



"""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


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

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

if is_torch_available():
							A_						:    Tuple				=      'pt'
elif is_tf_available():
							A_						:    int				=      'tf'
else:
							A_						:    Any				=      'jax'
@require_sentencepiece
class     lowerCAmelCase__						(	_lowerCamelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= MarianTokenizer
	_SCREAMING_SNAKE_CASE	:		str  						= False
	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= True





	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Any:





					"""simple docstring"""


					super().setUp()
					SCREAMING_SNAKE_CASE						:							List[str]							=     ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     dict(zip(_SCREAMING_SNAKE_CASE     ,       range(len(_SCREAMING_SNAKE_CASE    )    )    )    )
					SCREAMING_SNAKE_CASE						:							Optional[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']    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     MarianTokenizer.from_pretrained(self.tmpdirname    )
					tokenizer.save_pretrained(self.tmpdirname    )





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





					"""simple docstring"""


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





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





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     '</s>'
					SCREAMING_SNAKE_CASE						:							Any							=     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 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     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 			_lowerCAmelCase     (      self				:       List[str]    )						->	Optional[Any]:





					"""simple docstring"""


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





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""    )
					SCREAMING_SNAKE_CASE						:							int							=     en_de_tokenizer(['I am a small frog']     ,       return_tensors=_SCREAMING_SNAKE_CASE    )
					self.assertIsInstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     [38, 121, 14, 697, 38_848, 0]
					self.assertListEqual(_SCREAMING_SNAKE_CASE     ,       batch.input_ids[0]    )

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





	def 			_lowerCAmelCase     (      self				:       str    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     self.get_tokenizer()

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     tok(
					    ['I am a small frog' * 1_000, '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 			_lowerCAmelCase     (      self				:       Any    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.get_tokenizer()
					SCREAMING_SNAKE_CASE						:							List[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 			_lowerCAmelCase     (      self				:       Any    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     {'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=_SCREAMING_SNAKE_CASE     ,       model_name='Helsinki-NLP/opus-mt-en-de'     ,       revision='1a8c2263da11e68e50938f97e10cd57820bd504c'     ,       decode_kwargs={'use_source_tokenizer': True}     ,       )







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     'Tämä on testi'
					SCREAMING_SNAKE_CASE						:							Dict							=     'This is a test'

					SCREAMING_SNAKE_CASE						:							Dict							=     [76, 7, 2_047, 2]
					SCREAMING_SNAKE_CASE						:							Tuple							=     [69, 12, 11, 940, 2]

					SCREAMING_SNAKE_CASE						:							Any							=     tokenizer(_SCREAMING_SNAKE_CASE    ).input_ids
					self.assertListEqual(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

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

					SCREAMING_SNAKE_CASE						:							List[str]							=     tokenizer.decode(_SCREAMING_SNAKE_CASE     ,       skip_special_tokens=_SCREAMING_SNAKE_CASE    )
					self.assertEqual(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )










"""simple docstring"""

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)





