


import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


A   :					Tuple	=      logging.get_logger(__name__)


A   :					Tuple	=      {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
A   :					int	=      {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

A   :					Union[str, Any]	=      {
    "allenai/led-base-16384": 16384,
}

@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       str					=						(
          list(range(ord("!"		)					,      ord("~"		) + 1		)		) + list(range(ord("¡"		)					,      ord("¬"		) + 1		)		) + list(range(ord("®"		)					,      ord("ÿ"		) + 1		)		)
      )
      lowerCamelCase__	:       Any					=						bs[:]
      lowerCamelCase__	:       Union[str, Any]					=						0
      for b in range(2**8		):
            if b not in bs:
                  bs.append(_A		)
                  cs.append(2**8 + n		)
                  n += 1
      lowerCamelCase__	:       Any					=						[chr(_A		) for n in cs]
      return dict(zip(_A					,      _A		)		)

def    lowercase_	(	_A    :					Any		):

      """simple docstring"""




      lowerCamelCase__	:       Union[str, Any]					=						set()
      lowerCamelCase__	:       Optional[int]					=						word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)		)
            lowerCamelCase__	:       Any					=						char
      return pairs







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    VOCAB_FILES_NAMES
       A__      		=    PRETRAINED_VOCAB_FILES_MAP
       A__      		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A__      		=    ["input_ids", "attention_mask"]





       def __init__(     self							:    str      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Union[str, Any]="replace"      ,   __lowerCamelCase							:    Optional[Any]="<s>"      ,   __lowerCamelCase							:    int="</s>"      ,   __lowerCamelCase							:    str="</s>"      ,   __lowerCamelCase							:    List[str]="<s>"      ,   __lowerCamelCase							:    Optional[int]="<unk>"      ,   __lowerCamelCase							:    List[str]="<pad>"      ,   __lowerCamelCase							:    Union[str, Any]="<mask>"      ,   __lowerCamelCase							:    Tuple=False      ,   **__lowerCamelCase							:    Optional[Any]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase      ,   __lowerCamelCase ) else bos_token
             lowerCamelCase__	:       Optional[int]					=						AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase      ,   __lowerCamelCase ) else eos_token
             lowerCamelCase__	:       str					=						AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase      ,   __lowerCamelCase ) else sep_token
             lowerCamelCase__	:       int					=						AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase      ,   __lowerCamelCase ) else cls_token
             lowerCamelCase__	:       Optional[Any]					=						AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase      ,   __lowerCamelCase ) else unk_token
             lowerCamelCase__	:       Tuple					=						AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase      ,   __lowerCamelCase ) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             lowerCamelCase__	:       int					=						AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase      ,   __lowerCamelCase ) else mask_token

             super().__init__(
                 errors=__lowerCamelCase      ,   bos_token=__lowerCamelCase      ,   eos_token=__lowerCamelCase      ,   unk_token=__lowerCamelCase      ,   sep_token=__lowerCamelCase      ,   cls_token=__lowerCamelCase      ,   pad_token=__lowerCamelCase      ,   mask_token=__lowerCamelCase      ,   add_prefix_space=__lowerCamelCase      ,   **__lowerCamelCase      ,   )

             with open(__lowerCamelCase      ,   encoding="utf-8" ) as vocab_handle:
                   lowerCamelCase__	:       Union[str, Any]					=						json.load(__lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						{v: k for k, v in self.encoder.items()}
             lowerCamelCase__	:       Union[str, Any]					=						errors  # how to handle errors in decoding
             lowerCamelCase__	:       List[Any]					=						bytes_to_unicode()
             lowerCamelCase__	:       Optional[Any]					=						{v: k for k, v in self.byte_encoder.items()}
             with open(__lowerCamelCase      ,   encoding="utf-8" ) as merges_handle:
                   lowerCamelCase__	:       List[Any]					=						merges_handle.read().split("\n" )[1:-1]
             lowerCamelCase__	:       str					=						[tuple(merge.split() ) for merge in bpe_merges]
             lowerCamelCase__	:       Optional[Any]					=						dict(zip(__lowerCamelCase      ,   range(len(__lowerCamelCase ) ) ) )
             lowerCamelCase__	:       List[Any]					=						{}
             lowerCamelCase__	:       Dict					=						add_prefix_space

             # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
             lowerCamelCase__	:       List[str]					=						re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )





       @property
       # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return len(self.encoder )





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return dict(self.encoder      ,   **self.added_tokens_encoder )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Dict ):


             '''simple docstring'''



             if token in self.cache:
                   return self.cache[token]
             lowerCamelCase__	:       Union[str, Any]					=						tuple(__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						get_pairs(__lowerCamelCase )

             if not pairs:
                   return token

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

                         if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
                               new_word.append(first + second )
                               i += 2
                         else:
                               new_word.append(word[i] )
                               i += 1
                   lowerCamelCase__	:       Dict					=						tuple(__lowerCamelCase )
                   lowerCamelCase__	:       str					=						new_word
                   if len(__lowerCamelCase ) == 1:
                         break
                   else:
                         lowerCamelCase__	:       List[str]					=						get_pairs(__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						" ".join(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						word
             return word





