


"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
	from transformers.models.mbart.modeling_mbart import shift_tokens_right

__UpperCamelCase						=      250004
__UpperCamelCase						=      250020
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     MBartTokenizer
		SCREAMING_SNAKE_CASE_						:     str								=     MBartTokenizerFast
		SCREAMING_SNAKE_CASE_						:     str								=     True
		SCREAMING_SNAKE_CASE_						:     Dict								=     True







		def 							__A						(	self				)     ->							str:
								super().setUp()

								# We have a SentencePiece fixture for testing
								SCREAMING_SNAKE_CASE													=					MBartTokenizer(lowerCAmelCase__						,       keep_accents=lowerCAmelCase__				)
								tokenizer.save_pretrained(self.tmpdirname				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					MBartTokenizer(lowerCAmelCase__						,       keep_accents=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					tokenizer.tokenize('This is a test'				)
								self.assertListEqual(lowerCAmelCase__						,       ['▁This', '▁is', '▁a', '▁t', 'est']				)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)						,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,       )

								SCREAMING_SNAKE_CASE													=					tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
								self.assertListEqual(
								    lowerCAmelCase__						,       [
								        SPIECE_UNDERLINE + 'I',
								        SPIECE_UNDERLINE + 'was',
								        SPIECE_UNDERLINE + 'b',
								        'or',
								        'n',
								        SPIECE_UNDERLINE + 'in',
								        SPIECE_UNDERLINE + '',
								        '9',
								        '2',
								        '0',
								        '0',
								        '0',
								        ',',
								        SPIECE_UNDERLINE + 'and',
								        SPIECE_UNDERLINE + 'this',
								        SPIECE_UNDERLINE + 'is',
								        SPIECE_UNDERLINE + 'f',
								        'al',
								        's',
								        'é',
								        '.',
								    ]						,       )
								SCREAMING_SNAKE_CASE													=					tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
								self.assertListEqual(
								    lowerCAmelCase__						,       [
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ]						,       )

								SCREAMING_SNAKE_CASE													=					tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
								self.assertListEqual(
								    lowerCAmelCase__						,       [
								        SPIECE_UNDERLINE + 'I',
								        SPIECE_UNDERLINE + 'was',
								        SPIECE_UNDERLINE + 'b',
								        'or',
								        'n',
								        SPIECE_UNDERLINE + 'in',
								        SPIECE_UNDERLINE + '',
								        '<unk>',
								        '2',
								        '0',
								        '0',
								        '0',
								        ',',
								        SPIECE_UNDERLINE + 'and',
								        SPIECE_UNDERLINE + 'this',
								        SPIECE_UNDERLINE + 'is',
								        SPIECE_UNDERLINE + 'f',
								        'al',
								        's',
								        '<unk>',
								        '.',
								    ]						,       )







		def 							__A						(	self				)     ->							Tuple:
								if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

								SCREAMING_SNAKE_CASE													=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																				SCREAMING_SNAKE_CASE													=					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__						,       **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					self.tokenizer_class.from_pretrained(lowerCAmelCase__						,       **lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()

																				SCREAMING_SNAKE_CASE													=					tokenizer_r.save_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.save_pretrained(lowerCAmelCase__				)

																				# Checks it save with the same files + the tokenizer.json file for the fast one
																				self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)
																				SCREAMING_SNAKE_CASE													=					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f				)
																				self.assertSequenceEqual(lowerCAmelCase__						,       lowerCAmelCase__				)

																				# Checks everything loads correctly in the same way
																				SCREAMING_SNAKE_CASE													=					tokenizer_r.from_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.from_pretrained(lowerCAmelCase__				)

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(lowerCAmelCase__						,       lowerCAmelCase__				)				)
																										# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																										# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																				shutil.rmtree(lowerCAmelCase__				)

																				# Save tokenizer rust, legacy_format=True
																				SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()

																				SCREAMING_SNAKE_CASE													=					tokenizer_r.save_pretrained(lowerCAmelCase__						,       legacy_format=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.save_pretrained(lowerCAmelCase__				)

																				# Checks it save with the same files
																				self.assertSequenceEqual(lowerCAmelCase__						,       lowerCAmelCase__				)

																				# Checks everything loads correctly in the same way
																				SCREAMING_SNAKE_CASE													=					tokenizer_r.from_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.from_pretrained(lowerCAmelCase__				)

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(lowerCAmelCase__						,       lowerCAmelCase__				)				)

																				shutil.rmtree(lowerCAmelCase__				)

																				# Save tokenizer rust, legacy_format=False
																				SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()

																				SCREAMING_SNAKE_CASE													=					tokenizer_r.save_pretrained(lowerCAmelCase__						,       legacy_format=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.save_pretrained(lowerCAmelCase__				)

																				# Checks it saved the tokenizer.json file
																				self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)

																				# Checks everything loads correctly in the same way
																				SCREAMING_SNAKE_CASE													=					tokenizer_r.from_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE													=					tokenizer_p.from_pretrained(lowerCAmelCase__				)

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(lowerCAmelCase__						,       lowerCAmelCase__				)				)

																				shutil.rmtree(lowerCAmelCase__				)





@require_torch
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Dict								=     """facebook/mbart-large-en-ro"""
		SCREAMING_SNAKE_CASE_						:     str								=     [
		    """ UN Chief Says There Is No Military Solution in Syria""",
		    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
		]
		SCREAMING_SNAKE_CASE_						:     int								=     [
		    """Şeful ONU declară că nu există o soluţie militară în Siria""",
		    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
		    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
		    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
		]
		SCREAMING_SNAKE_CASE_						:     str								=     [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]







