

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			:						Dict  					=     '\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			:						Optional[Any]  					=     '\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      lowerCAmelCase					(     datasets.Metric					):


       '''simple docstring'''







       def 	lowerCamelCase__    (						self       :	str  ) ->			Optional[int]:







        '''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 	lowerCamelCase__    (						self       :	str    ,						__snake_case       :	List[str]    ,						__snake_case       :	Any    ,						__snake_case       :	Union[str, Any] = 16    ,						__snake_case       :	Any = True    ,						__snake_case       :	str=None  ) ->			int:







        '''simple docstring'''

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

        lowerCamelCase       =     AutoModelForCausalLM.from_pretrained(__snake_case  )
        lowerCamelCase       =     model.to(__snake_case  )

        lowerCamelCase       =     AutoTokenizer.from_pretrained(__snake_case  )

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

        lowerCamelCase       =     tokenizer(
            __snake_case    ,						add_special_tokens=__snake_case    ,						padding=__snake_case    ,						truncation=__snake_case    ,						max_length=__snake_case    ,						return_tensors='pt'    ,						return_attention_mask=__snake_case    ,						).to(__snake_case  )

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

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

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

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

         lowerCamelCase       =     encoded_batch

         with torch.no_grad():
          lowerCamelCase       =     model(__snake_case    ,						attention_mask=__snake_case  ).logits

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

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

         ppls += perplexity_batch.tolist()

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










"""simple docstring"""






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class lowerCAmelCase__      (	tf.keras.layers.Layer		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    ):
							super().__init__()
							_lowerCamelCase	:     Optional[Any]       =       pad_token_id
							_lowerCamelCase	:     Dict       =       max_length
							_lowerCamelCase	:     Tuple       =       vocab
							_lowerCamelCase	:     List[str]       =       merges
							_lowerCamelCase	:     int       =       BytePairTokenizer(lowercase    , lowercase    , sequence_length=lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     str       =       [' '.join(lowercase    ) for m in tokenizer.bpe_ranks.keys()]
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.get_vocab()
							return cls(lowercase    , lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       GPTaTokenizer.from_pretrained(lowercase    , *lowercase    , **lowercase    )
							return cls.from_tokenizer(lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    ):
							return cls(**lowercase    )
			def    A_       (    self    ):
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}






			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     int       =       self.tf_tokenizer(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tf.ones_like(lowercase    )

							if self.pad_token_id is not None:
											# pad the tokens up to max length
											_lowerCamelCase	:     str       =       max_length if max_length is not None else self.max_length

											if max_length is not None:
															_lowerCamelCase,					_lowerCamelCase	:     Union[str, Any]       =       pad_model_inputs(
															    lowercase    , max_seq_length=lowercase    , pad_value=self.pad_token_id    )

							return {"attention_mask": attention_mask, "input_ids": input_ids}