

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

import regex as re

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


__A			:					List[Any]						=			logging.get_logger(__name__)


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

__A			:					Optional[int]						=			{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

__A			:					Any						=			{'facebook/blenderbot_small-90M': 5_1_2}





def 	__a     (      A__			:  Dict       ):
    SCREAMING_SNAKE_CASE										=   set()
    SCREAMING_SNAKE_CASE										=   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        SCREAMING_SNAKE_CASE										=   char

    SCREAMING_SNAKE_CASE										=   set(A__       )
    return pairs





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




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





   def __init__(       self					:			Dict     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			int     , __lowerCamelCase					:			str="__start__"     , __lowerCamelCase					:			List[str]="__end__"     , __lowerCamelCase					:			Any="__unk__"     , __lowerCamelCase					:			Optional[Any]="__null__"     , **__lowerCamelCase					:			Any     , ):
       super().__init__(unk_token=__lowerCamelCase     , bos_token=__lowerCamelCase     , eos_token=__lowerCamelCase     , pad_token=__lowerCamelCase     , **__lowerCamelCase			)

       with open(__lowerCamelCase     , encoding="utf-8"			) as vocab_handle:
           SCREAMING_SNAKE_CASE										=   json.load(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   {v: k for k, v in self.encoder.items()}
       with open(__lowerCamelCase     , encoding="utf-8"			) as merges_handle:
           SCREAMING_SNAKE_CASE										=   merges_handle.read().split("\n"			)[1:-1]
       SCREAMING_SNAKE_CASE										=   [tuple(merge.split()			) for merge in merges]
       SCREAMING_SNAKE_CASE										=   dict(zip(__lowerCamelCase     , range(len(__lowerCamelCase			)			)			)			)
       SCREAMING_SNAKE_CASE										=   {}





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return len(self.encoder			)





   def      _snake_case		(       self					:			Dict			):
       return dict(self.encoder     , **self.added_tokens_encoder			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			str			):
       if token in self.cache:
           return self.cache[token]
       SCREAMING_SNAKE_CASE										=   re.sub("([.,!?()])"     , r" \1"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   re.sub("(')"     , r" \1 "     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   re.sub(r"\s{2,}"     , " "     , __lowerCamelCase			)
       if "\n" in token:
           SCREAMING_SNAKE_CASE										=   token.replace("\n"     , " __newln__"			)

       SCREAMING_SNAKE_CASE										=   token.split(" "			)
       SCREAMING_SNAKE_CASE										=   []
       for token in tokens:
           if not len(__lowerCamelCase			):
               continue

           SCREAMING_SNAKE_CASE										=   token.lower()
           SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   tuple(list(word[:-1]			) + [word[-1] + "</w>"]			)
           SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)

           if not pairs:
               words.append(__lowerCamelCase			)
               continue

           while True:
               SCREAMING_SNAKE_CASE										=   min(__lowerCamelCase     , key=lambda __lowerCamelCase			: self.bpe_ranks.get(__lowerCamelCase     , float("inf"			)			)			)
               if bigram not in self.bpe_ranks:
                   break
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   bigram
               SCREAMING_SNAKE_CASE										=   []
               SCREAMING_SNAKE_CASE										=   0

               while i < len(__lowerCamelCase			):
                   try:
                       SCREAMING_SNAKE_CASE										=   word.index(__lowerCamelCase     , __lowerCamelCase			)
                       new_word.extend(word[i:j]			)
                       SCREAMING_SNAKE_CASE										=   j
                   except ValueError:
                       new_word.extend(word[i:]			)
                       break

                   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
               SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
               SCREAMING_SNAKE_CASE										=   new_word
               if len(__lowerCamelCase			) == 1:
                   break
               else:
                   SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   "@@ ".join(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   word[:-4]

           SCREAMING_SNAKE_CASE										=   word
           words.append(__lowerCamelCase			)
       return " ".join(__lowerCamelCase			)





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   []

       SCREAMING_SNAKE_CASE										=   re.findall(r"\S+\n?"     , __lowerCamelCase			)

       for token in words:
           split_tokens.extend(list(self.bpe(__lowerCamelCase			).split(" "			)			)			)
       return split_tokens





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   token.lower()
       return self.encoder.get(__lowerCamelCase     , self.encoder.get(self.unk_token			)			)





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			int			):
       return self.decoder.get(__lowerCamelCase     , self.unk_token			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   " ".join(__lowerCamelCase			).replace("@@ "     , ""			).strip()
       return out_string





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       if not os.path.isdir(__lowerCamelCase			):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
           return
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
       SCREAMING_SNAKE_CASE										=   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"			)

       SCREAMING_SNAKE_CASE										=   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!"			)
                   SCREAMING_SNAKE_CASE										=   token_index
               writer.write(" ".join(__lowerCamelCase			) + "\n"			)
               index += 1

       return vocab_file, merge_file



import comet  # From: unbabel-comet
import torch

import datasets


SCREAMING_SNAKE_CASE							    =							datasets.logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							'\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

SCREAMING_SNAKE_CASE							    =							'\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

SCREAMING_SNAKE_CASE							    =							'\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'


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


     """simple docstring"""


     def 	snake_case_  (	self    ):
        return datasets.MetricInfo(
            description=_DESCRIPTION  ,       citation=_CITATION  ,       homepage="""https://unbabel.github.io/COMET/html/index.html"""  ,       inputs_description=_KWARGS_DESCRIPTION  ,       features=datasets.Features(
                {
                    """sources""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                    """predictions""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                    """references""": datasets.Value("""string"""  ,       id="""sequence"""    ),
                }    )  ,       codebase_urls=["""https://github.com/Unbabel/COMET"""]  ,       reference_urls=[
                """https://github.com/Unbabel/COMET""",
                """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
                """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
            ]  ,       )


     def 	snake_case_  (	self  ,       __A    ):
        if self.config_name == "default":
           __a					=  comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""    )    )
        else:
           __a					=  comet.load_from_checkpoint(comet.download_model(self.config_name    )    )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A=None  ,       __A=False    ):
        if gpus is None:
           __a					=  1 if torch.cuda.is_available() else 0
        __a					=  {"""src""": sources, """mt""": predictions, """ref""": references}
        __a					=  [dict(zip(__A  ,       __A    )    ) for t in zip(*data.values()    )]
        __a	,    __a					=  self.scorer.predict(__A  ,       gpus=__A  ,       progress_bar=__A    )
        return {"mean_score": mean_score, "scores": scores}



