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


__lowerCamelCase			: Optional[Any]							   =		get_tests_dir('''fixtures/test_sentencepiece.model''')

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

if is_torch_available():
   __lowerCamelCase			: Tuple							   =		'''pt'''
elif is_tf_available():
   __lowerCamelCase			: Dict							   =		'''tf'''
else:
   __lowerCamelCase			: Tuple							   =		'''jax'''







@require_sentencepiece
class __snake_case				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							MarianTokenizer
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							True




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      MarianTokenizer.from_pretrained(self.tmpdirname      )
     tokenizer.save_pretrained(self.tmpdirname      )




   def 	__a						(    self			:				Optional[Any]       ,	**_lowercase			:				Optional[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				int      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """</s>"""
     SCREAMING_SNAKE_CASE__										=      0

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




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      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(_lowercase      )       ,	9      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






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




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""      )
     SCREAMING_SNAKE_CASE__										=      en_de_tokenizer(["""I am a small frog"""]       ,	return_tensors=_lowercase      )
     self.assertIsInstance(_lowercase       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      [38, 1_21, 14, 6_97, 3_88_48, 0]
     self.assertListEqual(_lowercase       ,	batch.input_ids[0]      )

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




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_tokenizer()

     SCREAMING_SNAKE_CASE__										=      tok(
         ["""I am a small frog""" * 10_00, """I am a small frog"""]       ,	padding=_lowercase       ,	truncation=_lowercase       ,	return_tensors=_lowercase      )
     self.assertIsInstance(_lowercase       ,	_lowercase      )
     self.assertEqual(batch.input_ids.shape       ,	(2, 5_12)      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_tokenizer()
     SCREAMING_SNAKE_CASE__										=      tok(["""I am a tiny frog""", """I am a small frog"""]       ,	padding=_lowercase       ,	return_tensors=_lowercase      )
     self.assertIsInstance(_lowercase       ,	_lowercase      )
     self.assertEqual(batch_smaller.input_ids.shape       ,	(2, 10)      )




   @slow
   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {"""input_ids""": [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], """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=_lowercase       ,	model_name="""Helsinki-NLP/opus-mt-en-de"""       ,	revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""       ,	decode_kwargs={"""use_source_tokenizer""": True}       ,	)




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__										=      [76, 7, 20_47, 2]
     SCREAMING_SNAKE_CASE__										=      [69, 12, 11, 9_40, 2]

     SCREAMING_SNAKE_CASE__										=      tokenizer(_lowercase      ).input_ids
     self.assertListEqual(_lowercase       ,	_lowercase      )

     SCREAMING_SNAKE_CASE__										=      tokenizer(text_target=_lowercase      ).input_ids
     self.assertListEqual(_lowercase       ,	_lowercase      )

     SCREAMING_SNAKE_CASE__										=      tokenizer.decode(_lowercase       ,	skip_special_tokens=_lowercase      )
     self.assertEqual(_lowercase       ,	_lowercase      )

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase			: Union[str, Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(    self			:				Any       ,	_lowercase			:				bool = True       ,	_lowercase			:				int = 32       ,	_lowercase			:				Any=PILImageResampling.BILINEAR       ,	_lowercase			:				bool = True       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor
     SCREAMING_SNAKE_CASE__										=      resample
     super().__init__(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_image_size(_lowercase      )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__										=      height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      resize(_lowercase       ,	(new_h, new_w)       ,	resample=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )
     return image




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				float       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return rescale(image=_lowercase       ,	scale=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[Union[TensorType, str]] = None       ,	_lowercase			:				ChannelDimension = ChannelDimension.FIRST       ,	**_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__										=      resample if resample is not None else self.resample

     if do_resize and size_divisor is None:
       raise ValueError("""size_divisor is required for resizing"""      )

     SCREAMING_SNAKE_CASE__										=      make_list_of_images(_lowercase      )

     if not valid_images(_lowercase      ):
       raise ValueError("""Invalid image(s)"""      )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__										=      [to_numpy_array(_lowercase      ) for img in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__										=      [self.resize(_lowercase       ,	size_divisor=_lowercase       ,	resample=_lowercase      ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__										=      [self.rescale(_lowercase       ,	scale=1 / 2_55      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      [to_channel_dimension_format(_lowercase       ,	_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      {"""pixel_values""": images}
     return BatchFeature(data=_lowercase       ,	tensor_type=_lowercase      )
