
"""simple docstring"""



from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class       lowerCAmelCase__				(						A_ ,	A_ ,	A_	):
     __a						=					[R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]







     @register_to_config
     def __init__(							self			:			Any    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Optional[int] = None    ,      _lowerCamelCase			:			int = 50257    ,      _lowerCamelCase			:			int = 1024    ,      _lowerCamelCase			:			int = 768    ,      _lowerCamelCase			:			int = 12    ,      _lowerCamelCase			:			int = 12    ,      _lowerCamelCase			:			Optional[int] = None    ,      _lowerCamelCase			:			str = "gelu_new"    ,      _lowerCamelCase			:			float = 0.1    ,      _lowerCamelCase			:			float = 0.1    ,      _lowerCamelCase			:			float = 0.1    ,      _lowerCamelCase			:			float = 1e-5    ,      _lowerCamelCase			:			float = 0.0_2    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			bool = False    ,      _lowerCamelCase			:			bool = False    ,      ):
       super().__init__()

       _snake_case					     =       prefix_length

       if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
         raise ValueError(
             f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
             f''' `n_embd`: {n_embd} are not equal.'''			)

       _snake_case					     =       prefix_inner_dim
       _snake_case					     =       prefix_hidden_dim

       _snake_case					     =       (
           nn.Linear(self.prefix_inner_dim    ,      self.prefix_hidden_dim			)
           if self.prefix_hidden_dim is not None
           else nn.Identity()
       )
       _snake_case					     =       (
           nn.Linear(self.prefix_hidden_dim    ,      _lowerCamelCase			) if self.prefix_hidden_dim is not None else nn.Identity()
       )

       _snake_case					     =       GPTaConfig(
           vocab_size=_lowerCamelCase    ,      n_positions=_lowerCamelCase    ,      n_embd=_lowerCamelCase    ,      n_layer=_lowerCamelCase    ,      n_head=_lowerCamelCase    ,      n_inner=_lowerCamelCase    ,      activation_function=_lowerCamelCase    ,      resid_pdrop=_lowerCamelCase    ,      embd_pdrop=_lowerCamelCase    ,      attn_pdrop=_lowerCamelCase    ,      layer_norm_epsilon=_lowerCamelCase    ,      initializer_range=_lowerCamelCase    ,      scale_attn_weights=_lowerCamelCase    ,      use_cache=_lowerCamelCase    ,      scale_attn_by_inverse_layer_idx=_lowerCamelCase    ,      reorder_and_upcast_attn=_lowerCamelCase    ,      )
       _snake_case					     =       GPTaLMHeadModel(_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			torch.Tensor    ,      _lowerCamelCase			:			torch.Tensor    ,      _lowerCamelCase			:			Optional[torch.Tensor] = None    ,      _lowerCamelCase			:			Optional[torch.Tensor] = None    ,      ):
       _snake_case					     =       self.transformer.transformer.wte(_lowerCamelCase			)
       _snake_case					     =       self.encode_prefix(_lowerCamelCase			)
       _snake_case					     =       self.decode_prefix(_lowerCamelCase			)
       _snake_case					     =       torch.cat((prefix_embeds, embedding_text)    ,      dim=1			)

       if labels is not None:
         _snake_case					     =       self.get_dummy_token(input_ids.shape[0]    ,      input_ids.device			)
         _snake_case					     =       torch.cat((dummy_token, input_ids)    ,      dim=1			)
       _snake_case					     =       self.transformer(inputs_embeds=_lowerCamelCase    ,      labels=_lowerCamelCase    ,      attention_mask=_lowerCamelCase			)
       if self.prefix_hidden_dim is not None:
         return out, hidden
       else:
         return out







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			torch.device			):
       return torch.zeros(_lowerCamelCase    ,      self.prefix_length    ,      dtype=torch.intaa    ,      device=_lowerCamelCase			)







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			Any			):
       return self.encode_prefix(_lowerCamelCase			)







     @torch.no_grad()
     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			Optional[int]    ,      _lowerCamelCase			:			Dict    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       torch.split(_lowerCamelCase    ,      1    ,      dim=0			)
       _snake_case					     =       []
       _snake_case					     =       []
       for feature in features:
         _snake_case					     =       self.decode_prefix(feature.to(_lowerCamelCase			)			)  # back to the clip feature
         # Only support beam search for now
         _snake_case     ,					_snake_case					     =       self.generate_beam(
             input_embeds=_lowerCamelCase    ,      device=_lowerCamelCase    ,      eos_token_id=_lowerCamelCase			)
         generated_tokens.append(output_tokens[0]			)
         generated_seq_lengths.append(seq_lengths[0]			)
       _snake_case					     =       torch.stack(_lowerCamelCase			)
       _snake_case					     =       torch.stack(_lowerCamelCase			)
       return generated_tokens, generated_seq_lengths







