


"""simple docstring"""


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 			lowerCamelCase__						(   UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     BarthezTokenizer
						lowerCAmelCase       =     BarthezTokenizerFast
						lowerCAmelCase       =     True
						lowerCAmelCase       =     True







						def __a						(							self   :    int		):
								super().setUp()

								A					       = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'		)
								tokenizer.save_pretrained(self.tmpdirname		)
								tokenizer.save_pretrained(self.tmpdirname			,			legacy_format=_lowercase		)
								A					       = tokenizer







						def __a						(							self   :    Any		):
								A					       = '<pad>'
								A					       = 1

								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   :    List[str]		):
								A					       = list(self.get_tokenizer().get_vocab().keys()		)

								self.assertEqual(vocab_keys[0]			,			'<s>'		)
								self.assertEqual(vocab_keys[1]			,			'<pad>'		)
								self.assertEqual(vocab_keys[-1]			,			'<mask>'		)
								self.assertEqual(len(_lowercase		)			,			101_122		)







						def __a						(							self   :    List[Any]		):
								self.assertEqual(self.get_tokenizer().vocab_size			,			101_122		)







						@require_torch
						def __a						(							self   :    str		):
								A					       = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								A					       = [0, 57, 3_018, 70_307, 91, 2]

								A					       = self.tokenizer(
								    _lowercase			,			max_length=len(_lowercase		)			,			padding=_lowercase			,			truncation=_lowercase			,			return_tensors='pt'		)
								self.assertIsInstance(_lowercase			,			_lowercase		)

								self.assertEqual((2, 6)			,			batch.input_ids.shape		)
								self.assertEqual((2, 6)			,			batch.attention_mask.shape		)
								A					       = batch.input_ids.tolist()[0]
								self.assertListEqual(_lowercase			,			_lowercase		)







						def __a						(							self   :    Union[str, Any]		):
								if not self.test_rust_tokenizer:
										return

								A					       = self.get_tokenizer()
								A					       = self.get_rust_tokenizer()

								A					       = 'I was born in 92000, and this is falsé.'

								A					       = tokenizer.tokenize(_lowercase		)
								A					       = rust_tokenizer.tokenize(_lowercase		)
								self.assertListEqual(_lowercase			,			_lowercase		)

								A					       = tokenizer.encode(_lowercase			,			add_special_tokens=_lowercase		)
								A					       = rust_tokenizer.encode(_lowercase			,			add_special_tokens=_lowercase		)
								self.assertListEqual(_lowercase			,			_lowercase		)

								A					       = self.get_rust_tokenizer()
								A					       = tokenizer.encode(_lowercase		)
								A					       = rust_tokenizer.encode(_lowercase		)
								self.assertListEqual(_lowercase			,			_lowercase		)







						@slow
						def __a						(							self   :    int		):
								# fmt: off
								A					       = {'input_ids': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], '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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# moussaKam/mbarthez is a french model. So we also use french texts.
								A					       = [
								    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
								    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
								    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
								    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
								    'telles que la traduction et la synthèse de texte.',
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=_lowercase			,			model_name='moussaKam/mbarthez'			,			revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'			,			sequences=_lowercase			,			)









"""simple docstring"""


import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:



	class 			lowerCamelCase__						:







							@staticmethod
							def __a						(							*_lowercase   :    int			,			**_lowercase   :    Optional[int]		):
									pass



@is_pipeline_test
@require_vision
@require_torch
class 			lowerCamelCase__						(   unittest.TestCase			):
						lowerCAmelCase       =     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







						def __a						(							self   :    int			,			_lowercase   :    Optional[Any]			,			_lowercase   :    int			,			_lowercase   :    Union[str, Any]		):
								A					       = pipeline(
								    'zero-shot-object-detection'			,			model='hf-internal-testing/tiny-random-owlvit-object-detection'		)

								A					       = [
								    {
								        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
								        'candidate_labels': ['cat', 'remote', 'couch'],
								    }
								]
								return object_detector, examples







						def __a						(							self   :    Tuple			,			_lowercase   :    int			,			_lowercase   :    Tuple		):
								A					       = object_detector(examples[0]			,			threshold=0.0		)

								A					       = len(_lowercase		)
								self.assertGreater(_lowercase			,			0		)
								self.assertEqual(
								    _lowercase			,			[
								        {
								            'score': ANY(_lowercase		),
								            'label': ANY(_lowercase		),
								            'box': {'xmin': ANY(_lowercase		), 'ymin': ANY(_lowercase		), 'xmax': ANY(_lowercase		), 'ymax': ANY(_lowercase		)},
								        }
								        for i in range(_lowercase		)
								    ]			,			)







						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'		)
						def __a						(							self   :    Optional[int]		):
								pass







						@require_torch
						def __a						(							self   :    Optional[Any]		):
								A					       = pipeline(
								    'zero-shot-object-detection'			,			model='hf-internal-testing/tiny-random-owlvit-object-detection'		)

								A					       = object_detector(
								    './tests/fixtures/tests_samples/COCO/000000039769.png'			,			candidate_labels=['cat', 'remote', 'couch']			,			threshold=0.6_4			,			)

								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								        {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								        {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								        {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								        {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								        {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								        {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								        {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
								        {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								    ]			,			)

								A					       = object_detector(
								    [
								        {
								            'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
								            'candidate_labels': ['cat', 'remote', 'couch'],
								        }
								    ]			,			threshold=0.6_4			,			)

								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        [
								            {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								            {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								            {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
								            {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								            {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								            {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
								            {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								            {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
								            {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
								        ]
								    ]			,			)







						@require_torch
						@slow
						def __a						(							self   :    List[str]		):
								A					       = pipeline('zero-shot-object-detection'		)

								A					       = object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'			,			candidate_labels=['cat', 'remote', 'couch']			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								        {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								        {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
								        {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
								    ]			,			)

								A					       = object_detector(
								    [
								        {
								            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
								            'candidate_labels': ['cat', 'remote', 'couch'],
								        },
								        {
								            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
								            'candidate_labels': ['cat', 'remote', 'couch'],
								        },
								    ]			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        [
								            {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								            {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								            {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								            {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
								            {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
								        ],
								        [
								            {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								            {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								            {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								            {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
								            {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
								        ],
								    ]			,			)







						@require_tf
						@unittest.skip('Zero Shot Object Detection not implemented in TF'		)
						def __a						(							self   :    Optional[int]		):
								pass







						@require_torch
						@slow
						def __a						(							self   :    Optional[Any]		):
								A					       = 0.2
								A					       = pipeline('zero-shot-object-detection'		)

								A					       = object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'			,			candidate_labels=['cat', 'remote', 'couch']			,			threshold=_lowercase			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								        {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
								    ]			,			)







						@require_torch
						@slow
						def __a						(							self   :    Optional[int]		):
								A					       = 2
								A					       = pipeline('zero-shot-object-detection'		)

								A					       = object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'			,			candidate_labels=['cat', 'remote', 'couch']			,			top_k=_lowercase			,			)
								self.assertEqual(
								    nested_simplify(_lowercase			,			decimals=4		)			,			[
								        {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
								        {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
								    ]			,			)