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						[]
             for token in re.findall(self.pat      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       Union[str, Any]					=						"".join(
                       self.byte_encoder[b] for b in token.encode("utf-8" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                   bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase ).split(" " ) )
             return bpe_tokens





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             return self.encoder.get(__lowerCamelCase      ,   self.encoder.get(self.unk_token ) )





       def 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             return self.decoder.get(__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						"".join(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"      ,   errors=self.errors )
             return text





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Optional[str] = None ):


             '''simple docstring'''



             if not os.path.isdir(__lowerCamelCase ):
                   logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
                   return
             lowerCamelCase__	:       List[Any]					=						os.path.join(
                 __lowerCamelCase      ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
             lowerCamelCase__	:       Union[str, Any]					=						os.path.join(
                 __lowerCamelCase      ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

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

             lowerCamelCase__	:       Tuple					=						0
             with open(__lowerCamelCase      ,   "w"      ,   encoding="utf-8" ) as writer:
                   writer.write("#version: 0.2\n" )
                   for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,   key=lambda __lowerCamelCase : kv[1] ):
                         if index != token_index:
                               logger.warning(
                                   f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                                   " Please check that the tokenizer is not corrupted!" )
                               lowerCamelCase__	:       List[Any]					=						token_index
                         writer.write(" ".join(__lowerCamelCase ) + "\n" )
                         index += 1

             return vocab_file, merge_file





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = None ):


             '''simple docstring'''



             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             lowerCamelCase__	:       List[str]					=						[self.cls_token_id]
             lowerCamelCase__	:       int					=						[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = None      ,   __lowerCamelCase							:    bool = 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 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    List[int]      ,   __lowerCamelCase							:    Optional[List[int]] = None ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						[self.sep_token_id]
             lowerCamelCase__	:       Union[str, Any]					=						[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]





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Dict=False      ,   **__lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						kwargs.pop("add_prefix_space"      ,   self.add_prefix_space )
             if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()):
                   lowerCamelCase__	:       Dict					=						" " + text
             return (text, kwargs)





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Union[Dict[str, EncodedInput], BatchEncoding]      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    Optional[bool] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						super()._pad(
                 encoded_inputs=__lowerCamelCase      ,   max_length=__lowerCamelCase      ,   padding_strategy=__lowerCamelCase      ,   pad_to_multiple_of=__lowerCamelCase      ,   return_attention_mask=__lowerCamelCase      ,   )

             # Load from model defaults
             if return_attention_mask is None:
                   lowerCamelCase__	:       str					=						"attention_mask" in self.model_input_names

             if return_attention_mask and "global_attention_mask" in encoded_inputs:
                   lowerCamelCase__	:       Optional[int]					=						encoded_inputs[self.model_input_names[0]]
                   # `global_attention_mask` need to have the same length as other (sequential) inputs.
                   lowerCamelCase__	:       Union[str, Any]					=						len(encoded_inputs["global_attention_mask"] ) != len(__lowerCamelCase )

                   if needs_to_be_padded:
                         lowerCamelCase__	:       Dict					=						len(__lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )

                         if self.padding_side == "right":
                               # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                               lowerCamelCase__	:       Optional[int]					=						(
                                   encoded_inputs["global_attention_mask"] + [-1] * difference
                               )
                         elif self.padding_side == "left":
                               lowerCamelCase__	:       Union[str, Any]					=						[-1] * difference + encoded_inputs[
                                   "global_attention_mask"
                               ]
                         else:
                               raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

             return encoded_inputs






import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


A   :					Tuple	=      "\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

A   :					Optional[int]	=      "\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

A   :					str	=      "\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,						_KWARGS_DESCRIPTION)
class     _lowercase				(							datasets.Metric):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return datasets.MetricInfo(
                 description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
                     {
                         "predictions": datasets.Sequence(datasets.Value("string"      ,   id="token" )      ,   id="sequence" ),
                         "references": datasets.Sequence(
                             datasets.Sequence(datasets.Value("string"      ,   id="token" )      ,   id="sequence" )      ,   id="references" ),
                     } )      ,   codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"]      ,   reference_urls=[
                     "https://en.wikipedia.org/wiki/BLEU",
                     "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
                 ]      ,   )





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Optional[Any]=4      ,   __lowerCamelCase							:    Dict=False ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						compute_bleu(
                 reference_corpus=__lowerCamelCase      ,   translation_corpus=__lowerCamelCase      ,   max_order=__lowerCamelCase      ,   smooth=__lowerCamelCase )
             ((lowerCamelCase__)					,     (lowerCamelCase__)					,     (lowerCamelCase__)					,     (lowerCamelCase__)					,     (lowerCamelCase__)					,     (lowerCamelCase__))	:       List[str]					=						score
             return {
                 "bleu": bleu,
                 "precisions": precisions,
                 "brevity_penalty": bp,
                 "length_ratio": ratio,
                 "translation_length": translation_length,
                 "reference_length": reference_length,
             }


