






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(			UpperCAmelCase_				,      UpperCAmelCase_				,      UpperCAmelCase_   ):





       """simple docstring"""







       a							:						str										=[r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']





       @register_to_config
       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = 5_0_2_5_7    ,     _lowerCamelCase = 1_0_2_4    ,     _lowerCamelCase = 7_6_8    ,     _lowerCamelCase = 1_2    ,     _lowerCamelCase = 1_2    ,     _lowerCamelCase = None    ,     _lowerCamelCase = "gelu_new"    ,     _lowerCamelCase = 0.1    ,     _lowerCamelCase = 0.1    ,     _lowerCamelCase = 0.1    ,     _lowerCamelCase = 1e-5    ,     _lowerCamelCase = 0.0_2    ,     _lowerCamelCase = True    ,     _lowerCamelCase = True    ,     _lowerCamelCase = False    ,     _lowerCamelCase = False    ,     ):
         super().__init__()

         UpperCamelCase_:   int        =							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_:   Optional[int]        =							prefix_inner_dim
         UpperCamelCase_:   str        =							prefix_hidden_dim

         UpperCamelCase_:   Optional[int]        =							(
             nn.Linear(self.prefix_inner_dim    ,     self.prefix_hidden_dim				)
             if self.prefix_hidden_dim is not None
             else nn.Identity()
         )
         UpperCamelCase_:   int        =							(
             nn.Linear(self.prefix_hidden_dim    ,     _lowerCamelCase				) if self.prefix_hidden_dim is not None else nn.Identity()
         )

         UpperCamelCase_:   List[Any]        =							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_:   Optional[Any]        =							GPTaLMHeadModel(_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     ):
         UpperCamelCase_:   List[str]        =							self.transformer.transformer.wte(_lowerCamelCase				)
         UpperCamelCase_:   Any        =							self.encode_prefix(_lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							self.decode_prefix(_lowerCamelCase				)
         UpperCamelCase_:   str        =							torch.cat((prefix_embeds, embedding_text)    ,     dim=1				)

         if labels is not None:
           UpperCamelCase_:   Tuple        =							self.get_dummy_token(input_ids.shape[0]    ,     input_ids.device				)
           UpperCamelCase_:   List[Any]        =							torch.cat((dummy_token, input_ids)    ,     dim=1				)
         UpperCamelCase_:   Optional[Any]        =							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    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         return torch.zeros(_lowerCamelCase    ,     self.prefix_length    ,     dtype=torch.intaa    ,     device=_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase				):
         return self.encode_prefix(_lowerCamelCase				)





       @torch.no_grad()
       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Dict        =							torch.split(_lowerCamelCase    ,     1    ,     dim=0				)
         UpperCamelCase_:   Optional[Any]        =							[]
         UpperCamelCase_:   Optional[Any]        =							[]
         for feature in features:
           UpperCamelCase_:   Any        =							self.decode_prefix(feature.to(_lowerCamelCase				)				)  # back to the clip feature
           # Only support beam search for now
           UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							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_:   Optional[Any]        =							torch.stack(_lowerCamelCase				)
         UpperCamelCase_:   str        =							torch.stack(_lowerCamelCase				)
         return generated_tokens, generated_seq_lengths





       @torch.no_grad()
       def      _a       (   self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase = 5    ,     _lowerCamelCase = 6_7    ,     _lowerCamelCase = 1.0    ,     _lowerCamelCase = None    ,     ):
         UpperCamelCase_:   Tuple        =							eos_token_id
         UpperCamelCase_:   Union[str, Any]        =							None
         UpperCamelCase_:   Union[str, Any]        =							None
         UpperCamelCase_:   Tuple        =							torch.ones(_lowerCamelCase    ,     device=_lowerCamelCase    ,     dtype=torch.int				)
         UpperCamelCase_:   Optional[int]        =							torch.zeros(_lowerCamelCase    ,     device=_lowerCamelCase    ,     dtype=torch.bool				)

         if input_embeds is not None:
           UpperCamelCase_:   List[Any]        =							input_embeds
         else:
           UpperCamelCase_:   int        =							self.transformer.transformer.wte(_lowerCamelCase				)

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

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

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

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





'''simple docstring'''

from numpy import exp, pi, sqrt


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0      )						->   int:

					'''simple docstring'''

					return 1 / sqrt(2 * pi * sigma**2      ) * exp(-((x - mu) ** 2) / (2 * sigma**2)      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

