




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
   from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__				:      Tuple	   = get_tests_dir("fixtures/test_sentencepiece.model")

lowercase__				:      Dict	   = {"target_lang": "fi", "source_lang": "en"}
lowercase__				:      List[str]	   = ">>zh<<"
lowercase__				:      int	   = "Helsinki-NLP/"

if is_torch_available():
   lowercase__				:      str	   = "pt"
elif is_tf_available():
   lowercase__				:      Tuple	   = "tf"
else:
   lowercase__				:      str	   = "jax"




@require_sentencepiece
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      Optional[int]		 =     MarianTokenizer
     a		:      str		 =     False
     a		:      List[str]		 =     True







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          super().setUp()
          a     =				["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
          a     =				dict(zip(A  ,		range(len(A			)			)			)			)
          a     =				Path(self.tmpdirname			)
          save_json(A  ,		save_dir / VOCAB_FILES_NAMES["vocab"]			)
          save_json(A  ,		save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]			)
          if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
               copyfile(A  ,		save_dir / VOCAB_FILES_NAMES["source_spm"]			)
               copyfile(A  ,		save_dir / VOCAB_FILES_NAMES["target_spm"]			)

          a     =				MarianTokenizer.from_pretrained(self.tmpdirname			)
          tokenizer.save_pretrained(self.tmpdirname			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   MarianTokenizer:



          '''simple docstring'''



          return MarianTokenizer.from_pretrained(self.tmpdirname  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return (
              "This is a test",
              "This is a test",
          )







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				"</s>"
          a     =				0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(A			)  ,		A			)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				list(self.get_tokenizer().get_vocab().keys()			)

          self.assertEqual(vocab_keys[0]  ,		"</s>"			)
          self.assertEqual(vocab_keys[1]  ,		"<unk>"			)
          self.assertEqual(vocab_keys[-1]  ,		"<pad>"			)
          self.assertEqual(len(A			)  ,		9			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          self.assertEqual(self.get_tokenizer().vocab_size  ,		9			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''			)
          a     =				en_de_tokenizer(["I am a small frog"]  ,		return_tensors=A			)
          self.assertIsInstance(A  ,		A			)
          a     =				[38, 121, 14, 697, 38848, 0]
          self.assertListEqual(A  ,		batch.input_ids[0]			)

          a     =				tempfile.mkdtemp()
          en_de_tokenizer.save_pretrained(A			)
          a     =				[x.name for x in Path(A			).glob("*"			)]
          self.assertIn("source.spm"  ,		A			)
          MarianTokenizer.from_pretrained(A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.get_tokenizer()

          a     =				tok(
              ["I am a small frog" * 1000, "I am a small frog"]  ,		padding=A  ,		truncation=A  ,		return_tensors=A			)
          self.assertIsInstance(A  ,		A			)
          self.assertEqual(batch.input_ids.shape  ,		(2, 512)			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a     =				tok(["I am a tiny frog", "I am a small frog"]  ,		padding=A  ,		return_tensors=A			)
          self.assertIsInstance(A  ,		A			)
          self.assertEqual(batch_smaller.input_ids.shape  ,		(2, 10)			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				{"input_ids": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=A  ,		model_name="Helsinki-NLP/opus-mt-en-de"  ,		revision="1a8c2263da11e68e50938f97e10cd57820bd504c"  ,		decode_kwargs={"use_source_tokenizer": True}  ,		)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"			)

          a     =				"Tämä on testi"
          a     =				"This is a test"

          a     =				[76, 7, 2047, 2]
          a     =				[69, 12, 11, 940, 2]

          a     =				tokenizer(A			).input_ids
          self.assertListEqual(A  ,		A			)

          a     =				tokenizer(text_target=A			).input_ids
          self.assertListEqual(A  ,		A			)

          a     =				tokenizer.decode(A  ,		skip_special_tokens=A			)
          self.assertEqual(A  ,		A			)







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase__				:      Optional[int]	   = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
   from nltk import word_tokenize


lowercase__				:      int	   = "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowercase__				:      str	   = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowercase__				:      Tuple	   = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"




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







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          return datasets.MetricInfo(
              description=_DESCRIPTION  ,		citation=_CITATION  ,		inputs_description=_KWARGS_DESCRIPTION  ,		features=datasets.Features(
                  {
                      "predictions": datasets.Value("string"  ,		id="sequence"			),
                      "references": datasets.Value("string"  ,		id="sequence"			),
                  }			)  ,		codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]  ,		reference_urls=[
                  "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
                  "https://en.wikipedia.org/wiki/METEOR",
              ]  ,		)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          import nltk

          nltk.download("wordnet"			)
          if NLTK_VERSION >= version.Version("3.6.5"			):
               nltk.download("punkt"			)
          if NLTK_VERSION >= version.Version("3.6.6"			):
               nltk.download("omw-1.4"			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A=0.9  ,		A=3  ,		A=0.5			)  ->   Tuple:



          '''simple docstring'''



          if NLTK_VERSION >= version.Version("3.6.5"			):
               a     =				[
                   meteor_score.single_meteor_score(
                       word_tokenize(A			)  ,		word_tokenize(A			)  ,		alpha=A  ,		beta=A  ,		gamma=A			)
                   for ref, pred in zip(A  ,		A			)
               ]
          else:
               a     =				[
                   meteor_score.single_meteor_score(A  ,		A  ,		alpha=A  ,		beta=A  ,		gamma=A			)
                   for ref, pred in zip(A  ,		A			)
               ]

          return {"meteor": np.mean(A			)}

