"""simple docstring"""




import json
import os
import tempfile

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


if is_torch_available():
					import numpy as np
					import torch

if is_vision_available():
					from PIL import Image



def 				__a       (			_lowercase							, _lowercase=False							, _lowercase=False							, _lowercase=False						):







					"""simple docstring"""





					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					lowerCamelCase__		:   int  				=					[]
					for i in range(image_processor_tester.batch_size						):
										if equal_resolution:
															lowerCamelCase__		:   int  				=					image_processor_tester.max_resolution
										else:
															# To avoid getting image width/height 0
															lowerCamelCase__		:   int  				=					image_processor_tester.min_resolution
															if getattr(UpperCamelCase_							, '''size_divisor'''							, UpperCamelCase_						):
																				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
																				lowerCamelCase__		:   Optional[int]  				=					max(image_processor_tester.size_divisor							, UpperCamelCase_						)
															lowerCamelCase__				,						lowerCamelCase__		:   Any  				=					np.random.choice(np.arange(UpperCamelCase_							, image_processor_tester.max_resolution						)							, 2						)
										image_inputs.append(
										    np.random.randint(255							, size=(image_processor_tester.num_channels, width, height)							, dtype=np.uinta						)						)

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										lowerCamelCase__		:   Any  				=					[Image.fromarray(np.moveaxis(UpperCamelCase_							, 0							, -1						)						) for image in image_inputs]

					if torchify:
										lowerCamelCase__		:   int  				=					[torch.from_numpy(UpperCamelCase_						) for image in image_inputs]

					return image_inputs



def 				__a       (			_lowercase							, _lowercase=10							, _lowercase=10							, _lowercase=False							, _lowercase=False						):







					"""simple docstring"""





					lowerCamelCase__		:   int  				=					[]
					for i in range(image_processor_tester.num_frames						):
										video.append(np.random.randint(255							, size=(image_processor_tester.num_channels, width, height)							, dtype=np.uinta						)						)

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										lowerCamelCase__		:   Union[str, Any]  				=					[Image.fromarray(np.moveaxis(UpperCamelCase_							, 0							, -1						)						) for frame in video]

					if torchify:
										lowerCamelCase__		:   str  				=					[torch.from_numpy(UpperCamelCase_						) for frame in video]

					return video



def 				__a       (			_lowercase							, _lowercase=False							, _lowercase=False							, _lowercase=False						):







					"""simple docstring"""





					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					lowerCamelCase__		:   Any  				=					[]
					for i in range(image_processor_tester.batch_size						):
										if equal_resolution:
															lowerCamelCase__		:   str  				=					image_processor_tester.max_resolution
										else:
															lowerCamelCase__				,						lowerCamelCase__		:   Union[str, Any]  				=					np.random.choice(
															    np.arange(image_processor_tester.min_resolution							, image_processor_tester.max_resolution						)							, 2						)
															lowerCamelCase__		:   int  				=					prepare_video(
															    image_processor_tester=UpperCamelCase_							, width=UpperCamelCase_							, height=UpperCamelCase_							, numpify=UpperCamelCase_							, torchify=UpperCamelCase_							, )
										video_inputs.append(UpperCamelCase_						)

					return video_inputs





class        __SCREAMING_SNAKE_CASE     :
				UpperCAmelCase	=      None







				def   lowercase_		(    self    :Optional[int]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					self.image_processing_class(**self.image_processor_dict  )
									lowerCamelCase__		:   Optional[Any]  				=					json.loads(image_processor.to_json_string()  )
									for key, value in self.image_processor_dict.items():
														self.assertEqual(obj[key]      ,UpperCAmelCase__  )







				def   lowercase_		(    self    :Union[str, Any]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					self.image_processing_class(**self.image_processor_dict  )

									with tempfile.TemporaryDirectory() as tmpdirname:
														lowerCamelCase__		:   int  				=					os.path.join(UpperCAmelCase__      ,'''image_processor.json'''  )
														image_processor_first.to_json_file(UpperCAmelCase__  )
														lowerCamelCase__		:   Dict  				=					self.image_processing_class.from_json_file(UpperCAmelCase__  )

									self.assertEqual(image_processor_second.to_dict()      ,image_processor_first.to_dict()  )







				def   lowercase_		(    self    :Any  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					self.image_processing_class(**self.image_processor_dict  )

									with tempfile.TemporaryDirectory() as tmpdirname:
														lowerCamelCase__		:   Union[str, Any]  				=					image_processor_first.save_pretrained(UpperCAmelCase__  )[0]
														check_json_file_has_correct_format(UpperCAmelCase__  )
														lowerCamelCase__		:   List[Any]  				=					self.image_processing_class.from_pretrained(UpperCAmelCase__  )

									self.assertEqual(image_processor_second.to_dict()      ,image_processor_first.to_dict()  )







				def   lowercase_		(    self    :Union[str, Any]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					self.image_processing_class()
									self.assertIsNotNone(UpperCAmelCase__  )







				@require_torch
				@require_vision
				def   lowercase_		(    self    :Any  )      ->					Any:




									"""simple docstring"""



									if self.test_cast_dtype is not None:
														# Initialize image_processor
														lowerCamelCase__		:   Any  				=					self.image_processing_class(**self.image_processor_dict  )

