




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        __UpperCAmelCase						(							lowerCamelCase_ :     str  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      torch.exp(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      torch.sum(lowerCamelCase_  ,							dim=1  )  # sum of exp(x_i)
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      torch.sum(x * exp_x  ,							dim=1  )  # sum of x_i * exp(x_i)
			return torch.log(lowerCamelCase_  ) - B / A







class 				lowerCAmelCase_       (					nn.Module							):






				def __init__(			self      ,snake_case__ ):
							super().__init__()
							SCREAMING_SNAKE_CASE_   : Any					  =      config.output_attentions
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      config.output_hidden_states
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      nn.ModuleList([BertLayer(snake_case__ ) for _ in range(config.num_hidden_layers )] )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      nn.ModuleList([BertHighway(snake_case__ ) for _ in range(config.num_hidden_layers )] )

							SCREAMING_SNAKE_CASE_   : Dict					  =      [-1 for _ in range(config.num_hidden_layers )]






				def 		snake_case	(			self      ,snake_case__ ):
							if (type(snake_case__ ) is float) or (type(snake_case__ ) is int):
										for i in range(len(self.early_exit_entropy ) ):
													SCREAMING_SNAKE_CASE_   : str					  =      x
							else:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      x






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      pooler.state_dict()
							for highway in self.highway:
										for name, param in highway.pooler.state_dict().items():
													param.copy_(loaded_model[name] )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      ()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ()
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ()
							for i, layer_module in enumerate(self.layer ):
										if self.output_hidden_states:
													SCREAMING_SNAKE_CASE_   : Any					  =      all_hidden_states + (hidden_states,)

										SCREAMING_SNAKE_CASE_   : int					  =      layer_module(
										    snake_case__      ,snake_case__      ,head_mask[i]      ,snake_case__      ,snake_case__ )
										SCREAMING_SNAKE_CASE_   : Any					  =      layer_outputs[0]

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

										SCREAMING_SNAKE_CASE_   : Tuple					  =      (hidden_states,)
										if self.output_hidden_states:
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      current_outputs + (all_hidden_states,)
										if self.output_attentions:
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      current_outputs + (all_attentions,)

										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.highway[i](snake_case__ )
										# logits, pooled_output

										if not self.training:
													SCREAMING_SNAKE_CASE_   : List[Any]					  =      highway_exit[0]
													SCREAMING_SNAKE_CASE_   : List[str]					  =      entropy(snake_case__ )
													SCREAMING_SNAKE_CASE_   : int					  =      highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
													SCREAMING_SNAKE_CASE_   : Dict					  =      all_highway_exits + (highway_exit,)

													if highway_entropy < self.early_exit_entropy[i]:
																SCREAMING_SNAKE_CASE_   : Tuple					  =      (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																raise HighwayException(snake_case__      ,i + 1 )
										else:
													SCREAMING_SNAKE_CASE_   : Any					  =      all_highway_exits + (highway_exit,)

        # Add last layer
							if self.output_hidden_states:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      all_hidden_states + (hidden_states,)

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      (hidden_states,)
							if self.output_hidden_states:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      outputs + (all_hidden_states,)
							if self.output_attentions:
										SCREAMING_SNAKE_CASE_   : Any					  =      outputs + (all_attentions,)

							SCREAMING_SNAKE_CASE_   : Tuple					  =      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). "						,     lowerCamelCase_						,     )
class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,snake_case__ ):
							super().__init__(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      config

							SCREAMING_SNAKE_CASE_   : int					  =      BertEmbeddings(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      DeeBertEncoder(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      BertPooler(snake_case__ )

							self.init_weights()






				def 		snake_case	(			self ):
							self.encoder.init_highway_pooler(self.pooler )






				def 		snake_case	(			self ):
							return self.embeddings.word_embeddings






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Any					  =      value






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






				@add_start_docstrings_to_model_forward(snake_case__ )
				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,):
							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:
										SCREAMING_SNAKE_CASE_   : Dict					  =      input_ids.size()
							elif inputs_embeds is not None:
										SCREAMING_SNAKE_CASE_   : str					  =      inputs_embeds.size()[:-1]
							else:
										raise ValueError('You have to specify either input_ids or inputs_embeds' )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
										SCREAMING_SNAKE_CASE_   : int					  =      torch.ones(snake_case__      ,device=snake_case__ )
							if encoder_attention_mask is None:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.ones(snake_case__      ,device=snake_case__ )
							if token_type_ids is None:
										SCREAMING_SNAKE_CASE_   : int					  =      torch.zeros(snake_case__      ,dtype=torch.long      ,device=snake_case__ )

