





import json
import os
from typing import Dict, List, Optional, Tuple

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


__UpperCAmelCase							=   logging.get_logger(__name__)


__UpperCAmelCase							=   {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

__UpperCAmelCase							=   {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

__UpperCAmelCase							=   '</w>'
__UpperCAmelCase							=   '@@ '
def       __UpperCamelCase					(      lowercase__						:							List[Any]		)						->   Optional[Any]:
    '''simple docstring'''





    lowerCAmelCase_       :						Tuple							= set()
    lowerCAmelCase_       :						Any							= word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        lowerCAmelCase_       :						Tuple							= char
    return pairs


# Speech2Text2 has no max input length
__UpperCAmelCase							=   {'facebook/s2t-wav2vec2-large-en-de': 10_24}

class 		__a				(						lowerCamelCase__   ):
       __snake_case    : int   =		VOCAB_FILES_NAMES
       __snake_case    : int   =		PRETRAINED_VOCAB_FILES_MAP
       __snake_case    : List[str]   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __snake_case    : List[Any]   =		["""input_ids""", """attention_mask"""]



       def __init__(  self						: Union[str, Any]       ,      UpperCAmelCase						: Dict       ,      UpperCAmelCase						: Tuple="<s>"       ,      UpperCAmelCase						: Dict="<pad>"       ,      UpperCAmelCase						: int="</s>"       ,      UpperCAmelCase						: Tuple="<unk>"       ,      UpperCAmelCase						: Optional[int]=False       ,      UpperCAmelCase						: Optional[Any]=None       ,      **UpperCAmelCase						: str       ,      ):
           super().__init__(
               unk_token=a__       ,      bos_token=a__       ,      eos_token=a__       ,      pad_token=a__       ,      do_lower_case=a__       ,      **a__       ,      )

           lowerCAmelCase_       :						List[str]							= do_lower_case

           with open(a__       ,      encoding="""utf-8"""      ) as vocab_handle:
               lowerCAmelCase_       :						Optional[Any]							= json.load(a__      )
           lowerCAmelCase_       :						int							= {v: k for k, v in self.encoder.items()}

           if merges_file is None:
               logger.info(F'No merges files provided. {self.__class__.__name__} can only be used for decoding.'      )

               lowerCAmelCase_       :						Optional[int]							= None
               lowerCAmelCase_       :						Optional[Any]							= None
           else:
               with open(a__       ,      encoding="""utf-8"""      ) as merges_handle:
                   lowerCAmelCase_       :						int							= merges_handle.read().split("""\n"""      )[:-1]

               lowerCAmelCase_       :						str							= [tuple(merge.split()[:2]      ) for merge in merges]
               lowerCAmelCase_       :						str							= dict(zip(a__       ,      range(len(a__      )      )      )      )
               lowerCAmelCase_       :						Optional[int]							= {}



       @property
       def 			A				(  self						: Dict      ):
           return len(self.decoder      )



       def 			A				(  self						: int      ):
           return dict(self.encoder       ,      **self.added_tokens_encoder      )



       def 			A				(  self						: str       ,      UpperCAmelCase						: int      ):
           lowerCAmelCase_       :						int							= tuple(token[:-1]      ) + (token[-1] + BPE_TOKEN_MERGES,)
           if token in self.cache:
               return self.cache[token]
           lowerCAmelCase_       :						Union[str, Any]							= get_pairs(a__      )

           if not pairs:
               return token

           while True:
               lowerCAmelCase_       :						List[str]							= min(a__       ,      key=lambda UpperCAmelCase      : self.bpe_ranks.get(a__       ,      float("""inf"""      )      )      )
               if bigram not in self.bpe_ranks:
                   break
               lowerCAmelCase_       :						List[str]							= bigram
               lowerCAmelCase_       :						Tuple							= []
               lowerCAmelCase_       :						Union[str, Any]							= 0
               while i < len(a__      ):
                   try:
                       lowerCAmelCase_       :						Any							= word.index(a__       ,      a__      )
                   except ValueError:
                       new_word.extend(word[i:]      )
                       break
                   else:
                       new_word.extend(word[i:j]      )
                       lowerCAmelCase_       :						List[Any]							= j

                   if word[i] == first and i < len(a__      ) - 1 and word[i + 1] == second:
                       new_word.append(first + second      )
                       i += 2
                   else:
                       new_word.append(word[i]      )
                       i += 1
               lowerCAmelCase_       :						List[Any]							= tuple(a__      )
               lowerCAmelCase_       :						List[str]							= new_word
               if len(a__      ) == 1:
                   break
               else:
                   lowerCAmelCase_       :						str							= get_pairs(a__      )
           lowerCAmelCase_       :						Tuple							= ''' '''.join(a__      )
           if word == "\n  " + BPE_TOKEN_MERGES:
               lowerCAmelCase_       :						int							= '''\n''' + BPE_TOKEN_MERGES

