




"""simple docstring"""




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
   lowercase__				:      Any	   = """pt"""
elif is_tf_available():
   lowercase__				:      List[str]	   = """tf"""
else:
   lowercase__				:      int	   = """jax"""




class       a__	( __A       , unittest.TestCase				):
     a		:      Optional[Any]		 =     PerceiverTokenizer
     a		:      int		 =     False







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          super().setUp()
          a     =				PerceiverTokenizer()
          tokenizer.save_pretrained(self.tmpdirname			)







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



          '''simple docstring'''



          return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"			)







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



          '''simple docstring'''



          return self.tokenizer_class.from_pretrained(self.tmpdirname  ,		**__lowercase			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A=False  ,		A=20  ,		A=5			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				[]
          for i in range(len(__lowercase			)			):
               try:
                    a     =				tokenizer.decode([i]  ,		clean_up_tokenization_spaces=__lowercase			)
               except UnicodeDecodeError:
                    pass
               toks.append((i, tok)			)

          a     =				list(filter(lambda A			: re.match(r"^[ a-zA-Z]+$"  ,		t[1]			)  ,		__lowercase			)			)
          a     =				list(filter(lambda A			: [t[0]] == tokenizer.encode(t[1]  ,		add_special_tokens=__lowercase			)  ,		__lowercase			)			)
          if max_length is not None and len(__lowercase			) > max_length:
               a     =				toks[:max_length]
          if min_length is not None and len(__lowercase			) < min_length and len(__lowercase			) > 0:
               while len(__lowercase			) < min_length:
                    a     =				toks + toks
        # toks_str = [t[1] for t in toks]
          a     =				[t[0] for t in toks]

          # Ensure consistency
          a     =				tokenizer.decode(__lowercase  ,		clean_up_tokenization_spaces=__lowercase			)
          if " " not in output_txt and len(__lowercase			) > 1:
               a     =				(
                   tokenizer.decode([toks_ids[0]]  ,		clean_up_tokenization_spaces=__lowercase			)
                   + " "
                   + tokenizer.decode(toks_ids[1:]  ,		clean_up_tokenization_spaces=__lowercase			)
               )
          if with_prefix_space:
               a     =				" " + output_txt
          a     =				tokenizer.encode(__lowercase  ,		add_special_tokens=__lowercase			)
          return output_txt, output_ids







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.perceiver_tokenizer
          a     =				"Unicode €."
          a     =				tokenizer(__lowercase			)
          a     =				[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
          self.assertEqual(encoded["input_ids"]  ,		__lowercase			)

          # decoding
          a     =				tokenizer.decode(__lowercase			)
          self.assertEqual(__lowercase  ,		"[CLS]Unicode €.[SEP]"			)

          a     =				tokenizer("e è é ê ë"			)
          a     =				[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
          self.assertEqual(encoded["input_ids"]  ,		__lowercase			)
          # decoding
          a     =				tokenizer.decode(__lowercase			)
          self.assertEqual(__lowercase  ,		"[CLS]e è é ê ë[SEP]"			)

          # encode/decode, but with `encode` instead of `__call__`
          self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"			)			)  ,		"[CLS]e è é ê ë[SEP]"			)







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



          '''simple docstring'''



          a     =				self.perceiver_tokenizer
          a     =				["A long paragraph for summarization.", "Another paragraph for summarization."]
          # fmt: off
          a     =				[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
          # fmt: on
          a     =				tokenizer(__lowercase  ,		padding=__lowercase  ,		return_tensors=__lowercase			)
          self.assertIsInstance(__lowercase  ,		__lowercase			)

          if FRAMEWORK != "jax":
               a     =				list(batch.input_ids.numpy()[0]			)
          else:
               a     =				list(batch.input_ids.tolist()[0]			)

          self.assertListEqual(__lowercase  ,		__lowercase			)

          self.assertEqual((2, 38)  ,		batch.input_ids.shape			)
          self.assertEqual((2, 38)  ,		batch.attention_mask.shape			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				self.perceiver_tokenizer
          a     =				["A long paragraph for summarization.", "Another paragraph for summarization."]
          a     =				tokenizer(__lowercase  ,		padding=__lowercase  ,		return_tensors=__lowercase			)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn("input_ids"  ,		__lowercase			)
          self.assertIn("attention_mask"  ,		__lowercase			)
          self.assertNotIn("decoder_input_ids"  ,		__lowercase			)
          self.assertNotIn("decoder_attention_mask"  ,		__lowercase			)







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



          '''simple docstring'''



          a     =				self.perceiver_tokenizer
          a     =				[
              "Summary of the text.",
              "Another summary.",
          ]
          a     =				tokenizer(
              text_target=__lowercase  ,		max_length=32  ,		padding="max_length"  ,		truncation=__lowercase  ,		return_tensors=__lowercase			)
          self.assertEqual(32  ,		targets["input_ids"].shape[1]			)