		@classmethod
		def 							__A						(	cls				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					MBartTokenizer.from_pretrained(
								    cls.checkpoint_name						,       src_lang='en_XX'						,       tgt_lang='ro_RO'				)
								SCREAMING_SNAKE_CASE													=					1
								return cls







		def 							__A						(	self				)     ->							int:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']						,       250_001				)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']						,       250_004				)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']						,       250_020				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
								self.assertListEqual(self.expected_src_tokens						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Any:
								self.assertIn(lowerCAmelCase__						,       self.tokenizer.all_special_ids				)
								SCREAMING_SNAKE_CASE													=					[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
								SCREAMING_SNAKE_CASE													=					self.tokenizer.decode(lowerCAmelCase__						,       skip_special_tokens=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.tokenizer.decode(generated_ids[1:]						,       skip_special_tokens=lowerCAmelCase__				)
								self.assertEqual(lowerCAmelCase__						,       lowerCAmelCase__				)
								self.assertNotIn(self.tokenizer.eos_token						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					['this is gunna be a long sentence ' * 20]
								assert isinstance(src_text[0]						,       lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					10
								SCREAMING_SNAKE_CASE													=					self.tokenizer(lowerCAmelCase__						,       max_length=lowerCAmelCase__						,       truncation=lowerCAmelCase__				).input_ids[0]
								self.assertEqual(ids[-2]						,       2				)
								self.assertEqual(ids[-1]						,       lowerCAmelCase__				)
								self.assertEqual(len(lowerCAmelCase__				)						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Tuple:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']				)						,       [250_026, 250_001]				)







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()
								SCREAMING_SNAKE_CASE													=					self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					MBartTokenizer.from_pretrained(lowerCAmelCase__				)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,       lowerCAmelCase__				)







		@require_torch
		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					self.tokenizer(self.src_text						,       text_target=self.tgt_text						,       padding=lowerCAmelCase__						,       return_tensors='pt'				)
								SCREAMING_SNAKE_CASE													=					shift_tokens_right(batch['labels']						,       self.tokenizer.pad_token_id				)

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
								assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
								assert batch.decoder_input_ids[1][-1] == 2
								assert batch.labels[1][-2:].tolist() == [2, RO_CODE]







		@require_torch
		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.tokenizer(
								    self.src_text						,       text_target=self.tgt_text						,       padding=lowerCAmelCase__						,       truncation=lowerCAmelCase__						,       max_length=len(self.expected_src_tokens				)						,       return_tensors='pt'						,       )

								SCREAMING_SNAKE_CASE													=					shift_tokens_right(batch['labels']						,       self.tokenizer.pad_token_id				)

								self.assertIsInstance(lowerCAmelCase__						,       lowerCAmelCase__				)

								self.assertEqual((2, 14)						,       batch.input_ids.shape				)
								self.assertEqual((2, 14)						,       batch.attention_mask.shape				)
								SCREAMING_SNAKE_CASE													=					batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens						,       lowerCAmelCase__				)
								self.assertEqual(2						,       batch.decoder_input_ids[0, -1]				)  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens						,       []				)
								self.assertEqual(self.tokenizer.suffix_tokens						,       [self.tokenizer.eos_token_id, EN_CODE]				)







		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					self.tokenizer(self.src_text						,       padding=lowerCAmelCase__						,       truncation=lowerCAmelCase__						,       max_length=3						,       return_tensors='pt'				)
								SCREAMING_SNAKE_CASE													=					self.tokenizer(
								    text_target=self.tgt_text						,       padding=lowerCAmelCase__						,       truncation=lowerCAmelCase__						,       max_length=10						,       return_tensors='pt'				)
								SCREAMING_SNAKE_CASE													=					targets['input_ids']
								SCREAMING_SNAKE_CASE													=					shift_tokens_right(lowerCAmelCase__						,       self.tokenizer.pad_token_id				)

								self.assertEqual(batch.input_ids.shape[1]						,       3				)
								self.assertEqual(batch.decoder_input_ids.shape[1]						,       10				)







		@require_torch
		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					self.tokenizer._build_translation_inputs(
								    'A test'						,       return_tensors='pt'						,       src_lang='en_XX'						,       tgt_lang='ar_AR'				)

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__				)						,       {
								        # A, test, EOS, en_XX
								        'input_ids': [[62, 3_034, 2, 250_004]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # ar_AR
								        'forced_bos_token_id': 250_001,
								    }						,       )






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase						=      {
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      ['''BlipImageProcessor''']


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlipModel''',
	    '''BlipPreTrainedModel''',
	    '''BlipForConditionalGeneration''',
	    '''BlipForQuestionAnswering''',
	    '''BlipVisionModel''',
	    '''BlipTextModel''',
	    '''BlipForImageTextRetrieval''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFBlipModel''',
	    '''TFBlipPreTrainedModel''',
	    '''TFBlipForConditionalGeneration''',
	    '''TFBlipForQuestionAnswering''',
	    '''TFBlipVisionModel''',
	    '''TFBlipTextModel''',
	    '''TFBlipForImageTextRetrieval''',
	]

if TYPE_CHECKING:
	from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
	from .processing_blip import BlipProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_blip import BlipImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blip import (
		    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlipForConditionalGeneration,
		    BlipForImageTextRetrieval,
		    BlipForQuestionAnswering,
		    BlipModel,
		    BlipPreTrainedModel,
		    BlipTextModel,
		    BlipVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blip import (
		    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFBlipForConditionalGeneration,
		    TFBlipForImageTextRetrieval,
		    TFBlipForQuestionAnswering,
		    TFBlipModel,
		    TFBlipPreTrainedModel,
		    TFBlipTextModel,
		    TFBlipVisionModel,
		)

else:
	import sys

	__UpperCamelCase						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


