

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

import datasets
from datasets import logging


__a    :int      =			'\\n\n'

__a    :Any      =			'\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'

__a    :List[str]      =			'\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   _a					(			datasets.Metric				):




      """simple docstring"""

      def 		__A	(   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 		__A	(   self							:				str						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				int = 16						,			UpperCAmelCase							:				bool = True						,			UpperCAmelCase							:				List[Any]=None	):
            if device is not None:
                  assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
                  if device == "gpu":
                        A_						  =				"cuda"
            else:
                  A_						  =				"cuda" if torch.cuda.is_available() else "cpu"

            A_						  =				AutoModelForCausalLM.from_pretrained(UpperCAmelCase	)
            A_						  =				model.to(UpperCAmelCase	)

            A_						  =				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:
                  A_						  =				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"
                  A_						  =				model.config.max_length - 1
            else:
                  A_						  =				model.config.max_length

            A_						  =				tokenizer(
                UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			padding=UpperCAmelCase						,			truncation=UpperCAmelCase						,			max_length=UpperCAmelCase						,			return_tensors="pt"						,			return_attention_mask=UpperCAmelCase						,			).to(UpperCAmelCase	)

            A_						  =				encodings["input_ids"]
            A_						  =				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_						  =				[]
            A_						  =				CrossEntropyLoss(reduction="none"	)

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

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

                  A_						  =				encoded_batch

                  with torch.no_grad():
                        A_						  =				model(UpperCAmelCase						,			attention_mask=UpperCAmelCase	).logits

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

                  A_						  =				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	)}
"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase       :			List[str]   	=logging.get_logger(__name__)

__lowerCAmelCase       :			str   	={"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCAmelCase       :			Dict   	={
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

__lowerCAmelCase       :			Tuple   	={
    """moussaKam/mbarthez""": 1_0_2_4,
    """moussaKam/barthez""": 1_0_2_4,
    """moussaKam/barthez-orangesum-title""": 1_0_2_4,
}

__lowerCAmelCase       :			Optional[int]   	="""▁"""





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Tuple				    =      VOCAB_FILES_NAMES
      snake_case__     :  Optional[Any]				    =      PRETRAINED_VOCAB_FILES_MAP
      snake_case__     :  Dict				    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__     :  Optional[Any]				    =      ['input_ids', 'attention_mask']

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase="<s>"						,			__lowerCAmelCase="</s>"						,			__lowerCAmelCase="</s>"						,			__lowerCAmelCase="<s>"						,			__lowerCAmelCase="<unk>"						,			__lowerCAmelCase="<pad>"						,			__lowerCAmelCase="<mask>"						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      AddedToken(__lowerCAmelCase						,			lstrip=__lowerCAmelCase						,			rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else mask_token

            lowercase								=      {} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=__lowerCAmelCase						,			eos_token=__lowerCAmelCase						,			unk_token=__lowerCAmelCase						,			sep_token=__lowerCAmelCase						,			cls_token=__lowerCAmelCase						,			pad_token=__lowerCAmelCase						,			mask_token=__lowerCAmelCase						,			sp_model_kwargs=self.sp_model_kwargs						,			**__lowerCAmelCase						,			)

            lowercase								=      vocab_file
            lowercase								=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(str(__lowerCAmelCase		)		)

            lowercase								=      {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

            lowercase								=      len(self.sp_model		) - 1
            lowercase								=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            lowercase								=      [self.cls_token_id]
            lowercase								=      [self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = False		):







            """simple docstring"""



            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=__lowerCAmelCase						,			token_ids_a=__lowerCAmelCase						,			already_has_special_tokens=__lowerCAmelCase		)

            if token_ids_a is None:
                  return [1] + ([0] * len(__lowerCAmelCase		)) + [1]
            return [1] + ([0] * len(__lowerCAmelCase		)) + [1, 1] + ([0] * len(__lowerCAmelCase		)) + [1]

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      [self.sep_token_id]
            lowercase								=      [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]

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return len(self.sp_model		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      {self.convert_ids_to_tokens(__lowerCAmelCase		): i for i in range(self.vocab_size		)}
            vocab.update(self.added_tokens_encoder		)
            return vocab

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.sp_model.encode(__lowerCAmelCase						,			out_type=__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            lowercase								=      self.sp_model.PieceToId(__lowerCAmelCase		)

            return spm_id if spm_id else self.unk_token_id

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      []
            lowercase								=      """"""
            lowercase								=      False
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        if not prev_is_special:
                              out_string += " "
                        out_string += self.sp_model.decode(__lowerCAmelCase		) + token
                        lowercase								=      True
                        lowercase								=      []
                  else:
                        current_sub_tokens.append(__lowerCAmelCase		)
                        lowercase								=      False
            out_string += self.sp_model.decode(__lowerCAmelCase		)
            return out_string.strip()

      def __getstate__(		self		):







            """simple docstring"""



            lowercase								=      self.__dict__.copy()
            lowercase								=      None
            return state

      def __setstate__(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      d

            # for backward compatibility
            if not hasattr(self						,			"""sp_model_kwargs"""		):
                  lowercase								=      {}

            lowercase								=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(self.vocab_file		)







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if not os.path.isdir(__lowerCAmelCase		):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
                  return
            lowercase								=      os.path.join(
                __lowerCAmelCase						,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

            if os.path.abspath(self.vocab_file		) != os.path.abspath(__lowerCAmelCase		) and os.path.isfile(self.vocab_file		):
                  copyfile(self.vocab_file						,			__lowerCAmelCase		)
            elif not os.path.isfile(self.vocab_file		):
                  with open(__lowerCAmelCase						,			"""wb"""		) as fi:
                        lowercase								=      self.sp_model.serialized_model_proto()
                        fi.write(__lowerCAmelCase		)

            return (out_vocab_file,)



