






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 				__a				(	unittest.TestCase    ):





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__=7			,     UpperCamelCase__=3			,     UpperCamelCase__=18			,     UpperCamelCase__=30			,     UpperCamelCase__=400			,     UpperCamelCase__=True			,     UpperCamelCase__=None			,     UpperCamelCase__=True			,     UpperCamelCase__=None			,     UpperCamelCase__=True			,     UpperCamelCase__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,     UpperCamelCase__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,     UpperCamelCase__=True			,     ):
						SCREAMING_SNAKE_CASE_ :						Dict    				= parent
						SCREAMING_SNAKE_CASE_ :						Dict    				= batch_size
						SCREAMING_SNAKE_CASE_ :						str    				= num_channels
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= image_size
						SCREAMING_SNAKE_CASE_ :						Tuple    				= min_resolution
						SCREAMING_SNAKE_CASE_ :						Dict    				= max_resolution
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= do_resize
						SCREAMING_SNAKE_CASE_ :						Dict    				= size if size is not None else {'height': 18, 'width': 18}
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= do_center_crop
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= crop_size if crop_size is not None else {'height': 18, 'width': 18}
						SCREAMING_SNAKE_CASE_ :						List[str]    				= do_normalize
						SCREAMING_SNAKE_CASE_ :						Tuple    				= image_mean
						SCREAMING_SNAKE_CASE_ :						str    				= image_std
						SCREAMING_SNAKE_CASE_ :						int    				= do_convert_rgb






		def 	__snake_case      (    self							):
						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 				__a				(	SCREAMING_SNAKE_CASE__			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Tuple					     =    OwlViTImageProcessor if is_vision_available() else None


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Dict    				= OwlViTImageProcessingTester(self							)


		@property
		def 	__snake_case      (    self							):
						return self.image_processor_tester.prepare_image_processor_dict()


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(a_			,     'do_resize'							)							)
						self.assertTrue(hasattr(a_			,     'size'							)							)
						self.assertTrue(hasattr(a_			,     'do_center_crop'							)							)
						self.assertTrue(hasattr(a_			,     'center_crop'							)							)
						self.assertTrue(hasattr(a_			,     'do_normalize'							)							)
						self.assertTrue(hasattr(a_			,     'image_mean'							)							)
						self.assertTrue(hasattr(a_			,     'image_std'							)							)
						self.assertTrue(hasattr(a_			,     'do_convert_rgb'							)							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[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}							)

						SCREAMING_SNAKE_CASE_ :						int    				= 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							):
						SCREAMING_SNAKE_CASE_ :						str    				= self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						SCREAMING_SNAKE_CASE_ :						str    				= prepare_image_inputs(self.image_processor_tester			,     equal_resolution=a_							)

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

						# Test not batched input
						SCREAMING_SNAKE_CASE_ :						Optional[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
						SCREAMING_SNAKE_CASE_ :						List[str]    				= image_processing(a_			,     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							):
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE_ :						str    				= prepare_image_inputs(self.image_processor_tester			,     equal_resolution=a_			,     numpify=a_							)
						for image in image_inputs:
										self.assertIsInstance(a_			,     np.ndarray							)

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

						# Test not batched input
						SCREAMING_SNAKE_CASE_ :						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
						SCREAMING_SNAKE_CASE_ :						str    				= image_processing(a_			,     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 Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather



@overload
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					None )		->       torch.Tensor:





   '''simple docstring'''







   ...



@overload
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor )		->       Tuple[torch.Tensor, torch.Tensor]:





   '''simple docstring'''







   ...



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					str )		->       int:





   '''simple docstring'''







   UpperCamelCase__			    =    aatype == rc.restype_order["G"]
   UpperCamelCase__			    =    rc.atom_order["CA"]
   UpperCamelCase__			    =    rc.atom_order["CB"]
   UpperCamelCase__			    =    torch.where(
       is_gly[..., None].expand(*((-1,) * len(is_gly.shape ))     ,		3 )     ,		all_atom_positions[..., ca_idx, :]     ,		all_atom_positions[..., cb_idx, :]     ,		)

   if all_atom_masks is not None:
      UpperCamelCase__			    =    torch.where(
          _UpperCamelCase     ,		all_atom_masks[..., ca_idx]     ,		all_atom_masks[..., cb_idx]     ,		)
      return pseudo_beta, pseudo_beta_mask
   else:
      return pseudo_beta



