






"""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_	):
    UpperCAmelCase_ :     Optional[Any]	     =[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__()

      lowercase							    =       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.'       )

      lowercase							    =       prefix_inner_dim
      lowercase							    =       prefix_hidden_dim

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

      lowercase							    =       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							,		)
      lowercase							    =       GPTaLMHeadModel(_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		):
      lowercase							    =       self.transformer.transformer.wte(_lowerCamelCase       )
      lowercase							    =       self.encode_prefix(_lowerCamelCase       )
      lowercase							    =       self.decode_prefix(_lowerCamelCase       )
      lowercase							    =       torch.cat((prefix_embeds, embedding_text)							,		dim=1       )

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







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







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







    @torch.no_grad()
    def 			UpperCamelCase_      (			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase = 5							,		_lowerCamelCase = 6_7							,		_lowerCamelCase = 1.0							,		_lowerCamelCase = None							,		):
      lowercase							    =       eos_token_id
      lowercase							    =       None
      lowercase							    =       None
      lowercase							    =       torch.ones(_lowerCamelCase							,		device=_lowerCamelCase							,		dtype=torch.int       )
      lowercase							    =       torch.zeros(_lowerCamelCase							,		device=_lowerCamelCase							,		dtype=torch.bool       )

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

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

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

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

      lowercase							    =       scores / seq_lengths
      lowercase							    =       scores.argsort(descending=_lowerCamelCase       )
      # tokens tensors are already padded to max_seq_length
      lowercase							    =       [tokens[i] for i in order]
      lowercase							    =       torch.stack(_lowerCamelCase							,		dim=0       )
      lowercase							    =       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 collections.abc import Iterator
from typing import Generic, TypeVar

_UpperCamelCase		:  Any						=		TypeVar('T')

class        a     (   Generic[T]	):







    def __init__(			self							,		_lowerCamelCase       ):
      lowercase							    =       data
      lowercase							    =       None







    def __str__(			self       ):
      return F'{self.data}'


class        a     (   Generic[T]	):







    def __init__(			self       ):
      lowercase							    =       None







    def __iter__(			self       ):
      lowercase							    =       self.top
      while node:
        yield node.data
        lowercase							    =       node.next







    def __str__(			self       ):
      return "->".join([str(_lowerCamelCase       ) for item in self]       )







    def __len__(			self       ):
      return len(tuple(iter(self       )       )       )







    def 			UpperCamelCase_      (			self       ):
      return self.top is None







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       Node(_lowerCamelCase       )
      if not self.is_empty():
        lowercase							    =       self.top
      lowercase							    =       node







    def 			UpperCamelCase_      (			self       ):
      if self.is_empty():
        raise IndexError('pop from empty stack'       )
      assert isinstance(self.top							,		_lowerCamelCase       )
      lowercase							    =       self.top
      lowercase							    =       self.top.next
      return pop_node.data







    def 			UpperCamelCase_      (			self       ):
      if self.is_empty():
        raise IndexError('peek from empty stack'       )

      assert self.top is not None
      return self.top.data







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       None


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

    testmod()