           if word.endswith(a__      ):
               lowerCAmelCase_       :						int							= word.replace(a__       ,      """"""      )

           lowerCAmelCase_       :						Tuple							= word.replace(""" """       ,      a__      )
           lowerCAmelCase_       :						List[Any]							= word
           return word



       def 			A				(  self						: List[str]       ,      UpperCAmelCase						: Any      ):





           if self.bpe_ranks is None:
               raise ValueError(
                   """This tokenizer was instantiated without a `merges.txt` file, so"""
                   """ that it can only be used for decoding, not for encoding."""
                   """Make sure to provide `merges.txt` file at instantiation to enable """
                   """encoding."""      )

           if self.do_lower_case:
               lowerCAmelCase_       :						Any							= text.lower()

           lowerCAmelCase_       :						Union[str, Any]							= text.split()

           lowerCAmelCase_       :						Union[str, Any]							= []
           for token in text:
               if token:
                   split_tokens.extend(list(self.bpe(a__      ).split(""" """      )      )      )

           return split_tokens



       def 			A				(  self						: List[str]       ,      UpperCAmelCase						: List[str]      ):
           return self.encoder.get(a__       ,      self.encoder.get(self.unk_token      )      )



       def 			A				(  self						: Any       ,      UpperCAmelCase						: Dict      ):
           lowerCAmelCase_       :						Any							= self.decoder.get(a__       ,      self.unk_token      )
           return result



       def 			A				(  self						: Dict       ,      UpperCAmelCase						: int      ):
           lowerCAmelCase_       :						Any							= ''' '''.join(a__      )

           # make sure @@ tokens are concatenated
           lowerCAmelCase_       :						Union[str, Any]							= ''''''.join(string.split(a__      )      )

           return string



       def 			A				(  self						: Tuple       ,      UpperCAmelCase						: int       ,      UpperCAmelCase						: str = None      ):





           if not os.path.isdir(a__      ):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
               return
           lowerCAmelCase_       :						int							= os.path.join(
               a__       ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )
           lowerCAmelCase_       :						str							= os.path.join(
               a__       ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]      )

           with open(a__       ,      """w"""       ,      encoding="""utf-8"""      ) as f:
               f.write(json.dumps(self.encoder       ,      indent=2       ,      sort_keys=a__       ,      ensure_ascii=a__      ) + """\n"""      )

           lowerCAmelCase_       :						Union[str, Any]							= 0
           if self.bpe_ranks is None:
               return (vocab_file,)

           with open(a__       ,      """w"""       ,      encoding="""utf-8"""      ) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,      key=lambda UpperCAmelCase      : kv[1]      ):
                   if index != token_index:
                       logger.warning(
                           F'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
                           """ Please check that the tokenizer is not corrupted!"""      )
                       lowerCAmelCase_       :						Union[str, Any]							= token_index
                   writer.write(""" """.join(a__      ) + """\n"""      )
                   index += 1

           return (vocab_file, merges_file)









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

import datasets
from datasets import logging


_lowerCamelCase      							=   '\\n\n'

_lowerCamelCase      							=   '\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      							=   '\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 					__snake_case      (		self):



             """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 					__snake_case      (		self   ,				a__   ,				a__   ,				a__ = 16   ,				a__ = True   ,				a__=None):



             """simple docstring"""





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

             _lowerCamelCase				:  List[str]						=		AutoModelForCausalLM.from_pretrained(a__)
             _lowerCamelCase				:  Union[str, Any]						=		model.to(a__)

             _lowerCamelCase				:  Tuple						=		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:
                    _lowerCamelCase				:  Any						=		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"
                    _lowerCamelCase				:  Optional[int]						=		model.config.max_length - 1
             else:
                    _lowerCamelCase				:  Optional[int]						=		model.config.max_length

             _lowerCamelCase				:  Union[str, Any]						=		tokenizer(
                 a__   ,				add_special_tokens=a__   ,				padding=a__   ,				truncation=a__   ,				max_length=a__   ,				return_tensors='''pt'''   ,				return_attention_mask=a__   ,				).to(a__)

             _lowerCamelCase				:  Any						=		encodings['''input_ids''']
             _lowerCamelCase				:  Dict						=		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				:  Dict						=		[]
             _lowerCamelCase				:  Union[str, Any]						=		CrossEntropyLoss(reduction='''none''')

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

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

                    _lowerCamelCase				:  Dict						=		encoded_batch

                    with torch.no_grad():
                           _lowerCamelCase				:  List[str]						=		model(a__   ,				attention_mask=a__).logits

                    _lowerCamelCase				:  List[str]						=		out_logits[..., :-1, :].contiguous()
                    _lowerCamelCase				:  Dict						=		labels[..., 1:].contiguous()
                    _lowerCamelCase				:  List[Any]						=		attn_mask[..., 1:].contiguous()

                    _lowerCamelCase				:  str						=		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__)}