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



          '''simple docstring'''



          a     =				self.get_tokenizers()
          for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
                    self.assertNotEqual(tokenizer.model_max_length  ,		42			)

        # Now let's start the test
          a     =				self.get_tokenizers()
          for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
                    # Isolate this from the other tests because we save additional tokens/etc
                    a     =				tempfile.mkdtemp()

                    a     =				" He is very happy, UNwant\u00E9d,running"
                    a     =				tokenizer.encode(__lowercase  ,		add_special_tokens=__lowercase			)
                    tokenizer.save_pretrained(__lowercase			)

                    a     =				tokenizer.__class__.from_pretrained(__lowercase			)
                    a     =				after_tokenizer.encode(__lowercase  ,		add_special_tokens=__lowercase			)
                    self.assertListEqual(__lowercase  ,		__lowercase			)

                    shutil.rmtree(__lowercase			)

          a     =				self.get_tokenizers(model_max_length=42			)
          for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
                    # Isolate this from the other tests because we save additional tokens/etc
                    a     =				tempfile.mkdtemp()

                    a     =				" He is very happy, UNwant\u00E9d,running"
                    tokenizer.add_tokens(["bim", "bambam"]			)
                    a     =				tokenizer.additional_special_tokens
                    additional_special_tokens.append("new_additional_special_token"			)
                    tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}			)
                    a     =				tokenizer.encode(__lowercase  ,		add_special_tokens=__lowercase			)
                    tokenizer.save_pretrained(__lowercase			)

                    a     =				tokenizer.__class__.from_pretrained(__lowercase			)
                    a     =				after_tokenizer.encode(__lowercase  ,		add_special_tokens=__lowercase			)
                    self.assertListEqual(__lowercase  ,		__lowercase			)
                    self.assertIn("new_additional_special_token"  ,		after_tokenizer.additional_special_tokens			)
                    self.assertEqual(after_tokenizer.model_max_length  ,		42			)

                    a     =				tokenizer.__class__.from_pretrained(__lowercase  ,		model_max_length=43			)
                    self.assertEqual(tokenizer.model_max_length  ,		43			)

                    shutil.rmtree(__lowercase			)







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				[]
          if self.test_slow_tokenizer:
               tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())			)

          if self.test_rust_tokenizer:
               tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())			)

          for tokenizer_class, tokenizer_utils in tokenizer_list:
               with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer_utils.save_pretrained(__lowercase			)

                    with open(os.path.join(__lowercase  ,		"special_tokens_map.json"			)  ,		encoding="utf-8"			) as json_file:
                         a     =				json.load(__lowercase			)

                    with open(os.path.join(__lowercase  ,		"tokenizer_config.json"			)  ,		encoding="utf-8"			) as json_file:
                         a     =				json.load(__lowercase			)

                    a     =				[F'''<extra_id_{i}>''' for i in range(125			)]

                    a     =				added_tokens_extra_ids + [
                        "an_additional_special_token"
                    ]
                    a     =				added_tokens_extra_ids + [
                        "an_additional_special_token"
                    ]

                    with open(os.path.join(__lowercase  ,		"special_tokens_map.json"			)  ,		"w"  ,		encoding="utf-8"			) as outfile:
                         json.dump(__lowercase  ,		__lowercase			)
                    with open(os.path.join(__lowercase  ,		"tokenizer_config.json"			)  ,		"w"  ,		encoding="utf-8"			) as outfile:
                         json.dump(__lowercase  ,		__lowercase			)

                    # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                    # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                    # "special_tokens_map.json" files
                    a     =				tokenizer_class.from_pretrained(
                        __lowercase  ,		)
                    self.assertIn(
                        "an_additional_special_token"  ,		tokenizer_without_change_in_init.additional_special_tokens			)
                    self.assertEqual(
                        ["an_additional_special_token"]  ,		tokenizer_without_change_in_init.convert_ids_to_tokens(
                            tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]			)			)  ,		)

                    # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                    a     =				added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"  ,		lstrip=__lowercase			)]
                    a     =				tokenizer_class.from_pretrained(
                        __lowercase  ,		additional_special_tokens=__lowercase  ,		)

                    self.assertIn("a_new_additional_special_token"  ,		tokenizer.additional_special_tokens			)
                    self.assertEqual(
                        ["a_new_additional_special_token"]  ,		tokenizer.convert_ids_to_tokens(
                            tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]			)			)  ,		)







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



          '''simple docstring'''



          a     =				self.perceiver_tokenizer
          self.assertEqual(tokenizer.decode([178]			)  ,		"�"			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          pass







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



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          pass







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



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				self.get_tokenizers(fast=__lowercase  ,		do_lower_case=__lowercase			)
          for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
                    a     =				["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
                    a     =				tokenizer.convert_tokens_to_string(__lowercase			)

                    self.assertIsInstance(__lowercase  ,		__lowercase			)







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			)}

