
"""simple docstring"""





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_a								=						logging.get_logger(__name__)

_a								=						"""▁"""

_a								=						{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

_a								=						{
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

_a								=						{
    """facebook/s2t-small-librispeech-asr""": 1024,
}

_a								=						["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

_a								=						{"""mustc""": MUSTC_LANGS}






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = VOCAB_FILES_NAMES
		lowercase__         = PRETRAINED_VOCAB_FILES_MAP
		lowercase__         = MAX_MODEL_INPUT_SIZES
		lowercase__         = ['input_ids', 'attention_mask']

		lowercase__         = []



		def __init__(   self							,						__a							,						__a							,						__a="<s>"							,						__a="</s>"							,						__a="<pad>"							,						__a="<unk>"							,						__a=False							,						__a=False							,						__a=None							,						__a=None							,						__a = None							,						**__a							,						)			->			None:






							'''simple docstring'''
							_UpperCamelCase									=    {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__a							,						eos_token=__a							,						unk_token=__a							,						pad_token=__a							,						do_upper_case=__a							,						do_lower_case=__a							,						tgt_lang=__a							,						lang_codes=__a							,						sp_model_kwargs=self.sp_model_kwargs							,						**__a							,						)
							_UpperCamelCase									=    do_upper_case
							_UpperCamelCase									=    do_lower_case

							_UpperCamelCase									=    load_json(__a)
							_UpperCamelCase									=    {v: k for k, v in self.encoder.items()}
							_UpperCamelCase									=    spm_file
							_UpperCamelCase									=    load_spm(__a							,						self.sp_model_kwargs)

							if lang_codes is not None:
												_UpperCamelCase									=    lang_codes
												_UpperCamelCase									=    LANGUAGES[lang_codes]
												_UpperCamelCase									=    [F'''<lang:{lang}>''' for lang in self.langs]
												_UpperCamelCase									=    {lang: self.sp_model.PieceToId(F'''<lang:{lang}>''') for lang in self.langs}

												_UpperCamelCase									=    self.lang_tokens
												_UpperCamelCase									=    tgt_lang if tgt_lang is not None else self.langs[0]

												self.set_tgt_lang_special_tokens(self._tgt_lang)
							else:
												_UpperCamelCase									=    {}



		@property
		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return len(self.encoder)



		@property
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							return self._tgt_lang



		@tgt_lang.setter
		def 		UpperCAmelCase				(   self							,						__a)			->			None:






							'''simple docstring'''
							_UpperCamelCase									=    new_tgt_lang
							self.set_tgt_lang_special_tokens(__a)



		def 		UpperCAmelCase				(   self							,						__a)			->			None:






							'''simple docstring'''
							_UpperCamelCase									=    self.lang_code_to_id[tgt_lang]
							_UpperCamelCase									=    [lang_code_id]



		def 		UpperCAmelCase				(   self							,						__a)			->			List[str]:






							'''simple docstring'''
							return self.sp_model.encode(__a							,						out_type=__a)



		def 		UpperCAmelCase				(   self							,						__a)			->			Any:






							'''simple docstring'''
							return self.encoder.get(__a							,						self.encoder[self.unk_token])



		def 		UpperCAmelCase				(   self							,						__a)			->			str:






							'''simple docstring'''
							return self.decoder.get(__a							,						self.unk_token)



		def 		UpperCAmelCase				(   self							,						__a)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    []
							_UpperCamelCase									=    ''''''
							for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																	_UpperCamelCase									=    self.sp_model.decode(__a)
																	out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																	_UpperCamelCase									=    []
												else:
																	current_sub_tokens.append(__a)
							_UpperCamelCase									=    self.sp_model.decode(__a)
							out_string += decoded.upper() if self.do_upper_case else decoded
							return out_string.strip()



		def 		UpperCAmelCase				(   self							,						__a							,						__a=None)			->			List[int]:






							'''simple docstring'''
							if token_ids_a is None:
												return self.prefix_tokens + token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]



		def 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = False)			->			List[int]:






							'''simple docstring'''

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

							_UpperCamelCase									=    [1] * len(self.prefix_tokens)
							_UpperCamelCase									=    [1]
							if token_ids_a is None:
												return prefix_ones + ([0] * len(__a)) + suffix_ones
							return prefix_ones + ([0] * len(__a)) + ([0] * len(__a)) + suffix_ones



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.encoder.copy()
							vocab.update(self.added_tokens_encoder)
							return vocab



		def __getstate__(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.__dict__.copy()
							_UpperCamelCase									=    None
							return state



		def __setstate__(   self							,						__a)			->			None:






							'''simple docstring'''
							_UpperCamelCase									=    d

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

							_UpperCamelCase									=    load_spm(self.spm_file							,						self.sp_model_kwargs)




		def 		UpperCAmelCase				(   self							,						__a							,						__a = None)			->			Tuple[str]:






							'''simple docstring'''
							_UpperCamelCase									=    Path(__a)
							assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
							_UpperCamelCase									=    save_dir / (
							    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
							)
							_UpperCamelCase									=    save_dir / (
							    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
							)

							save_json(self.encoder							,						__a)

							if os.path.abspath(self.spm_file) != os.path.abspath(__a) and os.path.isfile(self.spm_file):
												copyfile(self.spm_file							,						__a)
							elif not os.path.isfile(self.spm_file):
												with open(__a							,						'''wb''') as fi:
																	_UpperCamelCase									=    self.sp_model.serialized_model_proto()
																	fi.write(__a)

							return (str(__a), str(__a))





def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						sentencepiece.SentencePieceProcessor:


					"""simple docstring"""





					_UpperCamelCase									=    sentencepiece.SentencePieceProcessor(**__snake_case      )
					spm.Load(str(__snake_case      )      )
					return spm



def  lowerCamelCase__						(     __snake_case      )				->						Union[Dict, List]:


					"""simple docstring"""





					with open(__snake_case,	'''r'''      ) as f:
										return json.load(__snake_case      )



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						None:


					"""simple docstring"""





					with open(__snake_case,	'''w'''      ) as f:
										json.dump(__snake_case,	__snake_case,	indent=2      )










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   )}

