




"""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     lowercase (      unittest.TestCase):







				def __init__(							self				:   Tuple ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   Any=7 ,       _lowerCamelCase				:   Tuple=3 ,       _lowerCamelCase				:   Optional[Any]=18 ,       _lowerCamelCase				:   int=30 ,       _lowerCamelCase				:   Union[str, Any]=4_00 ,       _lowerCamelCase				:   Union[str, Any]=True ,       _lowerCamelCase				:   Tuple=None ,       _lowerCamelCase				:   Optional[Any]=True ,       _lowerCamelCase				:   List[str]=None ,       _lowerCamelCase				:   Tuple=True ,       _lowerCamelCase				:   List[str]=[0.48145466, 0.4578275, 0.40821073] ,       _lowerCamelCase				:   Dict=[0.26862954, 0.26130258, 0.27577711] ,       _lowerCamelCase				:   Dict=True ,       ):



					"""simple docstring"""

					A_					:			List[str]						=						parent
					A_					:			Union[str, Any]						=						batch_size
					A_					:			Optional[int]						=						num_channels
					A_					:			int						=						image_size
					A_					:			Any						=						min_resolution
					A_					:			int						=						max_resolution
					A_					:			Tuple						=						do_resize
					A_					:			int						=						size if size is not None else {'''height''': 18, '''width''': 18}
					A_					:			str						=						do_center_crop
					A_					:			Tuple						=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
					A_					:			List[Any]						=						do_normalize
					A_					:			Any						=						image_mean
					A_					:			Union[str, Any]						=						image_std
					A_					:			Tuple						=						do_convert_rgb







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					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     lowercase (      __UpperCAmelCase       ,		unittest.TestCase):
				__lowerCAmelCase      :      int					    =				OwlViTImageProcessor if is_vision_available() else None







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[Any]						=						OwlViTImageProcessingTester(self    )







				@property
				def    a_					(							self				:   List[Any]    ):



					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					A_					:			Any						=						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    a_					(							self				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			str						=						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}    )

					A_					:			Tuple						=						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    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.image_processing_class(**self.image_processor_dict    )
					# create random PIL images
					A_					:			List[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
					A_					:			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
					A_					:			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'''],
					    ) ,       )







				def    a_					(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.image_processing_class(**self.image_processor_dict    )
					# create random numpy tensors
					A_					:			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
					A_					:			List[Any]						=						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
					A_					:			Tuple						=						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    a_					(							self				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			Dict						=						self.image_processing_class(**self.image_processor_dict    )
					# create random PyTorch tensors
					A_					:			List[str]						=						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
					A_					:			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
					A_					:			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 .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims





