



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


SCREAMING_SNAKE_CASE_:Union[str, Any]					=						get_tests_dir("""fixtures/test_sentencepiece.model""")

SCREAMING_SNAKE_CASE_:int					=						{"""target_lang""": """fi""", """source_lang""": """en"""}
SCREAMING_SNAKE_CASE_:Tuple					=						""">>zh<<"""
SCREAMING_SNAKE_CASE_:int					=						"""Helsinki-NLP/"""

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






@require_sentencepiece
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




				'''simple docstring'''






				__lowerCamelCase      :			Union[str, Any] =				MarianTokenizer
				__lowerCamelCase      :			Dict =				False
				__lowerCamelCase      :			List[str] =				True






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

								A			:	Optional[Any]										=       MarianTokenizer.from_pretrained(self.tmpdirname		)
								tokenizer.save_pretrained(self.tmpdirname		)






				def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
								return MarianTokenizer.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






				def       _lowerCAmelCase (	self,		lowerCamelCase__		):
								return (
								    "This is a test",
								    "This is a test",
								)






				def       _lowerCAmelCase (	self		):
								A			:	Any										=       """</s>"""
								A			:	str										=       0

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






				def       _lowerCAmelCase (	self		):
								A			:	Tuple										=       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(lowerCamelCase__		),		9		)






				def       _lowerCAmelCase (	self		):
								self.assertEqual(self.get_tokenizer().vocab_size,		9		)






				def       _lowerCAmelCase (	self		):
								A			:	Any										=       MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''		)
								A			:	Tuple										=       en_de_tokenizer(["""I am a small frog"""],		return_tensors=lowerCamelCase__		)
								self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)
								A			:	List[str]										=       [38, 121, 14, 697, 3_8848, 0]
								self.assertListEqual(lowerCamelCase__,		batch.input_ids[0]		)

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






				def       _lowerCAmelCase (	self		):
								A			:	Tuple										=       self.get_tokenizer()

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






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






				@slow
				def       _lowerCAmelCase (	self		):
								# fmt: off
								A			:	Dict										=       {"""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=lowerCamelCase__,		model_name="""Helsinki-NLP/opus-mt-en-de""",		revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""",		decode_kwargs={"""use_source_tokenizer""": True},		)






				def       _lowerCAmelCase (	self		):
								A			:	Optional[Any]										=       MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""		)

								A			:	Optional[Any]										=       """Tämä on testi"""
								A			:	int										=       """This is a test"""

								A			:	Optional[int]										=       [76, 7, 2047, 2]
								A			:	Union[str, Any]										=       [69, 12, 11, 940, 2]

								A			:	str										=       tokenizer(lowerCamelCase__		).input_ids
								self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)

								A			:	int										=       tokenizer(text_target=lowerCamelCase__		).input_ids
								self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)

								A			:	str										=       tokenizer.decode(lowerCamelCase__,		skip_special_tokens=lowerCamelCase__		)
								self.assertEqual(lowerCamelCase__,		lowerCamelCase__		)







from __future__ import annotations




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  list[int]:



    """simple docstring"""






    A			:	Tuple										=       2
    A			:	List[Any]										=       []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(_lowerCAmelCase		)
    if n > 1:
        factors.append(_lowerCAmelCase		)
    return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()


