




'''simple docstring'''







import sacrebleu as scb
from packaging import version

import datasets


_lowerCamelCase							:						Dict								=  '\\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							:						Optional[int]								=  '\\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							:						str								=  '\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      lowerCamelCase_							(       datasets.Metric							):




					'''simple docstring'''






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase="exp",  __UpperCamelCase=None,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=None,  __UpperCamelCase=False,  ):
							_snake_case	       =       len(references[0] )
							if any(len(__UpperCamelCase ) != references_per_prediction for refs in references ):
									raise ValueError("Sacrebleu requires the same number of references for each prediction" )
							_snake_case	       =       [[refs[i] for refs in references] for i in range(__UpperCamelCase )]
							_snake_case	       =       scb.corpus_bleu(
							    __UpperCamelCase,  __UpperCamelCase,  smooth_method=__UpperCamelCase,  smooth_value=__UpperCamelCase,  force=__UpperCamelCase,  lowercase=__UpperCamelCase,  use_effective_order=__UpperCamelCase,  **({"tokenize": tokenize} if tokenize else {}),  )
							_snake_case	       =       {
							    "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










"""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     (			snake_case ,					unittest.TestCase    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    str		    =   PreTrainedTokenizerFast
	SCREAMING_SNAKE_CASE_				:    Optional[Any]		    =   False
	SCREAMING_SNAKE_CASE_				:    Optional[int]		    =   True
	SCREAMING_SNAKE_CASE_				:    int		    =   "tokenizer_file"






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[int]			=					False  # because we don't have pretrained_vocab_files_map
			super().setUp()
			_lowercase							:      Tuple			=					True

			_lowercase							:      Optional[Any]			=					["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
			_lowercase							:      Dict			=					"""SaulLu/dummy-tokenizer-bytelevel-bpe"""

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

			_lowercase							:      Any			=					PreTrainedTokenizerFast.from_pretrained(model_paths[0]					)
			tokenizer.save_pretrained(self.tmpdirname					)






	def 					lowerCamelCase__ (			self					):
			# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
			# model
			pass






	def 					lowerCamelCase__ (			self					):
			# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
			# model
			pass






	def 					lowerCamelCase__ (			self					):
			# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
			# model
			pass






	def 					lowerCamelCase__ (			self					):
			# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
			pass






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[str]			=					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:
									_lowercase							:      Optional[Any]			=					tempfile.mkdtemp()
									_lowercase							:      Any			=					self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_							,**UpperCAmelCase_					)

									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					)
									_lowercase							:      List[Any]			=					tmpdirname_orig






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[str]			=					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:
									_lowercase							:      Optional[int]			=					tempfile.mkdtemp()
									_lowercase							:      Tuple			=					self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_							,**UpperCAmelCase_					)

									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					)
									_lowercase							:      Optional[int]			=					tmpdirname_orig






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[Any]			=					self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name					)

			_lowercase							:      List[str]			=					("""a""" for _ in range(10_00					))
			_lowercase							:      Dict			=					tokenizer.train_new_from_iterator(text_iterator=UpperCAmelCase_							,length=10_00							,vocab_size=50					)

			_lowercase							:      List[str]			=					new_tokenizer.encode("""a🤗"""					)
			self.assertEqual(UpperCAmelCase_							,[64, 1_72, 2_53, 97, 2_45]					)







@require_tokenizers
class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[Any]			=					AutoTokenizer.from_pretrained("""bert-base-cased"""					)
			_lowercase							:      Optional[Any]			=					json.loads(tokenizer._tokenizer.to_str()					)
			_lowercase							:      Dict			=					len(UpperCAmelCase_					)

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

					# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
					_lowercase							:      int			=					AutoTokenizer.from_pretrained(UpperCAmelCase_					)
					self.assertEqual(len(UpperCAmelCase_					)							,len(UpperCAmelCase_					) + 1					)
					_lowercase							:      List[str]			=					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(UpperCAmelCase_							,"""tokenizer.4.0.0.json"""					)							,os.path.join(UpperCAmelCase_							,"""tokenizer.42.0.0.json"""					)					)
					_lowercase							:      Optional[int]			=					["""tokenizer.42.0.0.json"""]
					tokenizer.save_pretrained(UpperCAmelCase_					)
					_lowercase							:      List[Any]			=					AutoTokenizer.from_pretrained(UpperCAmelCase_					)
					self.assertEqual(len(UpperCAmelCase_					)							,len(UpperCAmelCase_					)					)
					_lowercase							:      List[Any]			=					json.loads(new_tokenizer._tokenizer.to_str()					)
					self.assertNotIn("""huggingface"""							,json_tokenizer["""model"""]["""vocab"""]					)






	def 					lowerCamelCase__ (			self					):
			# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
			_lowercase							:      Tuple			=					"""hf-internal-testing/test-two-tokenizers"""

			# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
			_lowercase							:      List[Any]			=					AutoTokenizer.from_pretrained(UpperCAmelCase_					)
			self.assertEqual(len(UpperCAmelCase_					)							,2_89_97					)
			_lowercase							:      List[Any]			=					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

			_lowercase							:      Optional[Any]			=					"""3.0.0"""
			_lowercase							:      int			=					old_transformers.models.auto.AutoTokenizer.from_pretrained(UpperCAmelCase_					)
			self.assertEqual(len(UpperCAmelCase_					)							,2_89_96					)
			_lowercase							:      str			=					json.loads(old_tokenizer._tokenizer.to_str()					)
			self.assertNotIn("""huggingface"""							,json_tokenizer["""model"""]["""vocab"""]					)







@require_tokenizers
class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	def 					lowerCamelCase__ (			self					):
			# See https://github.com/huggingface/transformers/pull/12550
			# and https://github.com/huggingface/tokenizers/issues/537
			_lowercase							:      Optional[int]			=					PreTrainedTokenizerFast.from_pretrained("""robot-test/dummy-tokenizer-wordlevel"""					)
			_lowercase							:      List[str]			=					"""The Matrix is a 1999 science fiction action film."""

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






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_					):
			return tokenizer.encode(UpperCAmelCase_							,truncation="""longest_first"""							,padding="""longest"""					)



