

'''simple docstring'''

import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""image_processor""", """tokenizer"""]
				_a    	=     """AutoImageProcessor"""
				_a    	=     """AutoTokenizer"""



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

											SCREAMING_SNAKE_CASE_				=				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__(_lowercase,     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.image_processor
											SCREAMING_SNAKE_CASE_				=				False



				def __call__(    self,     *_lowercase,     **_lowercase       )     ->							Any:
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor(*_lowercase,     **_lowercase       )

											SCREAMING_SNAKE_CASE_				=				kwargs.pop('images',     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				kwargs.pop('text',     _lowercase       )
											if len(_lowercase       ) > 0:
																		SCREAMING_SNAKE_CASE_				=				args[0]
																		SCREAMING_SNAKE_CASE_				=				args[1:]

											if images is None and text is None:
																		raise ValueError('You need to specify either an `images` or `text` input to process.'       )

											if images is not None:
																		SCREAMING_SNAKE_CASE_				=				self.image_processor(_lowercase,     *_lowercase,     **_lowercase       )
											if text is not None:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     **_lowercase       )

											if text is None:
																		return inputs
											elif images is None:
																		return encodings
											else:
																		SCREAMING_SNAKE_CASE_				=				encodings['input_ids']
																		return inputs



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Tuple:
											return self.tokenizer.batch_decode(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							List[str]:
											return self.tokenizer.decode(*_lowercase,     **_lowercase       )



				@contextmanager
				def   a__					(    self       )     ->							str:
											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your images inputs, or in a separate call.'       )
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				self.tokenizer
											yield
											SCREAMING_SNAKE_CASE_				=				self.image_processor
											SCREAMING_SNAKE_CASE_				=				False



				def   a__					(    self,     _lowercase,     _lowercase=False,     _lowercase=None       )     ->							Optional[Any]:
											if added_vocab is None:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer.get_added_vocab()

											SCREAMING_SNAKE_CASE_				=				{}

											while tokens:
																		SCREAMING_SNAKE_CASE_				=				re.search(R'<s_(.*?)>',     _lowercase,     re.IGNORECASE       )
																		if start_token is None:
																									break
																		SCREAMING_SNAKE_CASE_				=				start_token.group(1       )
																		SCREAMING_SNAKE_CASE_				=				re.search(Rf"""</s_{key}>""",     _lowercase,     re.IGNORECASE       )
																		SCREAMING_SNAKE_CASE_				=				start_token.group()
																		if end_token is None:
																									SCREAMING_SNAKE_CASE_				=				tokens.replace(_lowercase,     ''       )
																		else:
																									SCREAMING_SNAKE_CASE_				=				end_token.group()
																									SCREAMING_SNAKE_CASE_				=				re.escape(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				re.escape(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				re.search(f"""{start_token_escaped}(.*?){end_token_escaped}""",     _lowercase,     re.IGNORECASE       )
																									if content is not None:
																																SCREAMING_SNAKE_CASE_				=				content.group(1       ).strip()
																																if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																							SCREAMING_SNAKE_CASE_				=				self.tokenajson(_lowercase,     is_inner_value=_lowercase,     added_vocab=_lowercase       )
																																							if value:
																																														if len(_lowercase       ) == 1:
																																																					SCREAMING_SNAKE_CASE_				=				value[0]
																																														SCREAMING_SNAKE_CASE_				=				value
																																else:  # leaf nodes
																																							SCREAMING_SNAKE_CASE_				=				[]
																																							for leaf in content.split(R'<sep/>'       ):
																																														SCREAMING_SNAKE_CASE_				=				leaf.strip()
																																														if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																					SCREAMING_SNAKE_CASE_				=				leaf[1:-2]  # for categorical special tokens
																																														output[key].append(_lowercase       )
																																							if len(output[key]       ) == 1:
																																														SCREAMING_SNAKE_CASE_				=				output[key][0]

																									SCREAMING_SNAKE_CASE_				=				tokens[tokens.find(_lowercase       ) + len(_lowercase       ) :].strip()
																									if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																return [output] + self.tokenajson(tokens[6:],     is_inner_value=_lowercase,     added_vocab=_lowercase       )

											if len(_lowercase       ):
																		return [output] if is_inner_value else output
											else:
																		return [] if is_inner_value else {"text_sequence": tokens}



				@property
				def   a__					(    self       )     ->							Dict:
											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',     _lowercase,     )
											return self.image_processor_class





				@property
				def   a__					(    self       )     ->							List[str]:
											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',     _lowercase,     )
											return self.image_processor








'''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    snake_case     :






											"""simple docstring"""



											@staticmethod
											def   a__					(    *_lowercase,     **_lowercase       )     ->							Tuple:
																		pass






@is_pipeline_test
@require_vision
@require_torch
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

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



				def   a__					(    self,     _lowercase,     _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				object_detector(examples[0],     threshold=0.0       )

											SCREAMING_SNAKE_CASE_				=				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       )     ->							Union[str, Any]:
											pass



				@require_torch
				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    './tests/fixtures/tests_samples/COCO/000000039769.png',     candidate_labels=['cat', 'remote', 'couch'],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											        {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    [
											        {
											            'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        }
											    ],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											            {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        ]
											    ],     )



				@require_torch
				@slow
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											        {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											        {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				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_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											        [
											            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, '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[Any]:
											pass



				@require_torch
				@slow
				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				0.2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				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_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											    ],     )





				@require_torch
				@slow
				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				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_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											    ],     )





