



'''simple docstring'''


import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)







def 				__lowerCamelCase				(   A__    )       ->	Union[str, Any]:






   """simple docstring"""
   UpperCamelCase					=  torch.exp(A__    )
   UpperCamelCase					=  torch.sum(A__    ,						dim=1    )  # sum of exp(x_i)
   UpperCamelCase					=  torch.sum(x * exp_x    ,						dim=1    )  # sum of x_i * exp(x_i)
   return torch.log(A__    ) - B / A

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  config.output_attentions
       UpperCamelCase					=  config.output_hidden_states
       UpperCamelCase					=  nn.ModuleList([BertLayer(UpperCamelCase__ ) for _ in range(config.num_hidden_layers )] )
       UpperCamelCase					=  nn.ModuleList([BertHighway(UpperCamelCase__ ) for _ in range(config.num_hidden_layers )] )

       UpperCamelCase					=  [-1 for _ in range(config.num_hidden_layers )]






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       if (type(UpperCamelCase__ ) is float) or (type(UpperCamelCase__ ) is int):
          for i in range(len(self.early_exit_entropy ) ):
             UpperCamelCase					=  x
       else:
          UpperCamelCase					=  x






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  pooler.state_dict()
       for highway in self.highway:
          for name, param in highway.pooler.state_dict().items():
             param.copy_(loaded_model[name] )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Any=None  ,						UpperCamelCase__      :					List[Any]=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  ()
       UpperCamelCase					=  ()
       UpperCamelCase					=  ()
       for i, layer_module in enumerate(self.layer ):
          if self.output_hidden_states:
             UpperCamelCase					=  all_hidden_states + (hidden_states,)

          UpperCamelCase					=  layer_module(
              UpperCamelCase__  ,						UpperCamelCase__  ,						head_mask[i]  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  layer_outputs[0]

          if self.output_attentions:
             UpperCamelCase					=  all_attentions + (layer_outputs[1],)

          UpperCamelCase					=  (hidden_states,)
          if self.output_hidden_states:
             UpperCamelCase					=  current_outputs + (all_hidden_states,)
          if self.output_attentions:
             UpperCamelCase					=  current_outputs + (all_attentions,)

          UpperCamelCase					=  self.highway[i](UpperCamelCase__ )
          # logits, pooled_output

          if not self.training:
             UpperCamelCase					=  highway_exit[0]
             UpperCamelCase					=  entropy(UpperCamelCase__ )
             UpperCamelCase					=  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
             UpperCamelCase					=  all_highway_exits + (highway_exit,)

             if highway_entropy < self.early_exit_entropy[i]:
                UpperCamelCase					=  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
                raise HighwayException(UpperCamelCase__  ,						i + 1 )
          else:
             UpperCamelCase					=  all_highway_exits + (highway_exit,)

        # Add last layer
       if self.output_hidden_states:
          UpperCamelCase					=  all_hidden_states + (hidden_states,)

       UpperCamelCase					=  (hidden_states,)
       if self.output_hidden_states:
          UpperCamelCase					=  outputs + (all_hidden_states,)
       if self.output_attentions:
          UpperCamelCase					=  outputs + (all_attentions,)

       UpperCamelCase					=  outputs + (all_highway_exits,)
       return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits

@add_start_docstrings(
    """The Bert Model transformer with early exiting (DeeBERT). """   ,						_a   ,						)
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       super().__init__(UpperCamelCase__ )
       UpperCamelCase					=  config

       UpperCamelCase					=  BertEmbeddings(UpperCamelCase__ )
       UpperCamelCase					=  DeeBertEncoder(UpperCamelCase__ )
       UpperCamelCase					=  BertPooler(UpperCamelCase__ )

       self.init_weights()






    def  A       (  self      :					int ):


       """simple docstring"""







       self.encoder.init_highway_pooler(self.pooler )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       return self.embeddings.word_embeddings






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  value






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       for layer, heads in heads_to_prune.items():
          self.encoder.layer[layer].attention.prune_heads(UpperCamelCase__ )






    @add_start_docstrings_to_model_forward(UpperCamelCase__ )
    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					List[Any]=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					Any=None  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					Tuple=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						):


       """simple docstring"""







       if input_ids is not None and inputs_embeds is not None:
          raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
       elif input_ids is not None:
          UpperCamelCase					=  input_ids.size()
       elif inputs_embeds is not None:
          UpperCamelCase					=  inputs_embeds.size()[:-1]
       else:
          raise ValueError('You have to specify either input_ids or inputs_embeds' )

       UpperCamelCase					=  input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
          UpperCamelCase					=  torch.ones(UpperCamelCase__  ,						device=UpperCamelCase__ )
       if encoder_attention_mask is None:
          UpperCamelCase					=  torch.ones(UpperCamelCase__  ,						device=UpperCamelCase__ )
       if token_type_ids is None:
          UpperCamelCase					=  torch.zeros(UpperCamelCase__  ,						dtype=torch.long  ,						device=UpperCamelCase__ )

       # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
       # ourselves in which case we just need to make it broadcastable to all heads.
       UpperCamelCase					=  self.get_extended_attention_mask(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

       # If a 2D ou 3D attention mask is provided for the cross-attention
       # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
       if encoder_attention_mask.dim() == 3:
          UpperCamelCase					=  encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
          UpperCamelCase					=  encoder_attention_mask[:, None, None, :]

       UpperCamelCase					=  encoder_extended_attention_mask.to(
           dtype=next(self.parameters() ).dtype )  # fp16 compatibility
       UpperCamelCase					=  (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

       # Prepare head mask if needed
       # 1.0 in head_mask indicate we keep the head
       # attention_probs has shape bsz x n_heads x N x N
       # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
       # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
       UpperCamelCase					=  self.get_head_mask(UpperCamelCase__  ,						self.config.num_hidden_layers )

       UpperCamelCase					=  self.embeddings(
           input_ids=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						inputs_embeds=UpperCamelCase__ )
       UpperCamelCase					=  self.encoder(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						head_mask=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						encoder_attention_mask=UpperCamelCase__  ,						)
       UpperCamelCase					=  encoder_outputs[0]
       UpperCamelCase					=  self.pooler(UpperCamelCase__ )

       UpperCamelCase					=  (
           sequence_output,
           pooled_output,
       ) + encoder_outputs[
           1:
       ]  # add hidden_states and attentions if they are here
       return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  message
       UpperCamelCase					=  exit_layer  # start from 1!

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					Union[str, Any] ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  BertPooler(UpperCamelCase__ )
       UpperCamelCase					=  nn.Dropout(config.hidden_dropout_prob )
       UpperCamelCase					=  nn.Linear(config.hidden_size  ,						config.num_labels )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  encoder_outputs[0]
       UpperCamelCase					=  self.pooler(UpperCamelCase__ )
       # "return" pooler_output

       # BertModel
       UpperCamelCase					=  (pooler_input, pooler_output) + encoder_outputs[1:]
       # "return" bmodel_output

       # Dropout and classification
       UpperCamelCase					=  bmodel_output[1]

       UpperCamelCase					=  self.dropout(UpperCamelCase__ )
       UpperCamelCase					=  self.classifier(UpperCamelCase__ )

       return logits, pooled_output

@add_start_docstrings(
    """Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. """   ,						_a   ,						)
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       super().__init__(UpperCamelCase__ )
       UpperCamelCase					=  config.num_labels
       UpperCamelCase					=  config.num_hidden_layers

       UpperCamelCase					=  DeeBertModel(UpperCamelCase__ )
       UpperCamelCase					=  nn.Dropout(config.hidden_dropout_prob )
       UpperCamelCase					=  nn.Linear(config.hidden_size  ,						self.config.num_labels )

       self.init_weights()






    @add_start_docstrings_to_model_forward(UpperCamelCase__ )
    def  A       (  self      :					str  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					List[Any]=None  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					str=-1  ,						UpperCamelCase__      :					Union[str, Any]=False  ,						):


       """simple docstring"""







       UpperCamelCase					=  self.num_layers
       try:
          UpperCamelCase					=  self.bert(
              UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						head_mask=UpperCamelCase__  ,						inputs_embeds=UpperCamelCase__  ,						)
          # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

          UpperCamelCase					=  outputs[1]

          UpperCamelCase					=  self.dropout(UpperCamelCase__ )
          UpperCamelCase					=  self.classifier(UpperCamelCase__ )
          UpperCamelCase					=  (logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
          UpperCamelCase					=  e.message
          UpperCamelCase					=  e.exit_layer
          UpperCamelCase					=  outputs[0]

       if not self.training:
          UpperCamelCase					=  entropy(UpperCamelCase__ )
          UpperCamelCase					=  []
          UpperCamelCase					=  []
       if labels is not None:
          if self.num_labels == 1:
             #  We are doing regression
             UpperCamelCase					=  MSELoss()
             UpperCamelCase					=  loss_fct(logits.view(-1 )  ,						labels.view(-1 ) )
          else:
             UpperCamelCase					=  CrossEntropyLoss()
             UpperCamelCase					=  loss_fct(logits.view(-1  ,						self.num_labels )  ,						labels.view(-1 ) )

