


'''simple docstring'''







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE							: Tuple										=			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							: Optional[Any]										=			[
		    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'AutoformerForPrediction',
		    'AutoformerModel',
		    'AutoformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_autoformer import (
		    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    AutoformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_autoformer import (
				    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    AutoformerForPrediction,
				    AutoformerModel,
				    AutoformerPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE							: Optional[int]										=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




"""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				:  List[Any] =       get_tests_dir('fixtures/test_sentencepiece.model')

__snake_case				:  Union[str, Any] =       {'target_lang': 'fi', 'source_lang': 'en'}
__snake_case				:  Union[str, Any] =       '>>zh<<'
__snake_case				:  List[str] =       'Helsinki-NLP/'

if is_torch_available():
	__snake_case				:  Optional[int] =       'pt'
elif is_tf_available():
	__snake_case				:  List[Any] =       'tf'
else:
	__snake_case				:  Union[str, Any] =       'jax'



@require_sentencepiece
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      MarianTokenizer
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      True

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Tuple:

				"""simple docstring"""

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

				__lowerCAmelCase :    Tuple				=			MarianTokenizer.from_pretrained(self.tmpdirname)
				tokenizer.save_pretrained(self.tmpdirname)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			MarianTokenizer:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						List[str])							->			Dict:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			"</s>"
				__lowerCAmelCase :    int				=			0

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[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(_SCREAMING_SNAKE_CASE)			,     9)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Dict:

				"""simple docstring"""

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

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de""")
				__lowerCAmelCase :    int				=			en_de_tokenizer(["I am a small frog"]			,     return_tensors=_SCREAMING_SNAKE_CASE)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			[38, 121, 14, 697, 3_8848, 0]
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     batch.input_ids[0])

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

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

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.get_tokenizer()

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Optional[Any]:

				"""simple docstring"""

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

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			{"input_ids": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], "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=_SCREAMING_SNAKE_CASE			,     model_name="Helsinki-NLP/opus-mt-en-de"			,     revision="1a8c2263da11e68e50938f97e10cd57820bd504c"			,     decode_kwargs={"use_source_tokenizer": True}			,     )







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

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs")

				__lowerCAmelCase :    List[str]				=			"Tämä on testi"
				__lowerCAmelCase :    int				=			"This is a test"

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

				__lowerCAmelCase :    List[str]				=			tokenizer(_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    str				=			tokenizer(text_target=_SCREAMING_SNAKE_CASE).input_ids
				self.assertListEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			tokenizer.decode(_SCREAMING_SNAKE_CASE			,     skip_special_tokens=_SCREAMING_SNAKE_CASE)
				self.assertEqual(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)