






"""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


lowerCamelCase_							      = get_tests_dir('''fixtures/test_sentencepiece.model''')

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

if is_torch_available():
			lowerCamelCase_							      = '''pt'''
elif is_tf_available():
			lowerCamelCase_							      = '''tf'''
else:
			lowerCamelCase_							      = '''jax'''







@require_sentencepiece
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							MarianTokenizer
	__magic_name__       =							False
	__magic_name__       =							True





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Optional[Any]:
				super().setUp()
				UpperCAmelCase_	:					List[Any]     	=   ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
				UpperCAmelCase_	:					Tuple     	=   dict(zip(lowerCAmelCase_      ,      range(len(lowerCAmelCase_		)		)		)		)
				UpperCAmelCase_	:					Optional[Any]     	=   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"]		)

				UpperCAmelCase_	:					str     	=   MarianTokenizer.from_pretrained(self.tmpdirname		)
				tokenizer.save_pretrained(self.tmpdirname		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      **lowerCAmelCase_   :					Tuple		)		->     MarianTokenizer:
				return MarianTokenizer.from_pretrained(self.tmpdirname      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[Any]		)		->     List[Any]:
				return (
				    "This is a test",
				    "This is a test",
				)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     List[Any]:
				UpperCAmelCase_	:					List[str]     	=   "</s>"
				UpperCAmelCase_	:					Tuple     	=   0

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     List[Any]:
				UpperCAmelCase_	:					Union[str, 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(lowerCAmelCase_		)      ,      9		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     int:
				self.assertEqual(self.get_tokenizer().vocab_size      ,      9		)





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

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Dict:
				UpperCAmelCase_	:					Optional[int]     	=   self.get_tokenizer()

				UpperCAmelCase_	:					Tuple     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Dict:
				UpperCAmelCase_	:					List[Any]     	=   self.get_tokenizer()
				UpperCAmelCase_	:					Any     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[str]:
				# fmt: off
				UpperCAmelCase_	:					int     	=   {"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 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Union[str, Any]:
				UpperCAmelCase_	:					int     	=   MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"		)

				UpperCAmelCase_	:					Dict     	=   "Tämä on testi"
				UpperCAmelCase_	:					Optional[Any]     	=   "This is a test"

				UpperCAmelCase_	:					Union[str, Any]     	=   [76, 7, 2_047, 2]
				UpperCAmelCase_	:					Optional[int]     	=   [69, 12, 11, 940, 2]

				UpperCAmelCase_	:					int     	=   tokenizer(lowerCAmelCase_		).input_ids
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer(text_target=lowerCAmelCase_		).input_ids
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   tokenizer.decode(lowerCAmelCase_      ,      skip_special_tokens=lowerCAmelCase_		)
				self.assertEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)











"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class      UpperCamelCase_			(__A	):
	__magic_name__       =							['''image_processor''', '''tokenizer''']
	__magic_name__       =							'''FlavaImageProcessor'''
	__magic_name__       =							('''BertTokenizer''', '''BertTokenizerFast''')





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					List[str]=None      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     List[Any]:
				UpperCAmelCase_	:					Any     	=   None
				if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."      ,      lowerCAmelCase_      ,      )
							UpperCAmelCase_	:					Tuple     	=   kwargs.pop("feature_extractor"		)

				UpperCAmelCase_	:					Optional[Any]     	=   image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
							raise ValueError("You need to specify an `image_processor`."		)
				if tokenizer is None:
							raise ValueError("You need to specify a `tokenizer`."		)

				super().__init__(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   self.image_processor





	def __call__( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[ImageInput] = None      ,      lowerCAmelCase_   :					Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Union[bool, str, PaddingStrategy] = False      ,      lowerCAmelCase_   :					Union[bool, str, TruncationStrategy] = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      **lowerCAmelCase_   :					Optional[int]      ,      )		->     List[Any]:

				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:
							UpperCAmelCase_	:					Union[str, Any]     	=   self.tokenizer(
							    text=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      stride=lowerCAmelCase_      ,      pad_to_multiple_of=lowerCAmelCase_      ,      return_token_type_ids=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      return_overflowing_tokens=lowerCAmelCase_      ,      return_special_tokens_mask=lowerCAmelCase_      ,      return_offsets_mapping=lowerCAmelCase_      ,      return_length=lowerCAmelCase_      ,      verbose=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				if images is not None:
							UpperCAmelCase_	:					Optional[Any]     	=   self.image_processor(
							    lowerCAmelCase_      ,      return_image_mask=lowerCAmelCase_      ,      return_codebook_pixels=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				if text is not None and images is not None:
							encoding.update(lowerCAmelCase_		)
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowerCAmelCase_		)      ,      tensor_type=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      *lowerCAmelCase_   :					int      ,      **lowerCAmelCase_   :					List[str]		)		->     Any:
				return self.tokenizer.batch_decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					Optional[int]		)		->     List[Any]:
				return self.tokenizer.decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   self.tokenizer.model_input_names
				UpperCAmelCase_	:					Any     	=   self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor_class





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor