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					Dict[str, torch.Tensor] )		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    batched_gather(
       _UpperCamelCase     ,		batch["residx_atom37_to_atom14"]     ,		dim=-2     ,		no_batch_dims=len(atomaa.shape[:-2] )     ,		)

   UpperCamelCase__			    =    atomaa_data * batch["atom37_atom_exists"][..., None]

   return atomaa_data



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict[str, torch.Tensor] )		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    template_feats["template_aatype"]
   UpperCamelCase__			    =    template_feats["template_torsion_angles_sin_cos"]
   UpperCamelCase__			    =    template_feats["template_alt_torsion_angles_sin_cos"]
   UpperCamelCase__			    =    template_feats["template_torsion_angles_mask"]
   UpperCamelCase__			    =    torch.cat(
       [
           nn.functional.one_hot(_UpperCamelCase     ,		22 ),
           torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]     ,		14 ),
           alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]     ,		14 ),
           torsion_angles_mask,
       ]     ,		dim=-1     ,		)

   return template_angle_feat



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict[str, torch.Tensor]     ,		_UpperCamelCase   :					torch.types.Number     ,		_UpperCamelCase   :					torch.types.Number     ,		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					bool = False     ,		_UpperCamelCase   :					float = 1e-20     ,		_UpperCamelCase   :					float = 1e8     ,		)		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    batch["template_pseudo_beta_mask"]
   UpperCamelCase__			    =    template_mask[..., None] * template_mask[..., None, :]

   # Compute distogram (this seems to differ slightly from Alg. 5)
   UpperCamelCase__			    =    batch["template_pseudo_beta"]
   UpperCamelCase__			    =    torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2     ,		dim=-1     ,		keepdim=_UpperCamelCase )
   UpperCamelCase__			    =    torch.linspace(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		device=tpb.device ) ** 2
   UpperCamelCase__			    =    torch.cat([lower[1:], lower.new_tensor([inf] )]     ,		dim=-1 )
   UpperCamelCase__			    =    ((dgram > lower) * (dgram < upper)).type(dgram.dtype )

   UpperCamelCase__			    =    [dgram, template_mask_ad[..., None]]

   UpperCamelCase__			    =    nn.functional.one_hot(
       batch["template_aatype"]     ,		rc.restype_num + 2     ,		)

   UpperCamelCase__			    =    batch["template_aatype"].shape[-1]
   to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]     ,		_UpperCamelCase     ,		-1     ,		-1 ) )
   to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]     ,		-1     ,		_UpperCamelCase     ,		-1 ) )

   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    [rc.atom_order[a] for a in ["N", "CA", "C"]]
   UpperCamelCase__			    =    Rigid.make_transform_from_reference(
       n_xyz=batch["template_all_atom_positions"][..., n, :]     ,		ca_xyz=batch["template_all_atom_positions"][..., ca, :]     ,		c_xyz=batch["template_all_atom_positions"][..., c, :]     ,		eps=_UpperCamelCase     ,		)
   UpperCamelCase__			    =    rigids.get_trans()[..., None, :, :]
   UpperCamelCase__			    =    rigids[..., None].invert_apply(_UpperCamelCase )

   UpperCamelCase__			    =    torch.rsqrt(eps + torch.sum(rigid_vec**2     ,		dim=-1 ) )

   UpperCamelCase__			    =    batch["template_all_atom_mask"]
   UpperCamelCase__			    =    t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
   UpperCamelCase__			    =    template_mask[..., None] * template_mask[..., None, :]

   UpperCamelCase__			    =    inv_distance_scalar * template_mask_ad
   UpperCamelCase__			    =    rigid_vec * inv_distance_scalar[..., None]

   if not use_unit_vector:
      UpperCamelCase__			    =    unit_vector * 0.0

   to_concat.extend(torch.unbind(unit_vector[..., None, :]     ,		dim=-1 ) )
   to_concat.append(template_mask_ad[..., None] )

   UpperCamelCase__			    =    torch.cat(_UpperCamelCase     ,		dim=-1 )
   UpperCamelCase__			    =    act * template_mask_ad[..., None]

   return act



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict[str, torch.Tensor] )		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    nn.functional.one_hot(batch["extra_msa"]     ,		23 )
   UpperCamelCase__			    =    [
       msa_ahot,
       batch["extra_has_deletion"].unsqueeze(-1 ),
       batch["extra_deletion_value"].unsqueeze(-1 ),
   ]
   return torch.cat(_UpperCamelCase     ,		dim=-1 )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Rigid     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		)		->       Rigid:





   '''simple docstring'''







   UpperCamelCase__			    =    rrgdf[aatype, ...]

   # [*, N, 8] transformations, i.e.
   #   One [*, N, 8, 3, 3] rotation matrix and
   #   One [*, N, 8, 3]    translation matrix
   UpperCamelCase__			    =    r.from_tensor_axa(_UpperCamelCase )

   UpperCamelCase__			    =    alpha.new_zeros((*((1,) * len(alpha.shape[:-1] )), 2) )
   UpperCamelCase__			    =    1

   # [*, N, 8, 2]
   UpperCamelCase__			    =    torch.cat([bb_rot.expand(*alpha.shape[:-2]     ,		-1     ,		-1 ), alpha]     ,		dim=-2 )

   # [*, N, 8, 3, 3]
   # Produces rotation matrices of the form:
   # [
   #   [1, 0  , 0  ],
   #   [0, a_2,-a_1],
   #   [0, a_1, a_2]
   # ]
   # This follows the original code rather than the supplement, which uses
   # different indices.

   UpperCamelCase__			    =    alpha.new_zeros(default_r.get_rots().get_rot_mats().shape )
   UpperCamelCase__			    =    1
   UpperCamelCase__			    =    alpha[..., 1]
   UpperCamelCase__			    =    -alpha[..., 0]
   UpperCamelCase__			    =    alpha

   UpperCamelCase__			    =    default_r.compose(Rigid(Rotation(rot_mats=_UpperCamelCase )     ,		_UpperCamelCase ) )

   UpperCamelCase__			    =    all_frames[..., 5]
   UpperCamelCase__			    =    all_frames[..., 6]
   UpperCamelCase__			    =    all_frames[..., 7]

   UpperCamelCase__			    =    all_frames[..., 4]
   UpperCamelCase__			    =    chia_frame_to_bb.compose(_UpperCamelCase )
   UpperCamelCase__			    =    chia_frame_to_bb.compose(_UpperCamelCase )
   UpperCamelCase__			    =    chia_frame_to_bb.compose(_UpperCamelCase )

   UpperCamelCase__			    =    Rigid.cat(
       [
           all_frames[..., :5],
           chia_frame_to_bb.unsqueeze(-1 ),
           chia_frame_to_bb.unsqueeze(-1 ),
           chia_frame_to_bb.unsqueeze(-1 ),
       ]     ,		dim=-1     ,		)

   UpperCamelCase__			    =    r[..., None].compose(_UpperCamelCase )

   return all_frames_to_global






def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Rigid     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		_UpperCamelCase   :					torch.Tensor     ,		)		->       torch.Tensor:





   '''simple docstring'''







   UpperCamelCase__			    =    group_idx[aatype, ...]

   # [*, N, 14, 8]
   UpperCamelCase__			    =    nn.functional.one_hot(
       _UpperCamelCase     ,		num_classes=default_frames.shape[-3]     ,		)

   # [*, N, 14, 8]
   UpperCamelCase__			    =    r[..., None, :] * group_mask_one_hot

   # [*, N, 14]
   UpperCamelCase__			    =    t_atoms_to_global.map_tensor_fn(lambda _UpperCamelCase : torch.sum(_UpperCamelCase     ,		dim=-1 ) )

   # [*, N, 14, 1]
   UpperCamelCase__			    =    atom_mask[aatype, ...].unsqueeze(-1 )

   # [*, N, 14, 3]
   UpperCamelCase__			    =    lit_positions[aatype, ...]
   UpperCamelCase__			    =    t_atoms_to_global.apply(_UpperCamelCase )
   UpperCamelCase__			    =    pred_positions * atom_mask

   return pred_positions