
from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowercase        =  '''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

lowercase        =  '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

lowercase        =  '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''





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

					def    lowercase__      (	self					:     Tuple   ):
												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"""   ),
												        }   )   ,					)



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[List[List[str]]]   ,					__UpperCamelCase					:     List[List[str]]   ,					__UpperCamelCase					:     int = 1   ,					__UpperCamelCase					:     int = 4   ,					):
												return {
												    "google_bleu": gleu_score.corpus_gleu(
												        list_of_references=__UpperCamelCase   ,					hypotheses=__UpperCamelCase   ,					min_len=__UpperCamelCase   ,					max_len=__UpperCamelCase   )
												}







import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
		import jieba

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


lowercase        =  logging.get_logger(__name__)

lowercase        =  {'''vocab_file''': '''vocab.txt'''}

lowercase        =  {
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

lowercase        =  {
    '''openbmb/cpm-ant-10b''': 1_0_2_4,
}



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]       )  ->    Dict:
							lowerCamelCase_							  =					collections.OrderedDict()
							with open(UpperCAmelCase__			,				"""r"""			,				encoding="""utf-8"""       ) as reader:
														lowerCamelCase_							  =					reader.readlines()
							for index, token in enumerate(UpperCAmelCase__       ):
														lowerCamelCase_							  =					token.rstrip("""\n"""       )
														lowerCamelCase_							  =					index
							return vocab





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[str]="<unk>"   ,					__UpperCamelCase					:     List[str]=2_0_0   ):
												lowerCamelCase_							  =					vocab
												lowerCamelCase_							  =					unk_token
												lowerCamelCase_							  =					max_input_chars_per_word



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												if len(__UpperCamelCase   ) > self.max_input_chars_per_word:
																			return [self.unk_token]

												lowerCamelCase_							  =					0
												lowerCamelCase_							  =					[]
												while start < len(__UpperCamelCase   ):
																			lowerCamelCase_							  =					len(__UpperCamelCase   )
																			lowerCamelCase_							  =					None
																			while start < end:
																										lowerCamelCase_							  =					"""""".join(chars[start:end]   )
																										if substr in self.vocab:
																																	lowerCamelCase_							  =					substr
																																	break
																										end -= 1
																			if cur_substr is None:
																										sub_tokens.append(self.unk_token   )
																										start += 1
																			else:
																										sub_tokens.append(__UpperCamelCase   )
																										lowerCamelCase_							  =					end

												return sub_tokens





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE			    =    False

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]="<d>"   ,					__UpperCamelCase					:     List[str]="</d>"   ,					__UpperCamelCase					:     Optional[Any]="<s>"   ,					__UpperCamelCase					:     Union[str, Any]="</s>"   ,					__UpperCamelCase					:     List[str]="<pad>"   ,					__UpperCamelCase					:     Union[str, Any]="<unk>"   ,					__UpperCamelCase					:     List[Any]="</n>"   ,					__UpperCamelCase					:     Tuple="</_>"   ,					__UpperCamelCase					:     Optional[Any]="left"   ,					**__UpperCamelCase					:     List[str]   ,					):
												requires_backends(self   ,					["""jieba"""]   )
												super().__init__(
												    bod_token=__UpperCamelCase   ,					eod_token=__UpperCamelCase   ,					bos_token=__UpperCamelCase   ,					eos_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					line_token=__UpperCamelCase   ,					space_token=__UpperCamelCase   ,					padding_side=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												lowerCamelCase_							  =					bod_token
												lowerCamelCase_							  =					eod_token
												lowerCamelCase_							  =					load_vocab(__UpperCamelCase   )
												lowerCamelCase_							  =					self.encoder[space_token]
												lowerCamelCase_							  =					self.encoder[line_token]

												del self.encoder[space_token]
												del self.encoder[line_token]

												lowerCamelCase_							  =					collections.OrderedDict(sorted(self.encoder.items()   ,					key=lambda __UpperCamelCase   : x[1]   )   )
												lowerCamelCase_							  =					{v: k for k, v in self.encoder.items()}

												lowerCamelCase_							  =					WordpieceTokenizer(vocab=self.encoder   ,					unk_token=self.unk_token   )

					@property
					def    lowercase__      (	self					:     Any   ):
												return self.encoder[self.bod_token]

					@property
					def    lowercase__      (	self					:     Any   ):
												return self.encoder[self.eod_token]

					@property
					def    lowercase__      (	self					:     int   ):
												return self.encoder["\n"]

					@property
					def    lowercase__      (	self					:     str   ):
												return len(self.encoder   )

					def    lowercase__      (	self					:     Any   ):
												return dict(self.encoder   ,					**self.added_tokens_encoder   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					[]
												for x in jieba.cut(__UpperCamelCase   ,					cut_all=__UpperCamelCase   ):
																			output_tokens.extend(self.wordpiece_tokenizer.tokenize(__UpperCamelCase   )   )
												return output_tokens

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Dict   ,					**__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					[i for i in token_ids if i >= 0]
												lowerCamelCase_							  =					[
												    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
												]
												return super()._decode(__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ):
												return token in self.encoder

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[str]   ):
												return "".join(__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     int   ):
												return self.encoder.get(__UpperCamelCase   ,					self.encoder.get(self.unk_token   )   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Union[str, Any]   ):
												return self.decoder.get(__UpperCamelCase   ,					self.unk_token   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												if os.path.isdir(__UpperCamelCase   ):
																			lowerCamelCase_							  =					os.path.join(
																			    __UpperCamelCase   ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
												else:
																			lowerCamelCase_							  =					(filename_prefix + """-""" if filename_prefix else """""") + save_directory
												lowerCamelCase_							  =					0
												if " " in self.encoder:
																			lowerCamelCase_							  =					self.encoder[""" """]
																			del self.encoder[" "]
												if "\n" in self.encoder:
																			lowerCamelCase_							  =					self.encoder["""\n"""]
																			del self.encoder["\n"]
												lowerCamelCase_							  =					collections.OrderedDict(sorted(self.encoder.items()   ,					key=lambda __UpperCamelCase   : x[1]   )   )
												with open(__UpperCamelCase   ,					"""w"""   ,					encoding="""utf-8"""   ) as writer:
																			for token, token_index in self.encoder.items():
																										if index != token_index:
																																	logger.warning(
																																	    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																																	    """ Please check that the vocabulary is not corrupted!"""   )
																																	lowerCamelCase_							  =					token_index
																										writer.write(token + """\n"""   )
																										index += 1
												return (vocab_file,)

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     List[int] = None   ):
												if token_ids_a is None:
																			return [self.bos_token_id] + token_ids_a
												return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ,					__UpperCamelCase					:     bool = False   ):

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

												if token_ids_a is not None:
																			return [1] + ([0] * len(__UpperCamelCase   )) + [1] + ([0] * len(__UpperCamelCase   ))
												return [1] + ([0] * len(__UpperCamelCase   ))