							# 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.
							SCREAMING_SNAKE_CASE_   : torch.Tensor					  =      self.get_extended_attention_mask(snake_case__      ,snake_case__      ,snake_case__ )

							# 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:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
										SCREAMING_SNAKE_CASE_   : str					  =      encoder_attention_mask[:, None, None, :]

							SCREAMING_SNAKE_CASE_   : Tuple					  =      encoder_extended_attention_mask.to(
							    dtype=next(self.parameters() ).dtype )  # fp16 compatibility
							SCREAMING_SNAKE_CASE_   : int					  =      (1.0 - encoder_extended_attention_mask) * -10000.0

							# 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]
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_head_mask(snake_case__      ,self.config.num_hidden_layers )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.embeddings(
							    input_ids=snake_case__      ,position_ids=snake_case__      ,token_type_ids=snake_case__      ,inputs_embeds=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.encoder(
							    snake_case__      ,attention_mask=snake_case__      ,head_mask=snake_case__      ,encoder_hidden_states=snake_case__      ,encoder_attention_mask=snake_case__      ,)
							SCREAMING_SNAKE_CASE_   : Tuple					  =      encoder_outputs[0]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.pooler(snake_case__ )

							SCREAMING_SNAKE_CASE_   : int					  =      (
							    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 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      message
							SCREAMING_SNAKE_CASE_   : int					  =      exit_layer  # start from 1!







class 				lowerCAmelCase_       (					nn.Module							):






				def __init__(			self      ,snake_case__ ):
							super().__init__()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      BertPooler(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      nn.Dropout(config.hidden_dropout_prob )
							SCREAMING_SNAKE_CASE_   : Any					  =      nn.Linear(config.hidden_size      ,config.num_labels )






				def 		snake_case	(			self      ,snake_case__ ):
							# Pooler
							SCREAMING_SNAKE_CASE_   : Tuple					  =      encoder_outputs[0]
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.pooler(snake_case__ )
							# "return" pooler_output

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

							# Dropout and classification
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      bmodel_output[1]

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.dropout(snake_case__ )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      self.classifier(snake_case__ )

							return logits, pooled_output







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






				def __init__(			self      ,snake_case__ ):
							super().__init__(snake_case__ )
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      config.num_labels
							SCREAMING_SNAKE_CASE_   : int					  =      config.num_hidden_layers

							SCREAMING_SNAKE_CASE_   : int					  =      DeeBertModel(snake_case__ )
							SCREAMING_SNAKE_CASE_   : str					  =      nn.Dropout(config.hidden_dropout_prob )
							SCREAMING_SNAKE_CASE_   : str					  =      nn.Linear(config.hidden_size      ,self.config.num_labels )

							self.init_weights()






				@add_start_docstrings_to_model_forward(snake_case__ )
				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__=-1      ,snake_case__=False      ,):
							SCREAMING_SNAKE_CASE_   : str					  =      self.num_layers
							try:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      self.bert(
										    snake_case__      ,attention_mask=snake_case__      ,token_type_ids=snake_case__      ,position_ids=snake_case__      ,head_mask=snake_case__      ,inputs_embeds=snake_case__      ,)
										# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

										SCREAMING_SNAKE_CASE_   : str					  =      outputs[1]

