




"""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


SCREAMING_SNAKE_CASE    =	get_tests_dir("fixtures/test_sentencepiece.model")

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

if is_torch_available():
  SCREAMING_SNAKE_CASE    =	"pt"
elif is_tf_available():
  SCREAMING_SNAKE_CASE    =	"tf"
else:
  SCREAMING_SNAKE_CASE    =	"jax"




@require_sentencepiece
class      UpperCAmelCase_  (						A_,       unittest.TestCase							):
    lowercase__						 =				MarianTokenizer
    lowercase__						 =				False
    lowercase__						 =				True




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




     '''simple docstring'''

     super().setUp()
     A__	=    ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
     A__	=    dict(zip(lowerCAmelCase__     ,			range(len(lowerCAmelCase__				)				)				)				)
     A__	=    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__	=    MarianTokenizer.from_pretrained(self.tmpdirname				)
     tokenizer.save_pretrained(self.tmpdirname				)




    def 							__magic_name__							(   self						:	Any     ,			**snake_case_						:	Optional[int]				)				->							MarianTokenizer:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	List[str]				)				->							List[Any]:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[int]				)				->							str:




     '''simple docstring'''

     A__	=    "</s>"
     A__	=    0

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




    def 							__magic_name__							(   self						:	Tuple				)				->							int:




     '''simple docstring'''

     A__	=    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 							__magic_name__							(   self						:	Optional[int]				)				->							Optional[int]:




     '''simple docstring'''

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




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




     '''simple docstring'''

     A__	=    MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""				)
     A__	=    en_de_tokenizer(["I am a small frog"]     ,			return_tensors=lowerCAmelCase__				)
     self.assertIsInstance(lowerCAmelCase__     ,			lowerCAmelCase__				)
     A__	=    [38, 121, 14, 697, 38_848, 0]
     self.assertListEqual(lowerCAmelCase__     ,			batch.input_ids[0]				)

     A__	=    tempfile.mkdtemp()
     en_de_tokenizer.save_pretrained(lowerCAmelCase__				)
     A__	=    [x.name for x in Path(lowerCAmelCase__				).glob("*"				)]
     self.assertIn("source.spm"     ,			lowerCAmelCase__				)
     MarianTokenizer.from_pretrained(lowerCAmelCase__				)




    def 							__magic_name__							(   self						:	int				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.get_tokenizer()

     A__	=    tok(
         ["I am a small frog" * 1_000, "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 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     A__	=    self.get_tokenizer()
     A__	=    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 							__magic_name__							(   self						:	List[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    {"input_ids": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], "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 							__magic_name__							(   self						:	int				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"				)

     A__	=    "Tämä on testi"
     A__	=    "This is a test"

     A__	=    [76, 7, 2_047, 2]
     A__	=    [69, 12, 11, 940, 2]

     A__	=    tokenizer(lowerCAmelCase__				).input_ids
     self.assertListEqual(lowerCAmelCase__     ,			lowerCAmelCase__				)

     A__	=    tokenizer(text_target=lowerCAmelCase__				).input_ids
     self.assertListEqual(lowerCAmelCase__     ,			lowerCAmelCase__				)

     A__	=    tokenizer.decode(lowerCAmelCase__     ,			skip_special_tokens=lowerCAmelCase__				)
     self.assertEqual(lowerCAmelCase__     ,			lowerCAmelCase__				)











"""simple docstring"""



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''AutoImageProcessor'''
    lowercase__						 =				'''AutoTokenizer'''




    def __init__(   self						:	str     ,			snake_case_						:	Dict     ,			snake_case_						:	List[str]				)				->							str:




     '''simple docstring'''

     super().__init__(snake_case_     ,			snake_case_				)
     A__	=    self.image_processor




    def __call__(   self						:	int     ,			snake_case_						:	Any=None     ,			snake_case_						:	Any=None     ,			snake_case_						:	Union[str, Any]=None     ,			**snake_case_						:	Optional[int]				)				->							Optional[int]:




     '''simple docstring'''

     if text is None and images is None:
      raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			*snake_case_						:	Union[str, Any]     ,			**snake_case_						:	List[Any]				)				->							int:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	List[str]     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							Tuple:




     '''simple docstring'''

     return self.tokenizer.decode(*snake_case_     ,			**snake_case_				)




    @property
    def 							__magic_name__							(   self						:	List[Any]				)				->							List[Any]:




     '''simple docstring'''

     return ["input_ids", "attention_mask", "pixel_values"]





