


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       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase):
     A:		List[str]			=      [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]


     @register_to_config
     def __init__(      self							:     Tuple							,	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 = 0.02							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	)  ->      List[Any]:





      '''simple docstring'''






      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__					:    str          =   prefix_inner_dim
      UpperCamelCase__					:    Optional[int]          =   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__					:    Optional[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__					:    Union[str, Any]          =   GPTaLMHeadModel(lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     torch.Tensor							,	lowerCamelCase__							:     torch.Tensor							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	lowerCamelCase__							:     Optional[torch.Tensor] = None							,	)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   self.transformer.transformer.wte(lowerCamelCase__			)
      UpperCamelCase__					:    Union[str, Any]          =   self.encode_prefix(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   self.decode_prefix(lowerCamelCase__			)
      UpperCamelCase__					:    int          =   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__					:    int          =   torch.cat((dummy_token, input_ids)							,	dim=1			)
      UpperCamelCase__					:    Tuple          =   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							:     Any							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     torch.device			)  ->      torch.Tensor:





      '''simple docstring'''






      return torch.zeros(lowerCamelCase__							,	self.prefix_length							,	dtype=torch.intaa							,	device=lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     int			)  ->      Tuple:





      '''simple docstring'''






      return self.encode_prefix(lowerCamelCase__			)


     @torch.no_grad()
     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[Any]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   torch.split(lowerCamelCase__							,	1							,	dim=0			)
      UpperCamelCase__					:    List[Any]          =   []
      UpperCamelCase__					:    Optional[int]          =   []
      for feature in features:
       UpperCamelCase__					:    List[str]          =   self.decode_prefix(feature.to(lowerCamelCase__			)			)  # back to the clip feature
       # Only support beam search for now
       UpperCamelCase__       ,				UpperCamelCase__					:    List[str]          =   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__					:    Union[str, Any]          =   torch.stack(lowerCamelCase__			)
      UpperCamelCase__					:    str          =   torch.stack(lowerCamelCase__			)
      return generated_tokens, generated_seq_lengths


     @torch.no_grad()
     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     Optional[Any]=None							,	lowerCamelCase__							:     Tuple=None							,	lowerCamelCase__							:     Dict=None							,	lowerCamelCase__							:     int = 5							,	lowerCamelCase__							:     int = 67							,	lowerCamelCase__							:     float = 1.0							,	lowerCamelCase__							:     Optional[int] = None							,	)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   eos_token_id
      UpperCamelCase__					:    int          =   None
      UpperCamelCase__					:    Any          =   None
      UpperCamelCase__					:    int          =   torch.ones(lowerCamelCase__							,	device=lowerCamelCase__							,	dtype=torch.int			)
      UpperCamelCase__					:    Optional[Any]          =   torch.zeros(lowerCamelCase__							,	device=lowerCamelCase__							,	dtype=torch.bool			)

      if input_embeds is not None:
       UpperCamelCase__					:    Optional[int]          =   input_embeds
      else:
       UpperCamelCase__					:    str          =   self.transformer.transformer.wte(lowerCamelCase__			)

      for i in range(lowerCamelCase__			):
       UpperCamelCase__					:    Dict          =   self.transformer(inputs_embeds=lowerCamelCase__			)
       UpperCamelCase__					:    Any          =   outputs.logits
       UpperCamelCase__					:    Optional[int]          =   logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
       UpperCamelCase__					:    Optional[Any]          =   logits.softmax(-1			).log()

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

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

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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      "roberta-prelayernorm"


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     List[Any]=50265							,	lowerCamelCase__							:     Optional[Any]=768							,	lowerCamelCase__							:     str=12							,	lowerCamelCase__							:     Union[str, Any]=12							,	lowerCamelCase__							:     Dict=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     List[str]=512							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Tuple=0.02							,	lowerCamelCase__							:     List[Any]=1E-1_2							,	lowerCamelCase__							:     str=1							,	lowerCamelCase__							:     int=0							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Union[str, Any]="absolute"							,	lowerCamelCase__							:     Optional[Any]=True							,	lowerCamelCase__							:     Dict=None							,	**lowerCamelCase__							:     Any							,	)  ->      Optional[int]:





      '''simple docstring'''






      super().__init__(pad_token_id=lowerCamelCase__							,	bos_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	**lowerCamelCase__			)

      UpperCamelCase__					:    Optional[int]          =   vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   hidden_size
      UpperCamelCase__					:    List[str]          =   num_hidden_layers
      UpperCamelCase__					:    Optional[int]          =   num_attention_heads
      UpperCamelCase__					:    List[str]          =   hidden_act
      UpperCamelCase__					:    Optional[int]          =   intermediate_size
      UpperCamelCase__					:    Optional[int]          =   hidden_dropout_prob
      UpperCamelCase__					:    List[str]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Optional[int]          =   max_position_embeddings
      UpperCamelCase__					:    Optional[Any]          =   type_vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   initializer_range
      UpperCamelCase__					:    Dict          =   layer_norm_eps
      UpperCamelCase__					:    Union[str, Any]          =   position_embedding_type
      UpperCamelCase__					:    Optional[int]          =   use_cache
      UpperCamelCase__					:    int          =   classifier_dropout



class       __magic_name__	(     __lowerCAmelCase):


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Mapping[str, Mapping[int, str]]:





      '''simple docstring'''






      if self.task == "multiple-choice":
       UpperCamelCase__					:    Dict          =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       UpperCamelCase__					:    Any          =   {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]			)