     @torch.no_grad()
     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Optional[int]=None    ,      _lowerCamelCase			:			List[Any]=None    ,      _lowerCamelCase			:			Optional[int]=None    ,      _lowerCamelCase			:			int = 5    ,      _lowerCamelCase			:			int = 67    ,      _lowerCamelCase			:			float = 1.0    ,      _lowerCamelCase			:			Optional[int] = None    ,      ):
       _snake_case					     =       eos_token_id
       _snake_case					     =       None
       _snake_case					     =       None
       _snake_case					     =       torch.ones(_lowerCamelCase    ,      device=_lowerCamelCase    ,      dtype=torch.int			)
       _snake_case					     =       torch.zeros(_lowerCamelCase    ,      device=_lowerCamelCase    ,      dtype=torch.bool			)

       if input_embeds is not None:
         _snake_case					     =       input_embeds
       else:
         _snake_case					     =       self.transformer.transformer.wte(_lowerCamelCase			)

       for i in range(_lowerCamelCase			):
         _snake_case					     =       self.transformer(inputs_embeds=_lowerCamelCase			)
         _snake_case					     =       outputs.logits
         _snake_case					     =       logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
         _snake_case					     =       logits.softmax(-1			).log()

         if scores is None:
           _snake_case     ,					_snake_case					     =       logits.topk(_lowerCamelCase    ,      -1			)
           _snake_case					     =       generated.expand(_lowerCamelCase    ,      *generated.shape[1:]			)
           _snake_case     ,					_snake_case					     =       next_tokens.permute(1    ,      0			), scores.squeeze(0			)
           if tokens is None:
             _snake_case					     =       next_tokens
           else:
             _snake_case					     =       tokens.expand(_lowerCamelCase    ,      *tokens.shape[1:]			)
             _snake_case					     =       torch.cat((tokens, next_tokens)    ,      dim=1			)
         else:
           _snake_case					     =       -float(np.inf			)
           _snake_case					     =       0
           _snake_case					     =       scores[:, None] + logits
           seq_lengths[~is_stopped] += 1
           _snake_case					     =       scores_sum / seq_lengths[:, None]
           _snake_case     ,					_snake_case					     =       scores_sum_average.view(-1			).topk(_lowerCamelCase    ,      -1			)
           _snake_case					     =       next_tokens // scores_sum.shape[1]
           _snake_case					     =       seq_lengths[next_tokens_source]
           _snake_case					     =       next_tokens % scores_sum.shape[1]
           _snake_case					     =       next_tokens.unsqueeze(1			)
           _snake_case					     =       tokens[next_tokens_source]
           _snake_case					     =       torch.cat((tokens, next_tokens)    ,      dim=1			)
           _snake_case					     =       generated[next_tokens_source]
           _snake_case					     =       scores_sum_average * seq_lengths
           _snake_case					     =       is_stopped[next_tokens_source]

         _snake_case					     =       self.transformer.transformer.wte(next_tokens.squeeze()			).view(generated.shape[0]    ,      1    ,      -1			)
         _snake_case					     =       torch.cat((generated, next_token_embed)    ,      dim=1			)
         _snake_case					     =       is_stopped + next_tokens.eq(_lowerCamelCase			).squeeze()
         if is_stopped.all():
           break

       _snake_case					     =       scores / seq_lengths
       _snake_case					     =       scores.argsort(descending=_lowerCamelCase			)
       # tokens tensors are already padded to max_seq_length
       _snake_case					     =       [tokens[i] for i in order]
       _snake_case					     =       torch.stack(_lowerCamelCase    ,      dim=0			)
       _snake_case					     =       torch.tensor([seq_lengths[i] for i in order]    ,      dtype=seq_lengths.dtype			)
       return output_texts, seq_lengths





"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Any:
  stooge(__lowerCamelCase ,					0 ,					len(__lowerCamelCase		) - 1		)
  return arr
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str		)     ->					int:
  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case     ,					_snake_case					     =       arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case					     =       (int)((h - i + 1) / 3		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)

    # Recursively sort last 2/3 elements
    stooge(__lowerCamelCase ,					i + t ,					(__lowerCamelCase)		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)


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



