






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					int ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Dict[str, int] = None ,  __lowerCamelCase				:					int = 32 ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Union[int, float] = 1 / 255 ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073] ,  __lowerCamelCase				:					Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711] ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Any=7 ,  __lowerCamelCase				:					str=30 ,  __lowerCamelCase				:					Optional[Any]=400 ,  __lowerCamelCase				:					List[Any]=3 ,  ):
		UpperCamelCase     :Dict          =	parent
		UpperCamelCase     :Tuple          =	do_resize
		UpperCamelCase     :str          =	size if size is not None else {"""shortest_edge""": 288}
		UpperCamelCase     :Optional[Any]          =	size_divisor
		UpperCamelCase     :str          =	do_rescale
		UpperCamelCase     :Any          =	rescale_factor
		UpperCamelCase     :Tuple          =	do_normalize
		UpperCamelCase     :Dict          =	do_center_crop
		UpperCamelCase     :List[str]          =	image_mean
		UpperCamelCase     :int          =	image_std
		UpperCamelCase     :Tuple          =	do_pad
		UpperCamelCase     :Union[str, Any]          =	batch_size
		UpperCamelCase     :int          =	num_channels
		UpperCamelCase     :Optional[Any]          =	min_resolution
		UpperCamelCase     :Tuple          =	max_resolution





	def 						_A (		self				:					str	):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "size_divisor": self.size_divisor,
		}





	def 						_A (		self				:					int ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Tuple=False	):
		if not batched:
			UpperCamelCase     :str          =	self.size["""shortest_edge"""]
			UpperCamelCase     :Union[str, Any]          =	image_inputs[0]
			if isinstance(__lowerCamelCase ,  Image.Image	):
				UpperCamelCase     ,      UpperCamelCase     :str          =	image.size
			else:
				UpperCamelCase     ,      UpperCamelCase     :Any          =	image.shape[1], image.shape[2]
			UpperCamelCase     :Optional[Any]          =	size / min(__lowerCamelCase ,  __lowerCamelCase	)
			if h < w:
				UpperCamelCase     ,      UpperCamelCase     :int          =	size, scale * w
			else:
				UpperCamelCase     ,      UpperCamelCase     :Dict          =	scale * h, size

			UpperCamelCase     :Optional[int]          =	int((1_333 / 800) * size	)
			if max(__lowerCamelCase ,  __lowerCamelCase	) > max_size:
				UpperCamelCase     :Optional[Any]          =	max_size / max(__lowerCamelCase ,  __lowerCamelCase	)
				UpperCamelCase     :Tuple          =	newh * scale
				UpperCamelCase     :Union[str, Any]          =	neww * scale

			UpperCamelCase     ,      UpperCamelCase     :Tuple          =	int(newh + 0.5	), int(neww + 0.5	)
			UpperCamelCase     ,      UpperCamelCase     :List[Any]          =	(
			    newh // self.size_divisor * self.size_divisor,
			    neww // self.size_divisor * self.size_divisor,
			)

		else:
			UpperCamelCase     :List[Any]          =	[]
			for image in image_inputs:
				UpperCamelCase     ,      UpperCamelCase     :List[str]          =	self.get_expected_values([image]	)
				expected_values.append((expected_height, expected_width)	)
			UpperCamelCase     :Optional[Any]          =	max(__lowerCamelCase ,  key=lambda __lowerCamelCase	: item[0]	)[0]
			UpperCamelCase     :Union[str, Any]          =	max(__lowerCamelCase ,  key=lambda __lowerCamelCase	: item[1]	)[1]

		return expected_height, expected_width

@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     Tuple     	=  BridgeTowerImageProcessor if is_vision_available() else None





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :Optional[Any]          =	BridgeTowerImageProcessingTester(self	)





	@property
	def 						_A (		self				:					Any	):
		return self.image_processor_tester.prepare_image_processor_dict()





	def 						_A (		self				:					str	):
		UpperCamelCase     :List[str]          =	self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_mean"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_std"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_normalize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_resize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """size"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """size_divisor"""	)	)





	def 						_A (		self				:					int	):
		pass





	def 						_A (		self				:					str	):
		# Initialize image processor
		UpperCamelCase     :int          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		UpperCamelCase     :Tuple          =	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  Image.Image	)

		# Test not batched input
		UpperCamelCase     :Optional[Any]          =	image_processing(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values

		UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	self.image_processor_tester.get_expected_values(__lowerCamelCase	)
		self.assertEqual(
		    encoded_images.shape ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,  )

		# Test batched
		UpperCamelCase     :Tuple          =	image_processing(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values

		UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	self.image_processor_tester.get_expected_values(__lowerCamelCase ,  batched=__lowerCamelCase	)
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    ) ,  )





	def 						_A (		self				:					Union[str, Any]	):
		# Initialize image processor
		UpperCamelCase     :int          =	self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		UpperCamelCase     :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
		UpperCamelCase     :Dict          =	image_processing(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values

		UpperCamelCase     ,      UpperCamelCase     :Dict          =	self.image_processor_tester.get_expected_values(__lowerCamelCase	)
		self.assertEqual(
		    encoded_images.shape ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,  )

		# Test batched
		UpperCamelCase     :List[str]          =	image_processing(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values

		UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	self.image_processor_tester.get_expected_values(__lowerCamelCase ,  batched=__lowerCamelCase	)
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    ) ,  )





	def 						_A (		self				:					Dict	):
		# Initialize image processor
		UpperCamelCase     :Dict          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		UpperCamelCase     :Tuple          =	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
		UpperCamelCase     :List[Any]          =	image_processing(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values

		UpperCamelCase     ,      UpperCamelCase     :Dict          =	self.image_processor_tester.get_expected_values(__lowerCamelCase	)
		self.assertEqual(
		    encoded_images.shape ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,  )

		# Test batched
		UpperCamelCase     :List[str]          =	image_processing(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values

		UpperCamelCase     ,      UpperCamelCase     :Any          =	self.image_processor_tester.get_expected_values(__lowerCamelCase ,  batched=__lowerCamelCase	)
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    ) ,  )








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 ViTImageProcessor

class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any=13 ,  __lowerCamelCase				:					Dict=3 ,  __lowerCamelCase				:					int=224 ,  __lowerCamelCase				:					Any=30 ,  __lowerCamelCase				:					Tuple=400 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Any=True ,  __lowerCamelCase				:					Dict=[0.5, 0.5, 0.5] ,  __lowerCamelCase				:					List[Any]=[0.5, 0.5, 0.5] ,  ):
		UpperCamelCase     :List[Any]          =	size if size is not None else {"""height""": 18, """width""": 18}
		UpperCamelCase     :str          =	parent
		UpperCamelCase     :Optional[int]          =	batch_size
		UpperCamelCase     :Dict          =	num_channels
		UpperCamelCase     :str          =	image_size
		UpperCamelCase     :Dict          =	min_resolution
		UpperCamelCase     :str          =	max_resolution
		UpperCamelCase     :Union[str, Any]          =	do_resize
		UpperCamelCase     :Optional[Any]          =	size
		UpperCamelCase     :Any          =	do_normalize
		UpperCamelCase     :Optional[Any]          =	image_mean
		UpperCamelCase     :Tuple          =	image_std





	def 						_A (		self				:					int	):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "size": self.size,
		}

@require_torch
@require_vision
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     List[Any]     	=  ViTImageProcessor if is_vision_available() else None





	def 						_A (		self				:					str	):
		UpperCamelCase     :Tuple          =	EfficientFormerImageProcessorTester(self	)





	@property
	def 						_A (		self				:					List[str]	):
		return self.image_proc_tester.prepare_image_processor_dict()





	def 						_A (		self				:					int	):
		UpperCamelCase     :List[Any]          =	self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_mean"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_std"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_normalize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_resize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """size"""	)	)





	def 						_A (		self				:					Optional[int]	):
		pass





	def 						_A (		self				:					str	):
		# Initialize image_processor
		UpperCamelCase     :Union[str, Any]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		UpperCamelCase     :Union[str, Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  Image.Image	)

		# Test not batched input
		UpperCamelCase     :List[str]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :List[Any]          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					Union[str, Any]	):
		# Initialize image_processor
		UpperCamelCase     :Optional[int]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		UpperCamelCase     :List[Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  numpify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  np.ndarray	)

		# Test not batched input
		UpperCamelCase     :Dict          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :Tuple          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					List[Any]	):
		# Initialize image_processor
		UpperCamelCase     :List[str]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		UpperCamelCase     :Any          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  torchify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  torch.Tensor	)

		# Test not batched input
		UpperCamelCase     :List[Any]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :str          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )
