




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   A		(__A  :       torch.Tensor  ,							__A  :       torch.Tensor  ,							__A  :       None			)  -> torch.Tensor:







     """simple docstring"""


     ...


@overload
def   A		(__A  :       torch.Tensor  ,							__A  :       torch.Tensor  ,							__A  :       torch.Tensor			)  -> Tuple[torch.Tensor, torch.Tensor]:







     """simple docstring"""


     ...


def   A		(__A  :       Tuple  ,							__A  :       List[str]  ,							__A  :       Tuple			)  -> List[Any]:







     """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(
              __A  ,							all_atom_masks[..., ca_idx]  ,							all_atom_masks[..., cb_idx]  ,							)
          return pseudo_beta, pseudo_beta_mask
     else:
          return pseudo_beta


def   A		(__A  :       torch.Tensor  ,							__A  :       Dict[str, torch.Tensor]			)  -> torch.Tensor:







     """simple docstring"""


     UpperCAmelCase_		=				batched_gather(
         __A  ,							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   A		(__A  :       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(__A  ,							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   A		(__A  :       Dict[str, torch.Tensor]  ,							__A  :       torch.types.Number  ,							__A  :       torch.types.Number  ,							__A  :       int  ,							__A  :       bool = False  ,							__A  :       float = 1E-20  ,							__A  :       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=__A			)
     UpperCAmelCase_		=				torch.linspace(__A  ,							__A  ,							__A  ,							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]  ,							__A  ,							-1  ,							-1			)			)
     to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]  ,							-1  ,							__A  ,							-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=__A  ,							)
     UpperCAmelCase_		=				rigids.get_trans()[..., None, :, :]
     UpperCAmelCase_		=				rigids[..., None].invert_apply(__A			)

     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(__A  ,							dim=-1			)
     UpperCAmelCase_		=				act * template_mask_ad[..., None]

     return act


def   A		(__A  :       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(__A  ,							dim=-1			)


def   A		(__A  :       Rigid  ,							__A  :       torch.Tensor  ,							__A  :       torch.Tensor  ,							__A  :       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(__A			)

     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=__A			)  ,							__A			)			)

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

     UpperCAmelCase_		=				all_frames[..., 4]
     UpperCAmelCase_		=				chia_frame_to_bb.compose(__A			)
     UpperCAmelCase_		=				chia_frame_to_bb.compose(__A			)
     UpperCAmelCase_		=				chia_frame_to_bb.compose(__A			)

     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(__A			)

     return all_frames_to_global


def   A		(__A  :       Rigid  ,							__A  :       torch.Tensor  ,							__A  :       torch.Tensor  ,							__A  :       torch.Tensor  ,							__A  :       torch.Tensor  ,							__A  :       torch.Tensor  ,							)  -> torch.Tensor:







     """simple docstring"""


     UpperCAmelCase_		=				group_idx[aatype, ...]

     # [*, N, 14, 8]
     UpperCAmelCase_		=				nn.functional.one_hot(
         __A  ,							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 __A			: torch.sum(__A  ,							dim=-1			)			)

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

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

     return pred_positions












import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       str		=    PaintByExamplePipeline
   UpperCAmelCase__    :       Tuple		=    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   UpperCAmelCase__    :       Any		=    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   UpperCAmelCase__    :       Union[str, Any]		=    frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_		=				UNetaDConditionModel(
            block_out_channels=(32, 64)   ,		layers_per_block=2   ,		sample_size=32   ,		in_channels=9   ,		out_channels=4   ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,		cross_attention_dim=32   ,		)
        UpperCAmelCase_		=				PNDMScheduler(skip_prk_steps=_snake_case)
        torch.manual_seed(0)
        UpperCAmelCase_		=				AutoencoderKL(
            block_out_channels=[32, 64]   ,		in_channels=3   ,		out_channels=3   ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,		latent_channels=4   ,		)
        torch.manual_seed(0)
        UpperCAmelCase_		=				CLIPVisionConfig(
            hidden_size=32   ,		projection_dim=32   ,		intermediate_size=37   ,		layer_norm_eps=1e-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		image_size=32   ,		patch_size=4   ,		)
        UpperCAmelCase_		=				PaintByExampleImageEncoder(_snake_case   ,		proj_size=32)
        UpperCAmelCase_		=				CLIPImageProcessor(crop_size=32   ,		size=32)

        UpperCAmelCase_		=				{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''image_encoder''': image_encoder,
            '''safety_checker''': None,
            '''feature_extractor''': feature_extractor,
        }
        return components







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				np.array(image.convert('''RGB'''))
        UpperCAmelCase_		=				image[None].transpose(0   ,		3   ,		1   ,		2)
        UpperCAmelCase_		=				torch.from_numpy(_snake_case).to(dtype=torch.floataa) / 1_2_7.5 - 1.0
        return image







   def lowerCamelCase		( self						:		Tuple   ,		_snake_case						:		Dict="cpu"   ,		_snake_case						:		int=0):

        """simple docstring"""


        UpperCAmelCase_		=				floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_		=				image.cpu().permute(0   ,		2   ,		3   ,		1)[0]
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_		=				Image.fromarray(np.uinta(image + 4)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((32, 32))

        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_		=				torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_		=				torch.Generator(device=_snake_case).manual_seed(_snake_case)
        UpperCAmelCase_		=				{
            '''example_image''': example_image,
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**_snake_case)
        UpperCAmelCase_		=				pipe.to('''cpu''')
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				self.get_dummy_inputs()
        UpperCAmelCase_		=				pipe(**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        UpperCAmelCase_		=				np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				'''cpu'''
        UpperCAmelCase_		=				self.get_dummy_inputs()
        inputs.pop('''mask_image''')
        UpperCAmelCase_		=				self.convert_to_pt(inputs.pop('''image'''))
        UpperCAmelCase_		=				image.clamp(0   ,		1) / 2

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**self.get_dummy_components())
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				pipe(image=_snake_case   ,		mask_image=mask_image[:, 0]   ,		**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image.cpu().permute(0   ,		2   ,		3   ,		1)[0]
        UpperCAmelCase_		=				mask_image.cpu().permute(0   ,		2   ,		3   ,		1)[0]

        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')

        UpperCAmelCase_		=				pipe(**self.get_dummy_inputs())
        UpperCAmelCase_		=				output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()).max() < 5e-2







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







@slow
@require_torch_gpu
class __snake_case     (     unittest.TestCase						):







   def lowerCamelCase		( self						:		Union[str, Any]):

        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/dog_in_bucket.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/mask.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/panda.jpg''')

        UpperCAmelCase_		=				PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''')
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				torch.manual_seed(321)
        UpperCAmelCase_		=				pipe(
            image=_snake_case   ,		mask_image=_snake_case   ,		example_image=_snake_case   ,		generator=_snake_case   ,		guidance_scale=5.0   ,		num_inference_steps=50   ,		output_type='''np'''   ,		)

        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_		=				np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2






