





'''simple docstring'''

import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class  UpperCAmelCase			( UpperCamelCase__    , unittest.TestCase       ):
 __lowercase     	=       PreTrainedTokenizerFast
 __lowercase     	=       False
 __lowercase     	=       True
 __lowercase     	=       """tokenizer_file"""
 def   UpperCAmelCase_							(       self   :Dict							)->							Optional[int]:
     A__       =    False  # because we don't have pretrained_vocab_files_map
     super().setUp()
     A__       =    True

     A__       =    ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
     A__       =    "SaulLu/dummy-tokenizer-bytelevel-bpe"

     # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
     A__       =    [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

     A__       =    PreTrainedTokenizerFast.from_pretrained(model_paths[0]							)
     tokenizer.save_pretrained(self.tmpdirname							)
 def   UpperCAmelCase_							(       self   :Dict							)->							List[Any]:
     # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
     # model
     pass
 def   UpperCAmelCase_							(       self   :Dict							)->							Tuple:
     # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
     # model
     pass
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Dict:
     # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
     # model
     pass
 def   UpperCAmelCase_							(       self   :Any							)->							Any:
     # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
     pass
 def   UpperCAmelCase_							(       self   :int							)->							Dict:
     A__       =    self.tmpdirname
     # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
             try:
                 A__       =    tempfile.mkdtemp()
                 A__       =    self.rust_tokenizer_class.from_pretrained(lowercase_		, **lowercase_							)

                 tokenizer.save_pretrained(self.tmpdirname							)
                 super().test_training_new_tokenizer()
             finally:
                 # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                 # is restored
                 shutil.rmtree(self.tmpdirname							)
                 A__       =    tmpdirname_orig
 def   UpperCAmelCase_							(       self   :Dict							)->							Tuple:
     A__       =    self.tmpdirname
     # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
             try:
                 A__       =    tempfile.mkdtemp()
                 A__       =    self.rust_tokenizer_class.from_pretrained(lowercase_		, **lowercase_							)

                 tokenizer.save_pretrained(self.tmpdirname							)
                 super().test_training_new_tokenizer_with_special_tokens_change()
             finally:
                 # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                 # is restored
                 shutil.rmtree(self.tmpdirname							)
                 A__       =    tmpdirname_orig



 def   UpperCAmelCase_							(       self   :str							)->							Union[str, Any]:
     A__       =    self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name							)

     A__       =    ("a" for _ in range(10_00							))
     A__       =    tokenizer.train_new_from_iterator(text_iterator=lowercase_		, length=10_00		, vocab_size=50							)

     A__       =    new_tokenizer.encode("a🤗"							)
     self.assertEqual(lowercase_		, [64, 1_72, 2_53, 97, 2_45]							)






@require_tokenizers
class  UpperCAmelCase			( unittest.TestCase       ):
 def   UpperCAmelCase_							(       self   :List[Any]							)->							List[str]:
     A__       =    AutoTokenizer.from_pretrained("bert-base-cased"							)
     A__       =    json.loads(tokenizer._tokenizer.to_str()							)
     A__       =    len(lowercase_							)

     with tempfile.TemporaryDirectory() as tmp_dir:
         # Hack to save this in the tokenizer_config.json
         A__       =    ["tokenizer.4.0.0.json"]
         tokenizer.save_pretrained(lowercase_							)
         json.dump(lowercase_		, open(os.path.join(lowercase_		, "tokenizer.4.0.0.json"							)		, "w"							)							)

         # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
         A__       =    AutoTokenizer.from_pretrained(lowercase_							)
         self.assertEqual(len(lowercase_							)		, len(lowercase_							) + 1							)
         A__       =    json.loads(new_tokenizer._tokenizer.to_str()							)
         self.assertIn("huggingface"		, json_tokenizer["model"]["vocab"]							)

         # Will need to be adjusted if we reach v42 and this test is still here.
         # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
         shutil.move(os.path.join(lowercase_		, "tokenizer.4.0.0.json"							)		, os.path.join(lowercase_		, "tokenizer.42.0.0.json"							)							)
         A__       =    ["tokenizer.42.0.0.json"]
         tokenizer.save_pretrained(lowercase_							)
         A__       =    AutoTokenizer.from_pretrained(lowercase_							)
         self.assertEqual(len(lowercase_							)		, len(lowercase_							)							)
         A__       =    json.loads(new_tokenizer._tokenizer.to_str()							)
         self.assertNotIn("huggingface"		, json_tokenizer["model"]["vocab"]							)



 def   UpperCAmelCase_							(       self   :int							)->							List[Any]:
     # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
     A__       =    "hf-internal-testing/test-two-tokenizers"

     # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
     A__       =    AutoTokenizer.from_pretrained(lowercase_							)
     self.assertEqual(len(lowercase_							)		, 2_89_97							)
     A__       =    json.loads(tokenizer._tokenizer.to_str()							)
     self.assertIn("huggingface"		, json_tokenizer["model"]["vocab"]							)

     # Testing an older version by monkey-patching the version in the module it's used.
     import transformers as old_transformers

     A__       =    "3.0.0"
     A__       =    old_transformers.models.auto.AutoTokenizer.from_pretrained(lowercase_							)
     self.assertEqual(len(lowercase_							)		, 2_89_96							)
     A__       =    json.loads(old_tokenizer._tokenizer.to_str()							)
     self.assertNotIn("huggingface"		, json_tokenizer["model"]["vocab"]							)






@require_tokenizers
class  UpperCAmelCase			( unittest.TestCase       ):
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							str:
     # See https://github.com/huggingface/transformers/pull/12550
     # and https://github.com/huggingface/tokenizers/issues/537
     A__       =    PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"							)
     A__       =    "The Matrix is a 1999 science fiction action film."

     with concurrent.futures.ThreadPoolExecutor() as executor:
         A__       =    [executor.submit(self.fetch		, lowercase_		, lowercase_							) for i in range(10							)]
         A__       =    [future.result() for future in futures]
         self.assertEqual(lowercase_		, [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10							)]							)



 def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :str		, lowercase_   :Union[str, Any]							)->							Tuple:
     return tokenizer.encode(lowercase_		, truncation="longest_first"		, padding="longest"							)










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


__lowerCAmelCase							:			Tuple				="\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCAmelCase							:			Dict				="\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

__lowerCAmelCase							:			Dict				="\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    , _KWARGS_DESCRIPTION       )
class      UpperCAmelCase			( datasets.Metric       ):
 def   UpperCAmelCase_							(       self   :int							)->							Union[str, Any]:
     if version.parse(scb.__version__							) < version.parse("1.4.12"							):
         raise ImportWarning(
             "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
             "You can install it with `pip install \"sacrebleu>=1.4.12\"`."							)
     return datasets.MetricInfo(
         description=_DESCRIPTION		, citation=_CITATION		, homepage="https://github.com/mjpost/sacreBLEU"		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
             {
                 "predictions": datasets.Value("string"		, id="sequence"							),
                 "references": datasets.Sequence(datasets.Value("string"		, id="sequence"							)		, id="references"							),
             }							)		, codebase_urls=["https://github.com/mjpost/sacreBLEU"]		, reference_urls=[
             "https://github.com/mjpost/sacreBLEU",
             "https://en.wikipedia.org/wiki/BLEU",
             "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
         ]		, )



 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]		, lowercase_   :Optional[Any]		, lowercase_   :List[str]="exp"		, lowercase_   :str=None		, lowercase_   :Tuple=False		, lowercase_   :Any=False		, lowercase_   :Optional[Any]=None		, lowercase_   :Any=False		, )->							Tuple:
     A__       =    len(references[0]							)
     if any(len(lowercase_							) != references_per_prediction for refs in references							):
         raise ValueError("Sacrebleu requires the same number of references for each prediction"							)
     A__       =    [[refs[i] for refs in references] for i in range(lowercase_							)]
     A__       =    scb.corpus_bleu(
         lowercase_		, lowercase_		, smooth_method=lowercase_		, smooth_value=lowercase_		, force=lowercase_		, lowercase=lowercase_		, use_effective_order=lowercase_		, **({"tokenize": tokenize} if tokenize else {})		, )
     A__       =    {
         "score": output.score,
         "counts": output.counts,
         "totals": output.totals,
         "precisions": output.precisions,
         "bp": output.bp,
         "sys_len": output.sys_len,
         "ref_len": output.ref_len,
     }
     return output_dict



