



"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    Optional[Any],    lowerCamelCase				:    Dict=7,    lowerCamelCase				:    Dict=3,    lowerCamelCase				:    Optional[int]=18,    lowerCamelCase				:    int=30,    lowerCamelCase				:    Any=400,    lowerCamelCase				:    str=True,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=None,    lowerCamelCase				:    List[str]=True,    lowerCamelCase				:    Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073],    lowerCamelCase				:    int=[0.26_862_954, 0.26_130_258, 0.27_577_711],    lowerCamelCase				:    Dict=True,    )->   Tuple:
								lowerCamelCase__						:						str						      =parent
								lowerCamelCase__						:						Dict						      =batch_size
								lowerCamelCase__						:						Optional[Any]						      =num_channels
								lowerCamelCase__						:						List[str]						      =image_size
								lowerCamelCase__						:						Tuple						      =min_resolution
								lowerCamelCase__						:						Dict						      =max_resolution
								lowerCamelCase__						:						Tuple						      =do_resize
								lowerCamelCase__						:						List[str]						      =size if size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__						:						Optional[Any]						      =do_center_crop
								lowerCamelCase__						:						int						      =crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__						:						Dict						      =do_normalize
								lowerCamelCase__						:						List[Any]						      =image_mean
								lowerCamelCase__						:						Tuple						      =image_std
								lowerCamelCase__						:						Any						      =do_convert_rgb




					def 						snake_case			(			self				:    Optional[int]	)->   Optional[int]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}





@require_torch
@require_vision
class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_						,  unittest.TestCase  ):


					'''simple docstring'''





					_a       =				OwlViTImageProcessor if is_vision_available() else None



					def 						snake_case			(			self				:    Optional[Any]	)->   Tuple:
								lowerCamelCase__						:						List[Any]						      =OwlViTImageProcessingTester(self	)



					@property
					def 						snake_case			(			self				:    int	)->   List[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						snake_case			(			self				:    Tuple	)->   int:
								lowerCamelCase__						:						str						      =self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_resize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''size'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_center_crop'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''center_crop'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_normalize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_mean'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_std'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_convert_rgb'''	)	)



					def 						snake_case			(			self				:    List[Any]	)->   str:
								lowerCamelCase__						:						Any						      =self.image_processing_class.from_dict(self.image_processor_dict	)
								self.assertEqual(image_processor.size,    {'''height''': 18, '''width''': 18}	)
								self.assertEqual(image_processor.crop_size,    {'''height''': 18, '''width''': 18}	)

								lowerCamelCase__						:						Any						      =self.image_processing_class.from_dict(self.image_processor_dict,    size=42,    crop_size=84	)
								self.assertEqual(image_processor.size,    {'''height''': 42, '''width''': 42}	)
								self.assertEqual(image_processor.crop_size,    {'''height''': 84, '''width''': 84}	)



					def 						snake_case			(			self				:    Optional[Any]	)->   Union[str, Any]:
								# Initialize image_processing
								lowerCamelCase__						:						Dict						      =self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								lowerCamelCase__						:						Optional[Any]						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase	)

								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    Image.Image	)

								# Test not batched input
								lowerCamelCase__						:						str						      =image_processing(image_inputs[0],    return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape,    (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),    )

								# Test batched
								lowerCamelCase__						:						Dict						      =image_processing(lowerCamelCase,    return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape,    (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),    )



					def 						snake_case			(			self				:    Optional[Any]	)->   Dict:
								# Initialize image_processing
								lowerCamelCase__						:						int						      =self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								lowerCamelCase__						:						Dict						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase,    numpify=lowerCamelCase	)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    np.ndarray	)

								# Test not batched input
								lowerCamelCase__						:						Optional[int]						      =image_processing(image_inputs[0],    return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape,    (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),    )

								# Test batched
								lowerCamelCase__						:						List[Any]						      =image_processing(lowerCamelCase,    return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape,    (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),    )




					def 						snake_case			(			self				:    Union[str, Any]	)->   Tuple:
								# Initialize image_processing
								lowerCamelCase__						:						Optional[Any]						      =self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								lowerCamelCase__						:						int						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase,    torchify=lowerCamelCase	)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    torch.Tensor	)

								# Test not batched input
								lowerCamelCase__						:						Tuple						      =image_processing(image_inputs[0],    return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape,    (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),    )

								# Test batched
								lowerCamelCase__						:						List[str]						      =image_processing(lowerCamelCase,    return_tensors='''pt'''	).pixel_values
								self.assertEqual(
								    encoded_images.shape,    (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),    )











'''simple docstring'''

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		):







							"""simple docstring"""
							super().__init__()

							self.register_buffer("watermark_image"		,	torch.zeros((62, 62, 4)		)		)
							__UpperCAmelCase	:							Optional[int]     =     None






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      List[PIL.Image.Image]		,	UpperCAmelCase_       :      int=None		):







							"""simple docstring"""
							# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

							__UpperCAmelCase	:							int     =     images[0].height
							__UpperCAmelCase	:							Any     =     images[0].width

							__UpperCAmelCase	:							Union[str, Any]     =     sample_size or h

							__UpperCAmelCase	:							Dict     =     min(h / sample_size		,	w / sample_size		)
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

							__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     1_024**2, img_w * img_h
							__UpperCAmelCase	:							Optional[int]     =     (Sa / Sa) ** 0.5
							__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

							if self.watermark_image_as_pil is None:
									__UpperCAmelCase	:							int     =     self.watermark_image.to(torch.uinta		).cpu().numpy()
									__UpperCAmelCase	:							Optional[int]     =     Image.fromarray(UpperCAmelCase_		,	mode="RGBA"		)
									__UpperCAmelCase	:							Optional[int]     =     watermark_image

							__UpperCAmelCase	:							List[str]     =     self.watermark_image_as_pil.resize(
							    (wm_size, wm_size)		,	PIL_INTERPOLATION["bicubic"]		,	reducing_gap=UpperCAmelCase_		)

							for pil_img in images:
									pil_img.paste(UpperCAmelCase_		,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,	mask=wm_img.split()[-1]		)

							return images




