




'''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 			_a  (	A_							,							A_							,							A_ ):
       __a				:     Dict   				=					[r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]
       @register_to_config
       def __init__(	self						:	Any   ,				lowercase						:	int   ,				lowercase						:	int   ,				lowercase						:	Optional[int] = None   ,				lowercase						:	int = 50_257   ,				lowercase						:	int = 1_024   ,				lowercase						:	int = 768   ,				lowercase						:	int = 12   ,				lowercase						:	int = 12   ,				lowercase						:	Optional[int] = None   ,				lowercase						:	str = "gelu_new"   ,				lowercase						:	float = 0.1   ,				lowercase						:	float = 0.1   ,				lowercase						:	float = 0.1   ,				lowercase						:	float = 1E-5   ,				lowercase						:	float = 0.02   ,				lowercase						:	bool = True   ,				lowercase						:	bool = True   ,				lowercase						:	bool = False   ,				lowercase						:	bool = False   ,				):



           '''simple docstring'''

           super().__init__()

           UpperCAmelCase			       =		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."				)

           UpperCAmelCase			       =		prefix_inner_dim
           UpperCAmelCase			       =		prefix_hidden_dim

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

           UpperCAmelCase			       =		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   ,				)
           UpperCAmelCase			       =		GPTaLMHeadModel(_lowerCamelCase				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	torch.Tensor   ,				lowercase						:	torch.Tensor   ,				lowercase						:	Optional[torch.Tensor] = None   ,				lowercase						:	Optional[torch.Tensor] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.transformer.transformer.wte(_lowerCamelCase				)
           UpperCAmelCase			       =		self.encode_prefix(_lowerCamelCase				)
           UpperCAmelCase			       =		self.decode_prefix(_lowerCamelCase				)
           UpperCAmelCase			       =		torch.cat((prefix_embeds, embedding_text)   ,				dim=1				)

           if labels is not None:
               UpperCAmelCase			       =		self.get_dummy_token(input_ids.shape[0]   ,				input_ids.device				)
               UpperCAmelCase			       =		torch.cat((dummy_token, input_ids)   ,				dim=1				)
           UpperCAmelCase			       =		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     A      (	self						:	Any   ,				lowercase						:	int   ,				lowercase						:	torch.device				):



           '''simple docstring'''

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



           '''simple docstring'''

           return self.encode_prefix(_lowerCamelCase				)
       @torch.no_grad()
       def     A      (	self						:	List[Any]   ,				lowercase						:	Optional[int]   ,				lowercase						:	Dict   ,				lowercase						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		torch.split(_lowerCamelCase   ,				1   ,				dim=0				)
           UpperCAmelCase			       =		[]
           UpperCAmelCase			       =		[]
           for feature in features:
               UpperCAmelCase			       =		self.decode_prefix(feature.to(_lowerCamelCase				)				)  # back to the clip feature
               # Only support beam search for now
               UpperCAmelCase       ,     UpperCAmelCase			       =		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]				)
           UpperCAmelCase			       =		torch.stack(_lowerCamelCase				)
           UpperCAmelCase			       =		torch.stack(_lowerCamelCase				)
           return generated_tokens, generated_seq_lengths







       @torch.no_grad()
       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	Optional[int]=None   ,				lowercase						:	List[Any]=None   ,				lowercase						:	Optional[int]=None   ,				lowercase						:	int = 5   ,				lowercase						:	int = 67   ,				lowercase						:	float = 1.0   ,				lowercase						:	Optional[int] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		eos_token_id
           UpperCAmelCase			       =		None
           UpperCAmelCase			       =		None
           UpperCAmelCase			       =		torch.ones(_lowerCamelCase   ,				device=_lowerCamelCase   ,				dtype=torch.int				)
           UpperCAmelCase			       =		torch.zeros(_lowerCamelCase   ,				device=_lowerCamelCase   ,				dtype=torch.bool				)

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

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

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

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

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






"""simple docstring"""



from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class       lowerCAmelCase__				(						A_	):







     def __lt__(							self			:			Any    ,      _lowerCamelCase			:			int			):
       return self[-1] < other[-1]







     def __eq__(							self			:			int    ,      _lowerCamelCase			:			Optional[Any]			):
       return self[-1] == other[-1]






def 					_UpperCAmelCase      ( __lowerCamelCase   :       list		)     ->					list:
  _snake_case					     =       []
  # sort into stacks
  for element in collection:
    _snake_case					     =       Stack([element]		)
    _snake_case					     =       bisect_left(__lowerCamelCase ,					__lowerCamelCase		)
    if i != len(__lowerCamelCase		):
      stacks[i].append(__lowerCamelCase		)
    else:
      stacks.append(__lowerCamelCase		)

    # use a heap-based merge to merge stack efficiently
  _snake_case					     =       merge(*(reversed(__lowerCamelCase		) for stack in stacks)		)
  return collection


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



