

'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase=sys.maxsize       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				'bilinear'
											SCREAMING_SNAKE_CASE_				=				max_size
											SCREAMING_SNAKE_CASE_				=				short_edge_length





				def __call__(    self,     _lowercase       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				[]
											for img in imgs:
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				img.shape[:2]
																		# later: provide list and randomly choose index for resize
																		SCREAMING_SNAKE_CASE_				=				np.random.randint(self.short_edge_length[0],     self.short_edge_length[1] + 1       )
																		if size == 0:
																									return img
																		SCREAMING_SNAKE_CASE_				=				size * 1.0 / min(_lowercase,     _lowercase       )
																		if h < w:
																									SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				size, scale * w
																		else:
																									SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				scale * h, size
																		if max(_lowercase,     _lowercase       ) > self.max_size:
																									SCREAMING_SNAKE_CASE_				=				self.max_size * 1.0 / max(_lowercase,     _lowercase       )
																									SCREAMING_SNAKE_CASE_				=				newh * scale
																									SCREAMING_SNAKE_CASE_				=				neww * scale
																		SCREAMING_SNAKE_CASE_				=				int(neww + 0.5       )
																		SCREAMING_SNAKE_CASE_				=				int(newh + 0.5       )

																		if img.dtype == np.uinta:
																									SCREAMING_SNAKE_CASE_				=				Image.fromarray(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				pil_image.resize((neww, newh),     PILImageResampling.BILINEAR       )
																									SCREAMING_SNAKE_CASE_				=				np.asarray(_lowercase       )
																		else:
																									SCREAMING_SNAKE_CASE_				=				img.permute(2,     0,     1       ).unsqueeze(0       )  # 3, 0, 1)  # hw(c) -> nchw
																									SCREAMING_SNAKE_CASE_				=				nn.functional.interpolate(
																									    _lowercase,     (newh, neww),     mode=self.interp_method,     align_corners=_lowercase       ).squeeze(0       )
																		img_augs.append(_lowercase       )

											return img_augs






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],     cfg.INPUT.MAX_SIZE_TEST       )
											SCREAMING_SNAKE_CASE_				=				cfg.INPUT.FORMAT
											SCREAMING_SNAKE_CASE_				=				cfg.SIZE_DIVISIBILITY
											SCREAMING_SNAKE_CASE_				=				cfg.PAD_VALUE
											SCREAMING_SNAKE_CASE_				=				cfg.INPUT.MAX_SIZE_TEST
											SCREAMING_SNAKE_CASE_				=				cfg.MODEL.DEVICE
											SCREAMING_SNAKE_CASE_				=				torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       ),     1,     1       )
											SCREAMING_SNAKE_CASE_				=				torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       ),     1,     1       )
											SCREAMING_SNAKE_CASE_				=				lambda _lowercase       : (x - self.pixel_mean) / self.pixel_std



				def   a__					(    self,     _lowercase       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				tuple(max(_lowercase       ) for s in zip(*[img.shape for img in images]       )       )
											SCREAMING_SNAKE_CASE_				=				[im.shape[-2:] for im in images]
											SCREAMING_SNAKE_CASE_				=				[
											    nn.functional.pad(
											        _lowercase,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],     value=self.pad_value,     )
											    for size, im in zip(_lowercase,     _lowercase       )
											]

											return torch.stack(_lowercase       ), torch.tensor(_lowercase       )





				def __call__(    self,     _lowercase,     _lowercase=False       )     ->							Any:
											with torch.no_grad():
																		if not isinstance(_lowercase,     _lowercase       ):
																									SCREAMING_SNAKE_CASE_				=				[images]
																		if single_image:
																									assert len(_lowercase       ) == 1
																		for i in range(len(_lowercase       )       ):
																									if isinstance(images[i],     torch.Tensor       ):
																																images.insert(_lowercase,     images.pop(_lowercase       ).to(self.device       ).float()       )
																									elif not isinstance(images[i],     torch.Tensor       ):
																																images.insert(
																																    _lowercase,     torch.as_tensor(img_tensorize(images.pop(_lowercase       ),     input_format=self.input_format       )       )
																																    .to(self.device       )
																																    .float(),     )
            # resize smallest edge
																		SCREAMING_SNAKE_CASE_				=				torch.tensor([im.shape[:2] for im in images]       )
																		SCREAMING_SNAKE_CASE_				=				self.aug(_lowercase       )
																		# transpose images and convert to torch tensors
																		# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																		# now normalize before pad to avoid useless arithmetic
																		SCREAMING_SNAKE_CASE_				=				[self.normalizer(_lowercase       ) for x in images]
																		# now pad them to do the following operations
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.pad(_lowercase       )
																		# Normalize

																		if self.size_divisibility > 0:
																									raise NotImplementedError()
																		# pad
																		SCREAMING_SNAKE_CASE_				=				torch.true_divide(_lowercase,     _lowercase       )
																		if single_image:
																									return images[0], sizes[0], scales_yx[0]
																		else:
																									return images, sizes, scales_yx




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					str   )      -> Union[str, Any]:
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					Tuple[int, int]   )      -> int:
							assert torch.isfinite(lowerCAmelCase__   ).all(), "Box tensor contains infinite or NaN!"
							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				box_size
							tensor[:, 0].clamp_(min=0		,max=lowerCAmelCase__   )
							tensor[:, 1].clamp_(min=0		,max=lowerCAmelCase__   )
							tensor[:, 2].clamp_(min=0		,max=lowerCAmelCase__   )
							tensor[:, 3].clamp_(min=0		,max=lowerCAmelCase__   )








'''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}},
											    ],     )





