






import datasets

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


A_		:		Optional[int]		     =			'\\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_		:		str		     =			'\\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_		:		Union[str, Any]		     =			'\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     _lowerCAmelCase(	datasets.Metric ):





       """simple docstring"""







       def       _a   ( self							):
         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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=4  ,      _lowerCamelCase=False							):
         UpperCamelCase_: int   				=    compute_bleu(
             reference_corpus=_lowerCamelCase  ,      translation_corpus=_lowerCamelCase  ,      max_order=_lowerCamelCase  ,      smooth=_lowerCamelCase							)
         ((UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)): int   				=    score
         return {
             "bleu": bleu,
             "precisions": precisions,
             "brevity_penalty": bp,
             "length_ratio": ratio,
             "translation_length": translation_length,
             "reference_length": reference_length,
         }







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[Any]									=CLIPTokenizer
       a    :		List[Any]									=CLIPTokenizerFast
       a    :		str									=True
       a    :		List[Any]									={}
       a    :		str									=False





       def       _a   ( self							):
         super().setUp()

         # fmt: off
         UpperCamelCase_: str   				=    ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
         # fmt: on
         UpperCamelCase_: Any   				=    dict(zip(_lowerCamelCase  ,      range(len(_lowerCamelCase							)							)							)							)
         UpperCamelCase_: Optional[int]   				=    ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
         UpperCamelCase_: Any   				=    {'unk_token': '<unk>'}

         UpperCamelCase_: List[Any]   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['vocab_file']							)
         UpperCamelCase_: Optional[int]   				=    os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES['merges_file']							)
         with open(self.vocab_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write(json.dumps(_lowerCamelCase							) + '\n'							)
         with open(self.merges_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write('\n'.join(_lowerCamelCase							)							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return CLIPTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    'lower newer'
         UpperCamelCase_: Tuple   				=    'lower newer'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    CLIPTokenizer(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map							)
         UpperCamelCase_: Optional[int]   				=    'lower newer'
         UpperCamelCase_: Any   				=    ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
         UpperCamelCase_: Union[str, Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       @require_ftfy
       def       _a   ( self							):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: Optional[int]   				=    self.tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)
             UpperCamelCase_: List[str]   				=    self.rust_tokenizer_class.from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)

             UpperCamelCase_: Optional[int]   				=    'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
             UpperCamelCase_: Tuple   				=    tokenizer_s.tokenize(_lowerCamelCase							)
             UpperCamelCase_: Any   				=    tokenizer_r.tokenize(_lowerCamelCase							)

             self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
             # with Tilde) encoded in 2 different ways
             UpperCamelCase_: List[str]   				=    'xa\u0303y' + ' ' + 'x\xe3y'
             UpperCamelCase_: List[Any]   				=    tokenizer_s.tokenize(_lowerCamelCase							)
             UpperCamelCase_: int   				=    tokenizer_r.tokenize(_lowerCamelCase							)

             self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on unicode of space type
             UpperCamelCase_: Dict   				=    [
                 '\u0009',  # (horizontal tab, '\t')
                 '\u000B',  # (vertical tab)
                 '\u000C',  # (form feed)
                 '\u0020',  # (space, ' ')
                 '\u200E',  # (left-to-right mark):w
                 '\u200F',  # (right-to-left mark)
             ]
             for unicode_seq in spaces_unicodes:
               UpperCamelCase_: int   				=    tokenizer_s.tokenize(_lowerCamelCase							)
               UpperCamelCase_: Dict   				=    tokenizer_r.tokenize(_lowerCamelCase							)

               self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

             # Test that the tokenization is identical on unicode of line break type
             UpperCamelCase_: List[str]   				=    [
                 '\u000A',  # (line feed, '\n')
                 '\r\n',  # (carriage return and line feed, '\r\n')
                 '\u000D',  # (carriage return, '\r')
                 '\r',  # (carriage return, '\r')
                 '\u000D',  # (carriage return, '\r')
                 '\u2028',  # (line separator)
                 '\u2029',  # (paragraph separator)
                 # "\u0085", # (next line)
             ]

             # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
             # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
             # space (and thus into an empty list).

             for unicode_seq in line_break_unicodes:
               UpperCamelCase_: Optional[Any]   				=    tokenizer_s.tokenize(_lowerCamelCase							)
               UpperCamelCase_: str   				=    tokenizer_r.tokenize(_lowerCamelCase							)

               self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             UpperCamelCase_: str   				=    'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
             UpperCamelCase_: str   				=    f'''{text_of_1_token} {text_of_1_token}'''

             UpperCamelCase_: Optional[Any]   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      )
             UpperCamelCase_: int   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (0, len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (len(_lowerCamelCase							) + 1, len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )

             UpperCamelCase_: List[str]   				=    f''' {text}'''

             UpperCamelCase_: str   				=    self.rust_tokenizer_class.from_pretrained(
                 _lowerCamelCase  ,      use_fast=_lowerCamelCase  ,      )
             UpperCamelCase_: Dict   				=    tokenizer_r(_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase							)
             self.assertEqual(encoding.offset_mapping[0]  ,      (1, 1 + len(_lowerCamelCase							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]  ,      (1 + len(_lowerCamelCase							) + 1, 1 + len(_lowerCamelCase							) + 1 + len(_lowerCamelCase							))  ,      )





       def       _a   ( self							):
         # Test related to the breaking change introduced in transformers v4.17.0
         # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
         with self.assertRaises(_lowerCamelCase							) as context:
           self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer'							)

         self.assertTrue(
             context.exception.args[0].startswith(
                 'The `backend_tokenizer` provided does not match the expected format.'							)							)





       @require_ftfy
       def       _a   ( self							):
         super().test_tokenization_python_rust_equals()





       def       _a   ( self							):
         # CLIP always lower cases letters
         pass