														# create random PyTorch tensors
														lowerCamelCase__		:   Union[str, Any]  				=					prepare_image_inputs(self.image_processor_tester      ,equal_resolution=UpperCAmelCase__      ,torchify=UpperCAmelCase__  )

														lowerCamelCase__		:   Optional[int]  				=					image_processor(UpperCAmelCase__      ,return_tensors='''pt'''  )
														# for layoutLM compatiblity
														self.assertEqual(encoding.pixel_values.device      ,torch.device('''cpu'''  )  )
														self.assertEqual(encoding.pixel_values.dtype      ,torch.floataa  )

														lowerCamelCase__		:   str  				=					image_processor(UpperCAmelCase__      ,return_tensors='''pt'''  ).to(torch.floataa  )
														self.assertEqual(encoding.pixel_values.device      ,torch.device('''cpu'''  )  )
														self.assertEqual(encoding.pixel_values.dtype      ,torch.floataa  )

														lowerCamelCase__		:   Union[str, Any]  				=					image_processor(UpperCAmelCase__      ,return_tensors='''pt'''  ).to('''cpu'''      ,torch.bfloataa  )
														self.assertEqual(encoding.pixel_values.device      ,torch.device('''cpu'''  )  )
														self.assertEqual(encoding.pixel_values.dtype      ,torch.bfloataa  )

														with self.assertRaises(UpperCAmelCase__  ):
																			lowerCamelCase__		:   List[Any]  				=					image_processor(UpperCAmelCase__      ,return_tensors='''pt'''  ).to(torch.bfloataa      ,'''cpu'''  )

														# Try with text + image feature
														lowerCamelCase__		:   Optional[Any]  				=					image_processor(UpperCAmelCase__      ,return_tensors='''pt'''  )
														encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]  )}  )
														lowerCamelCase__		:   List[str]  				=					encoding.to(torch.floataa  )

														self.assertEqual(encoding.pixel_values.device      ,torch.device('''cpu'''  )  )
														self.assertEqual(encoding.pixel_values.dtype      ,torch.floataa  )
														self.assertEqual(encoding.input_ids.dtype      ,torch.long  )









"""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    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Dict,   UpperCAmelCase__				:     Optional[int]=7,   UpperCAmelCase__				:     List[str]=3,   UpperCAmelCase__				:     List[Any]=1_8,   UpperCAmelCase__				:     Any=3_0,   UpperCAmelCase__				:     str=4_0_0,   UpperCAmelCase__				:     Union[str, Any]=True,   UpperCAmelCase__				:     Dict=None,   UpperCAmelCase__				:     int=True,   UpperCAmelCase__				:     str=None,   UpperCAmelCase__				:     Dict=True,   UpperCAmelCase__				:     str=[0.48_145_466, 0.4_578_275, 0.40_821_073],   UpperCAmelCase__				:     Dict=[0.26_862_954, 0.26_130_258, 0.27_577_711],   UpperCAmelCase__				:     Any=True,   ):
    __lowercase								=		parent
    __lowercase								=		batch_size
    __lowercase								=		num_channels
    __lowercase								=		image_size
    __lowercase								=		min_resolution
    __lowercase								=		max_resolution
    __lowercase								=		do_resize
    __lowercase								=		size if size is not None else {"height": 1_8, "width": 1_8}
    __lowercase								=		do_center_crop
    __lowercase								=		crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
    __lowercase								=		do_normalize
    __lowercase								=		image_mean
    __lowercase								=		image_std
    __lowercase								=		do_convert_rgb



 def    _lowercase					(     self				:     List[str]      ):
    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    _lowerCAmelCase    (      lowercase	,unittest.TestCase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      OwlViTImageProcessor if is_vision_available() else None


 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		OwlViTImageProcessingTester(self      )


 @property
 def    _lowercase					(     self				:     int      ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def    _lowercase					(     self				:     Tuple      ):
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_resize"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "size"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_center_crop"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "center_crop"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_normalize"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "image_mean"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "image_std"      )      )
    self.assertTrue(hasattr(UpperCAmelCase__,   "do_convert_rgb"      )      )


 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size,   {"height": 1_8, "width": 1_8}      )
    self.assertEqual(image_processor.crop_size,   {"height": 1_8, "width": 1_8}      )

    __lowercase								=		self.image_processing_class.from_dict(self.image_processor_dict,   size=4_2,   crop_size=8_4      )
    self.assertEqual(image_processor.size,   {"height": 4_2, "width": 4_2}      )
    self.assertEqual(image_processor.crop_size,   {"height": 8_4, "width": 8_4}      )


 def    _lowercase					(     self				:     Any      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__      )

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

    # Test not batched input
    __lowercase								=		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
    __lowercase								=		image_processing(UpperCAmelCase__,   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    _lowercase					(     self				:     Optional[Any]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__,   numpify=UpperCAmelCase__      )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   np.ndarray      )

    # Test not batched input
    __lowercase								=		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
    __lowercase								=		image_processing(UpperCAmelCase__,   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    _lowercase					(     self				:     List[str]      ):
    # Initialize image_processing
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__,   torchify=UpperCAmelCase__      )
    for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase__,   torch.Tensor      )

    # Test not batched input
    __lowercase								=		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
    __lowercase								=		image_processing(UpperCAmelCase__,   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"],
        ),   )




