'''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      ( UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__	):
    a__       :						int          =  [r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]



    @register_to_config
    def __init__(self    :    List[Any]   ,			__a    :    int   ,			__a    :    int   ,			__a    :    Optional[int] = None   ,			__a    :    int = 50257   ,			__a    :    int = 1024   ,			__a    :    int = 768   ,			__a    :    int = 12   ,			__a    :    int = 12   ,			__a    :    Optional[int] = None   ,			__a    :    str = "gelu_new"   ,			__a    :    float = 0.1   ,			__a    :    float = 0.1   ,			__a    :    float = 0.1   ,			__a    :    float = 1E-5   ,			__a    :    float = 0.02   ,			__a    :    bool = True   ,			__a    :    bool = True   ,			__a    :    bool = False   ,			__a    :    bool = False   ,			):
         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   ,			__a ) if self.prefix_hidden_dim is not None else nn.Identity()
         )

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



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None   ,			):
         UpperCAmelCase_           =   self.transformer.transformer.wte(__a )
         UpperCAmelCase_           =   self.encode_prefix(__a )
         UpperCAmelCase_           =   self.decode_prefix(__a )
         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=__a   ,			labels=__a   ,			attention_mask=__a )
         if self.prefix_hidden_dim is not None:
              return out, hidden
         else:
              return out



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    int   ,			__a    :    torch.device ):
         return torch.zeros(__a   ,			self.prefix_length   ,			dtype=torch.intaa   ,			device=__a )



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Tuple ):
         return self.encode_prefix(__a )



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






    @torch.no_grad()
    def 					_lowercase						(self    :    List[str]   ,			__a    :    Dict=None   ,			__a    :    Optional[int]=None   ,			__a    :    List[Any]=None   ,			__a    :    int = 5   ,			__a    :    int = 67   ,			__a    :    float = 1.0   ,			__a    :    Optional[int] = None   ,			):
         UpperCAmelCase_           =   eos_token_id
         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   torch.ones(__a   ,			device=__a   ,			dtype=torch.int )
         UpperCAmelCase_           =   torch.zeros(__a   ,			device=__a   ,			dtype=torch.bool )

         if input_embeds is not None:
              UpperCAmelCase_           =   input_embeds
         else:
              UpperCAmelCase_           =   self.transformer.transformer.wte(__a )

         for i in range(__a ):
              UpperCAmelCase_           =   self.transformer(inputs_embeds=__a )
              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(__a   ,			-1 )
                   UpperCAmelCase_           =   generated.expand(__a   ,			*generated.shape[1:] )
                   UpperCAmelCase_    , UpperCAmelCase_           =   next_tokens.permute(1   ,			0 ), scores.squeeze(0 )
                   if tokens is None:
                        UpperCAmelCase_           =   next_tokens
                   else:
                        UpperCAmelCase_           =   tokens.expand(__a   ,			*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(__a   ,			-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(__a ).squeeze()
              if is_stopped.all():
                   break

         UpperCAmelCase_           =   scores / seq_lengths
         UpperCAmelCase_           =   scores.argsort(descending=__a )
         # tokens tensors are already padded to max_seq_length
         UpperCAmelCase_           =   [tokens[i] for i in order]
         UpperCAmelCase_           =   torch.stack(__a   ,			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





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : int | None = None					,    snake_case_       : int | None = None			)    ->      None:


     '''simple docstring'''




     if start is None:
          UpperCAmelCase_           =   0

     if end is None:
          UpperCAmelCase_           =   len(snake_case_			) - 1

     if start >= end:
          return

     UpperCAmelCase_           =   (start + end) // 2

     slowsort(snake_case_					,    snake_case_					,    snake_case_			)
     slowsort(snake_case_					,    mid + 1					,    snake_case_			)

     if sequence[end] < sequence[mid]:
          UpperCAmelCase_    , UpperCAmelCase_           =   sequence[mid], sequence[end]

     slowsort(snake_case_					,    snake_case_					,    end - 1			)


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

    testmod()