										SCREAMING_SNAKE_CASE_   : int					  =      self.dropout(snake_case__ )
										SCREAMING_SNAKE_CASE_   : Any					  =      self.classifier(snake_case__ )
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      (logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      e.message
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      e.exit_layer
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      outputs[0]

							if not self.training:
										SCREAMING_SNAKE_CASE_   : str					  =      entropy(snake_case__ )
										SCREAMING_SNAKE_CASE_   : str					  =      []
										SCREAMING_SNAKE_CASE_   : Any					  =      []
							if labels is not None:
										if self.num_labels == 1:
													#  We are doing regression
													SCREAMING_SNAKE_CASE_   : Tuple					  =      MSELoss()
													SCREAMING_SNAKE_CASE_   : List[Any]					  =      loss_fct(logits.view(-1 )      ,labels.view(-1 ) )
										else:
													SCREAMING_SNAKE_CASE_   : str					  =      CrossEntropyLoss()
													SCREAMING_SNAKE_CASE_   : List[Any]					  =      loss_fct(logits.view(-1      ,self.num_labels )      ,labels.view(-1 ) )

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

										if train_highway:
													SCREAMING_SNAKE_CASE_   : str					  =      (sum(highway_losses[:-1] ),) + outputs
													# exclude the final highway, of course
										else:
													SCREAMING_SNAKE_CASE_   : List[Any]					  =      (loss,) + outputs
							if not self.training:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
													SCREAMING_SNAKE_CASE_   : Dict					  =      (
													    (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


a		:					List[Any]	      =     '''\

'''

a		:					Optional[int]	      =     '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
'''

a		:					List[Any]	      =     '''
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        78.22
        >>> print(round(results["perplexities"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = datasets.load_dataset("wikitext",
        ...                                     "wikitext-2-raw-v1",
        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        60.35
        >>> print(round(results["perplexities"][0], 2))
        81.12
'''


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






							def        __a  (  self )      ->					List[Any]:
								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 ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = 16 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__=None )      ->					Tuple:
								if device is not None:
									assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
									if device == "gpu":
										a :						Optional[Any]      =							"cuda"
								else:
									a :						str      =							"cuda" if torch.cuda.is_available() else "cpu"

								a :						str      =							AutoModelForCausalLM.from_pretrained(lowerCAmelCase__ )
								a :						int      =							model.to(lowerCAmelCase__ )

								a :						str      =							AutoTokenizer.from_pretrained(lowerCAmelCase__ )

								# 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:
									a :						str      =							list(tokenizer.special_tokens_map_extended.values() )
									# check that the model already has at least one special token defined
									assert (
									    len(lowerCAmelCase__ ) > 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"
									a :						List[Any]      =							model.config.max_length - 1
								else:
									a :						Union[str, Any]      =							model.config.max_length

								a :						Union[str, Any]      =							tokenizer(
								    lowerCAmelCase__ ,   add_special_tokens=lowerCAmelCase__ ,   padding=lowerCAmelCase__ ,   truncation=lowerCAmelCase__ ,   max_length=lowerCAmelCase__ ,   return_tensors="pt" ,   return_attention_mask=lowerCAmelCase__ ,   ).to(lowerCAmelCase__ )

								a :						str      =							encodings["input_ids"]
								a :						Optional[int]      =							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."

								a :						List[Any]      =							[]
								a :						List[Any]      =							CrossEntropyLoss(reduction="none" )

								for start_index in logging.tqdm(range(0 ,   len(lowerCAmelCase__ ) ,   lowerCAmelCase__ ) ):
									a :						Optional[Any]      =							min(start_index + batch_size ,   len(lowerCAmelCase__ ) )
									a :						Any      =							encoded_texts[start_index:end_index]
									a :						List[Any]      =							attn_masks[start_index:end_index]

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

									a :						Any      =							encoded_batch

									with torch.no_grad():
										a :						Optional[int]      =							model(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).logits

									a :						List[Any]      =							out_logits[..., :-1, :].contiguous()
									a :						int      =							labels[..., 1:].contiguous()
									a :						Union[str, Any]      =							attn_mask[..., 1:].contiguous()

									a :						Dict      =							torch.expa(
									    (loss_fct(shift_logits.transpose(1 ,   2 ) ,   lowerCAmelCase__ ) * shift_attention_mask_batch).sum(1 )
									    / shift_attention_mask_batch.sum(1 ) )

									ppls += perplexity_batch.tolist()

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


