

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__a    :Tuple      =			logging.get_logger(__name__)

__a    :Tuple      =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__a    :Any      =			{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

__a    :Union[str, Any]      =			{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Tuple      		=       VOCAB_FILES_NAMES
      _lowerCamelCase       :			int      		=       PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase       :			int      		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase       :			Optional[Any]      		=       ['input_ids', 'attention_mask']
      _lowerCamelCase       :			Tuple      		=       RobertaTokenizer





      def __init__(   self							:				int						,			UpperCAmelCase							:				Union[str, Any]=None						,			UpperCAmelCase							:				Union[str, Any]=None						,			UpperCAmelCase							:				Any=None						,			UpperCAmelCase							:				Optional[Any]="replace"						,			UpperCAmelCase							:				Optional[int]="<s>"						,			UpperCAmelCase							:				Tuple="</s>"						,			UpperCAmelCase							:				Tuple="</s>"						,			UpperCAmelCase							:				Optional[Any]="<s>"						,			UpperCAmelCase							:				Optional[int]="<unk>"						,			UpperCAmelCase							:				Any="<pad>"						,			UpperCAmelCase							:				Union[str, Any]="<mask>"						,			UpperCAmelCase							:				Optional[Any]=False						,			UpperCAmelCase							:				Optional[int]=True						,			**UpperCAmelCase							:				Tuple						,			):
            super().__init__(
                UpperCAmelCase						,			UpperCAmelCase						,			tokenizer_file=UpperCAmelCase						,			errors=UpperCAmelCase						,			bos_token=UpperCAmelCase						,			eos_token=UpperCAmelCase						,			sep_token=UpperCAmelCase						,			cls_token=UpperCAmelCase						,			unk_token=UpperCAmelCase						,			pad_token=UpperCAmelCase						,			mask_token=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase						,			**UpperCAmelCase						,			)

            A_						  =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
            if pre_tok_state.get("add_prefix_space"						,			UpperCAmelCase	) != add_prefix_space:
                  A_						  =				getattr(UpperCAmelCase						,			pre_tok_state.pop("type"	)	)
                  A_						  =				add_prefix_space
                  A_						  =				pre_tok_class(**UpperCAmelCase	)

            A_						  =				add_prefix_space

            A_						  =				"post_processor"
            A_						  =				getattr(self.backend_tokenizer						,			UpperCAmelCase						,			UpperCAmelCase	)
            if tokenizer_component_instance:
                  A_						  =				json.loads(tokenizer_component_instance.__getstate__()	)

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                        A_						  =				tuple(state["sep"]	)
                  if "cls" in state:
                        A_						  =				tuple(state["cls"]	)

                  A_						  =				False

                  if state.get("add_prefix_space"						,			UpperCAmelCase	) != add_prefix_space:
                        A_						  =				add_prefix_space
                        A_						  =				True

                  if state.get("trim_offsets"						,			UpperCAmelCase	) != trim_offsets:
                        A_						  =				trim_offsets
                        A_						  =				True

                  if changes_to_apply:
                        A_						  =				getattr(UpperCAmelCase						,			state.pop("type"	)	)
                        A_						  =				component_class(**UpperCAmelCase	)
                        setattr(self.backend_tokenizer						,			UpperCAmelCase						,			UpperCAmelCase	)





      @property
      def 		__A	(   self							:				Tuple	):
            if self._mask_token is None:
                  if self.verbose:
                        logger.error("Using mask_token, but it is not set yet."	)
                  return None
            return str(self._mask_token	)





      @mask_token.setter
      def 		__A	(   self							:				List[Any]						,			UpperCAmelCase							:				str	):
            A_						  =				AddedToken(UpperCAmelCase						,			lstrip=UpperCAmelCase						,			rstrip=UpperCAmelCase	) if isinstance(UpperCAmelCase						,			UpperCAmelCase	) else value
            A_						  =				value





      def 		__A	(   self							:				Tuple						,			*UpperCAmelCase							:				Tuple						,			**UpperCAmelCase							:				List[str]	):
            A_						  =				kwargs.get("is_split_into_words"						,			UpperCAmelCase	)
            assert self.add_prefix_space or not is_split_into_words, (
                f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*UpperCAmelCase						,			**UpperCAmelCase	)





      def 		__A	(   self							:				Tuple						,			*UpperCAmelCase							:				Dict						,			**UpperCAmelCase							:				str	):
            A_						  =				kwargs.get("is_split_into_words"						,			UpperCAmelCase	)

            assert self.add_prefix_space or not is_split_into_words, (
                f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*UpperCAmelCase						,			**UpperCAmelCase	)





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				Optional[str] = None	):
            A_						  =				self._tokenizer.model.save(UpperCAmelCase						,			name=UpperCAmelCase	)
            return tuple(UpperCAmelCase	)





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				Union[str, Any]=None	):
            A_						  =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                  return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







      def 		__A	(   self							:				Optional[int]						,			UpperCAmelCase							:				List[int]						,			UpperCAmelCase							:				Optional[List[int]] = None	):
            A_						  =				[self.sep_token_id]
            A_						  =				[self.cls_token_id]

            if token_ids_a is None:
                  return len(cls + token_ids_a + sep	) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]



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

import datasets
from datasets import logging


lowerCamelCase__							:						Any							     =   """\

"""

lowerCamelCase__							:						List[str]							     =   """
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
"""

lowerCamelCase__							:						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 		__magic_name__						(datasets.Metric				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:int     ,		_a:List[Any]     ,		_a:int = 16     ,		_a:bool = True     ,		_a:Any=None   ):
       if device is not None:
         assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
         if device == "gpu":
           snake_case__						= '''cuda'''
       else:
         snake_case__						= '''cuda''' if torch.cuda.is_available() else '''cpu'''

       snake_case__						= AutoModelForCausalLM.from_pretrained(_a   )
       snake_case__						= model.to(_a   )

       snake_case__						= AutoTokenizer.from_pretrained(_a   )

       # 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:
         snake_case__						= list(tokenizer.special_tokens_map_extended.values()   )
         # check that the model already has at least one special token defined
         assert (
             len(_a   ) > 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"
         snake_case__						= model.config.max_length - 1
       else:
         snake_case__						= model.config.max_length

       snake_case__						= tokenizer(
           _a     ,		add_special_tokens=_a     ,		padding=_a     ,		truncation=_a     ,		max_length=_a     ,		return_tensors='''pt'''     ,		return_attention_mask=_a     ,		).to(_a   )

       snake_case__						= encodings['''input_ids''']
       snake_case__						= 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."

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

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

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

         snake_case__						= encoded_batch

         with torch.no_grad():
           snake_case__						= model(_a     ,		attention_mask=_a   ).logits

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

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

         ppls += perplexity_batch.tolist()

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