          # work with highway exits
          UpperCamelCase					=  []
          for highway_exit in outputs[-1]:
             UpperCamelCase					=  highway_exit[0]
             if not self.training:
                highway_logits_all.append(UpperCamelCase__ )
                highway_entropy.append(highway_exit[2] )
             if self.num_labels == 1:
                #  We are doing regression
                UpperCamelCase					=  MSELoss()
                UpperCamelCase					=  loss_fct(highway_logits.view(-1 )  ,						labels.view(-1 ) )
             else:
                UpperCamelCase					=  CrossEntropyLoss()
                UpperCamelCase					=  loss_fct(highway_logits.view(-1  ,						self.num_labels )  ,						labels.view(-1 ) )
             highway_losses.append(UpperCamelCase__ )

          if train_highway:
             UpperCamelCase					=  (sum(highway_losses[:-1] ),) + outputs
             # exclude the final highway, of course
          else:
             UpperCamelCase					=  (loss,) + outputs
       if not self.training:
          UpperCamelCase					=  outputs + ((original_entropy, highway_entropy), exit_layer)
          if output_layer >= 0:
             UpperCamelCase					=  (
                 (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
             )  # use the highway of the last layer

       return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)






'''simple docstring'''


import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_lowerCamelCase	:       Union[str, Any]      	=	"\\n\n"

_lowerCamelCase	:       List[str]      	=	"\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

_lowerCamelCase	:       Dict      	=	"\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,						_KWARGS_DESCRIPTION		)
class  SCREAMING_SNAKE_CASE (  datasets.Metric		):






    """simple docstring"""





    def  A       (  self      :					Tuple ):


       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
               {
                   'input_texts': datasets.Value('string' ),
               } )  ,						reference_urls=['https://huggingface.co/docs/transformers/perplexity']  ,						)






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					int = 1_6  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					List[Any]=None ):


       """simple docstring"""







       if device is not None:
          assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
          if device == "gpu":
             UpperCamelCase					=  'cuda'
       else:
          UpperCamelCase					=  'cuda' if torch.cuda.is_available() else 'cpu'

       UpperCamelCase					=  AutoModelForCausalLM.from_pretrained(UpperCamelCase__ )
       UpperCamelCase					=  model.to(UpperCamelCase__ )

       UpperCamelCase					=  AutoTokenizer.from_pretrained(UpperCamelCase__ )

       # if batch_size > 1 (which generally leads to padding being required), and
       # if there is not an already assigned pad_token, assign an existing
       # special token to also be the padding token
       if tokenizer.pad_token is None and batch_size > 1:
          UpperCamelCase					=  list(tokenizer.special_tokens_map_extended.values() )
          # check that the model already has at least one special token defined
          assert (
              len(UpperCamelCase__ ) > 0
          ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
          # assign one of the special tokens to also be the pad token
          tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]} )

       if add_start_token:
          # leave room for <BOS> token to be added:
          assert (
              tokenizer.bos_token is not None
          ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
          UpperCamelCase					=  model.config.max_length - 1
       else:
          UpperCamelCase					=  model.config.max_length

       UpperCamelCase					=  tokenizer(
           UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						return_tensors='pt'  ,						return_attention_mask=UpperCamelCase__  ,						).to(UpperCamelCase__ )

       UpperCamelCase					=  encodings['input_ids']
       UpperCamelCase					=  encodings['attention_mask']

       # check that each input is long enough:
       if add_start_token:
          assert torch.all(torch.ge(attn_masks.sum(1 )  ,						1 ) ), "Each input text must be at least one token long."
       else:
          assert torch.all(
              torch.ge(attn_masks.sum(1 )  ,						2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

       UpperCamelCase					=  []
       UpperCamelCase					=  CrossEntropyLoss(reduction='none' )

       for start_index in logging.tqdm(range(0  ,						len(UpperCamelCase__ )  ,						UpperCamelCase__ ) ):
          UpperCamelCase					=  min(start_index + batch_size  ,						len(UpperCamelCase__ ) )
          UpperCamelCase					=  encoded_texts[start_index:end_index]
          UpperCamelCase					=  attn_masks[start_index:end_index]

          if add_start_token:
             UpperCamelCase					=  torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(UpperCamelCase__ )
             UpperCamelCase					=  torch.cat([bos_tokens_tensor, encoded_batch]  ,						dim=1 )
             UpperCamelCase					=  torch.cat(
                 [torch.ones(bos_tokens_tensor.size()  ,						dtype=torch.intaa ).to(UpperCamelCase__ ), attn_mask]  ,						dim=1 )

          UpperCamelCase					=  encoded_batch

          with torch.no_grad():
             UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__ ).logits

          UpperCamelCase					=  out_logits[..., :-1, :].contiguous()
          UpperCamelCase					=  labels[..., 1:].contiguous()
          UpperCamelCase					=  attn_mask[..., 1:].contiguous()

          UpperCamelCase					=  torch.expa(
              (loss_fct(shift_logits.transpose(1  ,						2 )  ,						UpperCamelCase__ ) * shift_attention_mask_batch).sum(1 )
              / shift_attention_mask_batch.sum(1 ) )

          ppls += perplexity_batch.tolist()

       return {"perplexities": ppls, "mean_perplexity": np.mean(UpperCamelCase__ )}

