



'''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


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

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

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

@require_sentencepiece
class __snake_case(				__lowerCamelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		MarianTokenizer
						UpperCAmelCase							:   Tuple									=		False
						UpperCAmelCase							:   int									=		True


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

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


						def     __snake_case   ( self     ,	**A_				)     ->     List[str]:
							return MarianTokenizer.from_pretrained(self.tmpdirname     ,	**UpperCamelCase_				)


						def     __snake_case   ( self     ,	A_				)     ->     Optional[int]:
							return (
							    "This is a test",
							    "This is a test",
							)


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     """</s>"""
							lowerCAmelCase   =     0

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


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     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(UpperCamelCase_				)     ,	9				)


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


						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     MarianTokenizer.from_pretrained(f'{ORG_NAME}opus-mt-en-de'				)
							lowerCAmelCase   =     en_de_tokenizer(["""I am a small frog"""]     ,	return_tensors=UpperCamelCase_				)
							self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_				)
							lowerCAmelCase   =     [38, 121, 14, 697, 3_8848, 0]
							self.assertListEqual(UpperCamelCase_     ,	batch.input_ids[0]				)

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


						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     self.get_tokenizer()

							lowerCAmelCase   =     tok(
							    ["""I am a small frog""" * 1000, """I am a small frog"""]     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	return_tensors=UpperCamelCase_				)
							self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_				)
							self.assertEqual(batch.input_ids.shape     ,	(2, 512)				)


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     self.get_tokenizer()
							lowerCAmelCase   =     tok(["""I am a tiny frog""", """I am a small frog"""]     ,	padding=UpperCamelCase_     ,	return_tensors=UpperCamelCase_				)
							self.assertIsInstance(UpperCamelCase_     ,	UpperCamelCase_				)
							self.assertEqual(batch_smaller.input_ids.shape     ,	(2, 10)				)


						@slow
						def     __snake_case   ( self				)     ->     str:
							# fmt: off
							lowerCAmelCase   =     {"""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=UpperCamelCase_     ,	model_name="""Helsinki-NLP/opus-mt-en-de"""     ,	revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""     ,	decode_kwargs={"""use_source_tokenizer""": True}     ,	)




						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""				)

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

							lowerCAmelCase   =     [76, 7, 2047, 2]
							lowerCAmelCase   =     [69, 12, 11, 940, 2]

							lowerCAmelCase   =     tokenizer(UpperCamelCase_				).input_ids
							self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_				)

							lowerCAmelCase   =     tokenizer(text_target=UpperCamelCase_				).input_ids
							self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_				)

							lowerCAmelCase   =     tokenizer.decode(UpperCamelCase_     ,	skip_special_tokens=UpperCamelCase_				)
							self.assertEqual(UpperCamelCase_     ,	UpperCamelCase_				)




'''simple docstring'''

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 __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							if images is None and text is None:
								raise ValueError("""You have to specify either images or text."""				)

							# Get only text
							if images is None:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
							else:
								lowerCAmelCase   =     None

							if text_encoding is not None:
								encoding_image_processor.update(A_				)

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)