

import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A			:					Optional[int]						=			get_tests_dir('fixtures/test_sentencepiece_no_bos.model')





@require_sentencepiece
@require_tokenizers
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      PegasusTokenizer
   lowerCamelCase__				=      PegasusTokenizerFast
   lowerCamelCase__				=      True
   lowerCamelCase__				=      True





   def      _snake_case		(       self					:			Optional[Any]			):
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE										=   PegasusTokenizer(__lowerCamelCase			)
       tokenizer.save_pretrained(self.tmpdirname			)





   @cached_property
   def      _snake_case		(       self					:			Any			):
       return PegasusTokenizer.from_pretrained("google/pegasus-large"			)





   def      _snake_case		(       self					:			str     , **__lowerCamelCase					:			Dict			):
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Any			):
       return ("This is a test", "This is a test")





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   "</s>"
       SCREAMING_SNAKE_CASE										=   1

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





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   list(self.get_tokenizer().get_vocab().keys()			)

       self.assertEqual(vocab_keys[0]     , "<pad>"			)
       self.assertEqual(vocab_keys[1]     , "</s>"			)
       self.assertEqual(vocab_keys[-1]     , "v"			)
       self.assertEqual(len(__lowerCamelCase			)     , 1103			)





   def      _snake_case		(       self					:			List[Any]			):
       self.assertEqual(self.get_tokenizer().vocab_size     , 1103			)





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
       SCREAMING_SNAKE_CASE										=   self.tokenizer_class.from_pretrained(self.tmpdirname			)
       SCREAMING_SNAKE_CASE										=   (
           "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
           " </s> <pad> <pad> <pad>"
       )
       SCREAMING_SNAKE_CASE										=   rust_tokenizer([raw_input_str]     , return_tensors=__lowerCamelCase     , add_special_tokens=__lowerCamelCase			).input_ids[0]
       SCREAMING_SNAKE_CASE										=   py_tokenizer([raw_input_str]     , return_tensors=__lowerCamelCase     , add_special_tokens=__lowerCamelCase			).input_ids[0]
       self.assertListEqual(__lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       SCREAMING_SNAKE_CASE										=   "<mask_1> To ensure a <mask_2> flow of bank resolutions."
       SCREAMING_SNAKE_CASE										=   [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
       SCREAMING_SNAKE_CASE										=   tokenizer([raw_input_str]     , return_tensors=__lowerCamelCase			).input_ids[0]
       self.assertListEqual(__lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 96103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1024
       SCREAMING_SNAKE_CASE										=   "To ensure a smooth flow of bank resolutions."
       SCREAMING_SNAKE_CASE										=   [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
       SCREAMING_SNAKE_CASE										=   tokenizer([raw_input_str]     , return_tensors=__lowerCamelCase			).input_ids[0]
       self.assertListEqual(__lowerCamelCase     , __lowerCamelCase			)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]			) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





   @require_torch
   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   ["This is going to be way too long." * 150, "short example"]
       SCREAMING_SNAKE_CASE										=   ["not super long but more than 5 tokens", "tiny"]
       SCREAMING_SNAKE_CASE										=   self._large_tokenizer(__lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase     , return_tensors="pt"			)
       SCREAMING_SNAKE_CASE										=   self._large_tokenizer(
           text_target=__lowerCamelCase     , max_length=5     , padding=__lowerCamelCase     , truncation=__lowerCamelCase     , return_tensors="pt"			)

       assert batch.input_ids.shape == (2, 1024)
       assert batch.attention_mask.shape == (2, 1024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(__lowerCamelCase			) == 2  # input_ids, attention_mask.





   @slow
   def      _snake_case		(       self					:			str			):
       # fmt: off
       SCREAMING_SNAKE_CASE										=   {"input_ids": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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]], "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, 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, 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=__lowerCamelCase     , model_name="google/bigbird-pegasus-large-arxiv"     , revision="ba85d0851d708441f91440d509690f1ab6353415"     , )





@require_sentencepiece
@require_tokenizers
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      PegasusTokenizer
   lowerCamelCase__				=      PegasusTokenizerFast
   lowerCamelCase__				=      True
   lowerCamelCase__				=      True





   def      _snake_case		(       self					:			int			):
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE										=   PegasusTokenizer(__lowerCamelCase     , offset=0     , mask_token_sent=__lowerCamelCase     , mask_token="[MASK]"			)
       tokenizer.save_pretrained(self.tmpdirname			)





   @cached_property
   def      _snake_case		(       self					:			str			):
       return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"			)





   def      _snake_case		(       self					:			int     , **__lowerCamelCase					:			Optional[Any]			):
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **__lowerCamelCase			)





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			List[str]			):
       return ("This is a test", "This is a test")





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
       SCREAMING_SNAKE_CASE										=   self.tokenizer_class.from_pretrained(self.tmpdirname			)
       SCREAMING_SNAKE_CASE										=   (
           "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
           " <pad> <pad> <pad>"
       )
       SCREAMING_SNAKE_CASE										=   rust_tokenizer([raw_input_str]     , return_tensors=__lowerCamelCase     , add_special_tokens=__lowerCamelCase			).input_ids[0]
       SCREAMING_SNAKE_CASE										=   py_tokenizer([raw_input_str]     , return_tensors=__lowerCamelCase     , add_special_tokens=__lowerCamelCase			).input_ids[0]
       self.assertListEqual(__lowerCamelCase     , __lowerCamelCase			)





   @require_torch
   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   ["This is going to be way too long." * 1000, "short example"]
       SCREAMING_SNAKE_CASE										=   ["not super long but more than 5 tokens", "tiny"]
       SCREAMING_SNAKE_CASE										=   self._large_tokenizer(__lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase     , return_tensors="pt"			)
       SCREAMING_SNAKE_CASE										=   self._large_tokenizer(
           text_target=__lowerCamelCase     , max_length=5     , padding=__lowerCamelCase     , truncation=__lowerCamelCase     , return_tensors="pt"			)

       assert batch.input_ids.shape == (2, 4096)
       assert batch.attention_mask.shape == (2, 4096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(__lowerCamelCase			) == 2  # input_ids, attention_mask.





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   (
           "This is an example string that is used to test the original TF implementation against the HF"
           " implementation"
       )

       SCREAMING_SNAKE_CASE										=   self._large_tokenizer(__lowerCamelCase			).input_ids

       self.assertListEqual(
           __lowerCamelCase     , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]     , )


import pytest


__A			:					Optional[Any]						=			'__dummy_dataset1__'

__A			:					Optional[int]						=			'\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 	__a     (      A__			:  Optional[Any]			,  A__			:  List[str]			,  A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   dataset_loading_script_name
    SCREAMING_SNAKE_CASE										=   tmp_path / "datasets" / script_name
    script_dir.mkdir(parents=A__       )
    SCREAMING_SNAKE_CASE										=   script_dir / F"{script_name}.py"
    with open(A__			,  "w"       ) as f:
        f.write(A__       )
    return str(A__       )