


"""simple docstring"""




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


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

_snake_case					:		Optional[Any]      =    {"target_lang": "fi", "source_lang": "en"}
_snake_case					:		Any      =    ">>zh<<"
_snake_case					:		Any      =    "Helsinki-NLP/"

if is_torch_available():
			_snake_case					:		Optional[Any]      =    "pt"
elif is_tf_available():
			_snake_case					:		Any      =    "tf"
else:
			_snake_case					:		Optional[int]      =    "jax"

@require_sentencepiece
class     a		(__lowercase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  MarianTokenizer
							__UpperCAmelCase   :							Optional[Any]					  =  False
							__UpperCAmelCase   :							Dict					  =  True

							def    __snake_case (							self	:  Tuple    )     ->   List[Any]:
														super().setUp()
														__snake_case		:      List[str]										=       ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
														__snake_case		:      Optional[Any]										=       dict(zip(_a   ,     range(len(_a    )    )    )    )
														__snake_case		:      Dict										=       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"]    )

														__snake_case		:      List[str]										=       MarianTokenizer.from_pretrained(self.tmpdirname    )
														tokenizer.save_pretrained(self.tmpdirname    )

							def    __snake_case (							self	:  Any   ,     **lowerCamelCase	:  List[Any]    )     ->   MarianTokenizer:
														return MarianTokenizer.from_pretrained(self.tmpdirname   ,     **_a    )

							def    __snake_case (							self	:  Optional[int]   ,     lowerCamelCase	:  Optional[Any]    )     ->   Optional[int]:
														return (
														    "This is a test",
														    "This is a test",
														)

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Dict:
														__snake_case		:      Tuple										=       '''</s>'''
														__snake_case		:      Union[str, Any]										=       0

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

							def    __snake_case (							self	:  str    )     ->   Tuple:
														__snake_case		:      str										=       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    __snake_case (							self	:  Tuple    )     ->   Optional[int]:
														self.assertEqual(self.get_tokenizer().vocab_size   ,     9    )

							def    __snake_case (							self	:  Optional[Any]    )     ->   List[Any]:
														__snake_case		:      Union[str, Any]										=       MarianTokenizer.from_pretrained(F'{ORG_NAME}opus-mt-en-de'    )
														__snake_case		:      List[str]										=       en_de_tokenizer(["I am a small frog"]   ,     return_tensors=_a    )
														self.assertIsInstance(_a   ,     _a    )
														__snake_case		:      str										=       [38, 121, 14, 697, 38848, 0]
														self.assertListEqual(_a   ,     batch.input_ids[0]    )

														__snake_case		:      Tuple										=       tempfile.mkdtemp()
														en_de_tokenizer.save_pretrained(_a    )
														__snake_case		:      Optional[Any]										=       [x.name for x in Path(_a    ).glob("*"    )]
														self.assertIn("source.spm"   ,     _a    )
														MarianTokenizer.from_pretrained(_a    )

							def    __snake_case (							self	:  List[str]    )     ->   Optional[Any]:
														__snake_case		:      Union[str, Any]										=       self.get_tokenizer()

														__snake_case		:      List[str]										=       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    __snake_case (							self	:  List[Any]    )     ->   Any:
														__snake_case		:      Dict										=       self.get_tokenizer()
														__snake_case		:      List[Any]										=       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    __snake_case (							self	:  Tuple    )     ->   Dict:
														# fmt: off
														__snake_case		:      List[str]										=       {'''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    __snake_case (							self	:  List[str]    )     ->   Optional[Any]:
														__snake_case		:      Optional[int]										=       MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"    )

														__snake_case		:      Any										=       '''Tämä on testi'''
														__snake_case		:      List[Any]										=       '''This is a test'''

														__snake_case		:      Tuple										=       [76, 7, 2047, 2]
														__snake_case		:      Union[str, Any]										=       [69, 12, 11, 940, 2]

														__snake_case		:      str										=       tokenizer(_a    ).input_ids
														self.assertListEqual(_a   ,     _a    )

														__snake_case		:      str										=       tokenizer(text_target=_a    ).input_ids
														self.assertListEqual(_a   ,     _a    )

														__snake_case		:      List[str]										=       tokenizer.decode(_a   ,     skip_special_tokens=_a    )
														self.assertEqual(_a   ,     _a    )





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_snake_case					:		List[str]      =    namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (abs(source - target     ) / target) < 0.0_1







@pytest.mark.integration
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       _TestCommandArgs(dataset=__lowerCamelCase							,     all_configs=__lowerCamelCase							,     save_infos=__lowerCamelCase     )
							__snake_case		:      List[Any]										=       TestCommand(*__lowerCamelCase     )
							test_command.run()
							__snake_case		:      List[Any]										=       os.path.join(__lowerCamelCase							,     "README.md"     )
							assert os.path.exists(__lowerCamelCase     )
							__snake_case		:      Optional[Any]										=       DatasetInfosDict.from_directory(__lowerCamelCase     )
							__snake_case		:      List[str]										=       DatasetInfosDict(
							    {
							        "default": DatasetInfo(
							            features=Features(
							                {
							                    "tokens": Sequence(Value("string"     )     ),
							                    "ner_tags": Sequence(
							                        ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]     )     ),
							                    "langs": Sequence(Value("string"     )     ),
							                    "spans": Sequence(Value("string"     )     ),
							                }     )							,     splits=[
							                {
							                    "name": "train",
							                    "num_bytes": 2_3_5_1_5_6_3,
							                    "num_examples": 1_0_0_0_0,
							                },
							                {
							                    "name": "validation",
							                    "num_bytes": 2_3_8_4_1_8,
							                    "num_examples": 1_0_0_0,
							                },
							            ]							,     download_size=3_9_4_0_6_8_0							,     dataset_size=2_5_8_9_9_8_1							,     )
							    }     )
							assert dataset_infos.keys() == expected_dataset_infos.keys()
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														__snake_case			,      __snake_case		:      Tuple										=       getattr(dataset_infos["default"]							,     __lowerCamelCase     ), getattr(expected_dataset_infos["default"]							,     __lowerCamelCase     )
														if key == "num_bytes":
																					assert is_apercent_close(__lowerCamelCase							,     __lowerCamelCase     )
														elif key == "splits":
																					assert list(__lowerCamelCase     ) == list(__lowerCamelCase     )
																					for split in result:
																												assert result[split].name == expected[split].name
																												assert result[split].num_examples == expected[split].num_examples
																												assert is_apercent_close(result[split].num_bytes							,     expected[split].num_bytes     )
														else:
																					result == expected

