'''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


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

UpperCamelCase__  :						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 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Union[str, Any]   				= '''gpt_neo'''
      UpperCAmelCase_   :   List[Any]   				= ['''past_key_values''']
      UpperCAmelCase_   :   Optional[Any]   				= {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




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



         '''simple docstring'''



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

         UpperCAmelCase__				:					Any 					=	bos_token_id
         UpperCAmelCase__				:					Optional[Any] 					=	eos_token_id

         UpperCAmelCase__				:					Optional[int] 					=	attention_types
         UpperCAmelCase__				:					List[Any] 					=	self.expand_attention_types_params(lowerCamelCase_				)

         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=lowerCamelCase_       ,eos_token_id=lowerCamelCase_       ,**lowerCamelCase_				)




      @staticmethod
      def 		lowerCAmelCase__       (					lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



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







def __UpperCamelCase( _A       :					List[Any]	,      _A       :					List[str]	,      _A       :					str	,      _A       :					List[Any]     ):
   '''simple docstring'''
   import torch

   UpperCAmelCase__				:					Tuple 					=	input.size()
   UpperCAmelCase__				:					List[str] 					=	len(_A     )
   UpperCAmelCase__				:					Tuple 					=	shape[dimension]

   UpperCAmelCase__				:					int 					=	torch.arange(0	,      _A	,      _A     )
   UpperCAmelCase__				:					Union[str, Any] 					=	torch.div(sizedim - size	,      _A	,      rounding_mode='''floor'''     ) + 1
   UpperCAmelCase__				:					List[Any] 					=	torch.arange(_A     ) + low_indices[:min_length][:, None]

   UpperCAmelCase__				:					int 					=	[slice(_A     )] * rank
   UpperCAmelCase__				:					int 					=	indices
   UpperCAmelCase__				:					str 					=	input[s]

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

   return sliced.permute(_A     )







def __UpperCamelCase( _A       :					List[Any]	,      _A       :					Optional[int]     ):
   '''simple docstring'''
   import torch

   UpperCAmelCase__				:					Tuple 					=	torch.arange(1	,      _A     )
   UpperCAmelCase__				:					Optional[Any] 					=	torch.remainder(_A	,      _A     )
   UpperCAmelCase__				:					Tuple 					=	remainders == 0
   UpperCAmelCase__				:					str 					=	candidates[divisor_indices]
   UpperCAmelCase__				:					List[Any] 					=	torch.max(_A     )
   return largest_divisor, torch.div(_A	,      _A	,      rounding_mode='''floor'''     )


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


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



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}				)
         if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase_       ,direction='''inputs'''				)
            UpperCAmelCase__				:					Tuple 					=	{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
            UpperCAmelCase__				:					Union[str, Any] 					=	{0: '''batch''', 1: '''sequence'''}

         return common_inputs




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         return self._config.num_heads




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



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	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()
         UpperCAmelCase__				:					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

               UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               UpperCAmelCase__				:					Optional[Any] 					=	seqlen + 2
               UpperCAmelCase__				:					List[str] 					=	(
                   batch,
                   self.num_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // self.num_attention_heads,
               )
               UpperCAmelCase__				:					str 					=	[
                   (torch.zeros(lowerCamelCase_				), torch.zeros(lowerCamelCase_				)) for _ in range(self.num_layers				)
               ]

         UpperCAmelCase__				:					Tuple 					=	common_inputs['''attention_mask''']
         if self.use_past:
            UpperCAmelCase__				:					Tuple 					=	ordered_inputs['''attention_mask'''].dtype
            UpperCAmelCase__				:					Union[str, Any] 					=	torch.cat(
                [ordered_inputs['''attention_mask'''], torch.ones(lowerCamelCase_       ,lowerCamelCase_       ,dtype=lowerCamelCase_				)]       ,dim=1				)

         return ordered_inputs




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         return 13





'''simple docstring'''

from __future__ import annotations







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if (direction == 1 and array[indexa] > array[indexa]) or (
       direction == 0 and array[indexa] < array[indexa]
   ):
      UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	array[indexa], array[indexa]







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					List[Any] 					=	int(length / 2     )
      for i in range(_A	,      low + middle     ):
         comp_and_swap(_A	,      _A	,      i + middle	,      _A     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )
      bitonic_merge(_A	,      low + middle	,      _A	,      _A     )







def __UpperCamelCase( _A       :					list[int]	,      _A       :					int	,      _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   if length > 1:
      UpperCAmelCase__				:					Optional[int] 					=	int(length / 2     )
      bitonic_sort(_A	,      _A	,      _A	,      1     )
      bitonic_sort(_A	,      low + middle	,      _A	,      0     )
      bitonic_merge(_A	,      _A	,      _A	,      _A     )


if __name__ == "__main__":
  UpperCamelCase__  :						Dict					=      input('Enter numbers separated by a comma:\n').strip()
  UpperCamelCase__  :						Tuple					=      [int(item.strip()) for item in user_input.split(',')]

  bitonic_sort(unsorted, 0, len(unsorted), 1)
  print('\nSorted array in ascending order is: ', end='')
  print(*unsorted, sep=', ')

  bitonic_merge(unsorted, 0, len(unsorted), 0)
  print('Sorted array in descending order is: ', end='')
  print(*unsorted, sep=', ')




