCase__				:					Optional[Any]        =  {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
       def __init__(			self,			SCREAMING_SNAKE_CASE_=5_0257,			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_=5_0256,			SCREAMING_SNAKE_CASE_=5_0256,			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[Any]:
          UpperCamelCase    :  Tuple							=	vocab_size
          UpperCamelCase    :  int							=	max_position_embeddings
          UpperCamelCase    :  Optional[Any]							=	hidden_size
          UpperCamelCase    :  str							=	num_layers
          UpperCamelCase    :  Any							=	num_heads
          UpperCamelCase    :  str							=	intermediate_size
          UpperCamelCase    :  Any							=	window_size
          UpperCamelCase    :  Any							=	activation_function
          UpperCamelCase    :  Tuple							=	resid_dropout
          UpperCamelCase    :  List[str]							=	embed_dropout
          UpperCamelCase    :  Dict							=	attention_dropout
          UpperCamelCase    :  Optional[Any]							=	classifier_dropout
          UpperCamelCase    :  Optional[int]							=	layer_norm_epsilon
          UpperCamelCase    :  Optional[Any]							=	initializer_range
          UpperCamelCase    :  List[Any]							=	use_cache

          UpperCamelCase    :  Tuple							=	bos_token_id
          UpperCamelCase    :  Tuple							=	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 					snake_case_       (			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  Optional[int]							=	[]
          for item in attention_types:
             for _ in range(item[1]       ):
                attentions.extend(item[0]       )
          return attentions






def 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    List[Any]       ,     snake_case__       :    int	)	->     Dict:
   import torch

   UpperCamelCase    :  Dict							=	input.size()
   UpperCamelCase    :  str							=	len(snake_case__	)
   UpperCamelCase    :  str							=	shape[dimension]

   UpperCamelCase    :  List[Any]							=	torch.arange(0       ,     snake_case__       ,     snake_case__	)
   UpperCamelCase    :  Optional[int]							=	torch.div(sizedim - size       ,     snake_case__       ,     rounding_mode='floor'	) + 1
   UpperCamelCase    :  Dict							=	torch.arange(snake_case__	) + low_indices[:min_length][:, None]

   UpperCamelCase    :  Any							=	[slice(snake_case__	)] * rank
   UpperCamelCase    :  List[str]							=	indices
   UpperCamelCase    :  str							=	input[s]

   UpperCamelCase    :  Dict							=	list(range(0       ,     rank + 1	)	)
   perm.append(perm.pop(dimension + 1	)	)

   return sliced.permute(snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    Tuple       ,     snake_case__       :    Optional[int]	)	->     Union[str, Any]:
   import torch

   UpperCamelCase    :  Dict							=	torch.arange(1       ,     snake_case__	)
   UpperCamelCase    :  Any							=	torch.remainder(snake_case__       ,     snake_case__	)
   UpperCamelCase    :  Any							=	remainders == 0
   UpperCamelCase    :  int							=	candidates[divisor_indices]
   UpperCamelCase    :  str							=	torch.max(snake_case__	)
   return largest_divisor, torch.div(snake_case__       ,     snake_case__       ,     rounding_mode='floor'	)



class 				lowerCAmelCase_		(  a__							):
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  int							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs'       )
             UpperCamelCase    :  Tuple							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  List[Any]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.num_heads
       def 					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]:
          UpperCamelCase    :  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    :  List[str]							=	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    :  Dict							=	common_inputs['input_ids'].shape
                # Not using the same length for past_key_values
                UpperCamelCase    :  Union[str, Any]							=	seqlen + 2
                UpperCamelCase    :  int							=	(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                UpperCamelCase    :  str							=	[
                    (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    :  List[Any]							=	ordered_inputs['attention_mask'].dtype
             UpperCamelCase    :  Union[str, Any]							=	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 					snake_case_       (			self       )     ->		int:
          return 13








from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def 		UpperCamelCase	(   )	->     tuple[list[int], int]:
   UpperCamelCase    :  int							=	[randint(-1000       ,     1000	) for i in range(10	)]
   UpperCamelCase    :  Dict							=	randint(-5000       ,     5000	)
   return (arr, r)


__UpperCAmelCase          =       make_dataset()
def 		UpperCamelCase	(   snake_case__       :    list[int]       ,     snake_case__       :    int	)	->     tuple[int, ...]:
   for triplet in permutations(snake_case__       ,     3	):
      if sum(snake_case__	) == target:
         return tuple(sorted(snake_case__	)	)
   return (0, 0, 0)
def 		UpperCamelCase	(   snake_case__       :    list[int]       ,     snake_case__       :    int	)	->     tuple[int, int, int]:
   arr.sort()
   UpperCamelCase    :  List[str]							=	len(snake_case__	)
   for i in range(n - 1	):
      UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	i + 1, n - 1
      while left < right:
         if arr[i] + arr[left] + arr[right] == target:
            return (arr[i], arr[left], arr[right])
         elif arr[i] + arr[left] + arr[right] < target:
            left += 1
         elif arr[i] + arr[left] + arr[right] > target:
            right -= 1
   return (0, 0, 0)
def 		UpperCamelCase	(   )	->     tuple[float, float]:
   UpperCamelCase    :  Any							=	'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
   UpperCamelCase    :  Optional[Any]							=	'\ntriplet_sum1(*dataset)\n'
   UpperCamelCase    :  Dict							=	'\ntriplet_sum2(*dataset)\n'
   UpperCamelCase    :  Optional[int]							=	repeat(setup=snake_case__       ,     stmt=snake_case__       ,     repeat=5       ,     number=10000	)
   UpperCamelCase    :  Any							=	repeat(setup=snake_case__       ,     stmt=snake_case__       ,     repeat=5       ,     number=10000	)
   return (min(snake_case__	), min(snake_case__	))


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

    testmod()
    __UpperCAmelCase          =       solution_times()
    print(F"""The time for naive implementation is {times[0]}.""")
    print(F"""The time for optimized implementation is {times[1]}.""")



