





'''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  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      ...




@overload
def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      ...




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Optional[int]         =  aatype == rc.restype_order["G"]
      _snake_case		:						Optional[int]         =  rc.atom_order["CA"]
      _snake_case		:						Optional[Any]         =  rc.atom_order["CB"]
      _snake_case		:						Optional[Any]         =  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:
            _snake_case		:						Tuple         =  torch.where(
                lowerCAmelCase_      ,   all_atom_masks[..., ca_idx]      ,   all_atom_masks[..., cb_idx]      ,   )
            return pseudo_beta, pseudo_beta_mask
      else:
            return pseudo_beta




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[str]         =  batched_gather(
          lowerCAmelCase_      ,   batch['''residx_atom37_to_atom14''']      ,   dim=-2      ,   no_batch_dims=len(atomaa.shape[:-2]						)      ,   )

      _snake_case		:						Any         =  atomaa_data * batch["atom37_atom_exists"][..., None]

      return atomaa_data




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Dict         =  template_feats["template_aatype"]
      _snake_case		:						Optional[int]         =  template_feats["template_torsion_angles_sin_cos"]
      _snake_case		:						Tuple         =  template_feats["template_alt_torsion_angles_sin_cos"]
      _snake_case		:						Optional[int]         =  template_feats["template_torsion_angles_mask"]
      _snake_case		:						Union[str, Any]         =  torch.cat(
          [
              nn.functional.one_hot(lowerCAmelCase_      ,   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     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_ = False      ,   lowerCAmelCase_ = 1E-20      ,   lowerCAmelCase_ = 1E8      ,   ):





      """simple docstring"""



      _snake_case		:						Union[str, Any]         =  batch["template_pseudo_beta_mask"]
      _snake_case		:						Union[str, Any]         =  template_mask[..., None] * template_mask[..., None, :]

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

      _snake_case		:						Union[str, Any]         =  [dgram, template_mask_ad[..., None]]

      _snake_case		:						torch.LongTensor         =  nn.functional.one_hot(
          batch['''template_aatype''']      ,   rc.restype_num + 2      ,   )

      _snake_case		:						List[Any]         =  batch["template_aatype"].shape[-1]
      to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]      ,   lowerCAmelCase_      ,   -1      ,   -1						)						)
      to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]      ,   -1      ,   lowerCAmelCase_      ,   -1						)						)

      _snake_case		:						Optional[Any]         =  [rc.atom_order[a] for a in ["N", "CA", "C"]]
      _snake_case		:						Dict         =  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=lowerCAmelCase_      ,   )
      _snake_case		:						Optional[int]         =  rigids.get_trans()[..., None, :, :]
      _snake_case		:						Any         =  rigids[..., None].invert_apply(lowerCAmelCase_						)

      _snake_case		:						List[Any]         =  torch.rsqrt(eps + torch.sum(rigid_vec**2      ,   dim=-1						)						)

      _snake_case		:						List[str]         =  batch["template_all_atom_mask"]
      _snake_case		:						Optional[Any]         =  t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
      _snake_case		:						Optional[int]         =  template_mask[..., None] * template_mask[..., None, :]

      _snake_case		:						Dict         =  inv_distance_scalar * template_mask_ad
      _snake_case		:						Any         =  rigid_vec * inv_distance_scalar[..., None]

      if not use_unit_vector:
            _snake_case		:						Any         =  unit_vector * 0.0

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

      _snake_case		:						Union[str, Any]         =  torch.cat(lowerCAmelCase_      ,   dim=-1						)
      _snake_case		:						Optional[int]         =  act * template_mask_ad[..., None]

      return act




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						torch.LongTensor         =  nn.functional.one_hot(batch['''extra_msa''']      ,   23						)
      _snake_case		:						Optional[Any]         =  [
          msa_ahot,
          batch["extra_has_deletion"].unsqueeze(-1						),
          batch["extra_deletion_value"].unsqueeze(-1						),
      ]
      return torch.cat(lowerCAmelCase_      ,   dim=-1						)




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   ):





      """simple docstring"""



      _snake_case		:						List[Any]         =  rrgdf[aatype, ...]

      # [*, N, 8] transformations, i.e.
      #   One [*, N, 8, 3, 3] rotation matrix and
      #   One [*, N, 8, 3]    translation matrix
      _snake_case		:						Tuple         =  r.from_tensor_axa(lowerCAmelCase_						)

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

      # [*, N, 8, 2]
      _snake_case		:						int         =  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.

      _snake_case		:						int         =  alpha.new_zeros(default_r.get_rots().get_rot_mats().shape						)
      _snake_case		:						Tuple         =  1
      _snake_case		:						Union[str, Any]         =  alpha[..., 1]
      _snake_case		:						Tuple         =  -alpha[..., 0]
      _snake_case		:						Any         =  alpha

      _snake_case		:						Optional[Any]         =  default_r.compose(Rigid(Rotation(rot_mats=lowerCAmelCase_						)      ,   lowerCAmelCase_						)						)

      _snake_case		:						Tuple         =  all_frames[..., 5]
      _snake_case		:						str         =  all_frames[..., 6]
      _snake_case		:						str         =  all_frames[..., 7]

      _snake_case		:						Optional[Any]         =  all_frames[..., 4]
      _snake_case		:						Union[str, Any]         =  chia_frame_to_bb.compose(lowerCAmelCase_						)
      _snake_case		:						Tuple         =  chia_frame_to_bb.compose(lowerCAmelCase_						)
      _snake_case		:						Optional[Any]         =  chia_frame_to_bb.compose(lowerCAmelCase_						)

      _snake_case		:						str         =  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      ,   )

      _snake_case		:						Dict         =  r[..., None].compose(lowerCAmelCase_						)

      return all_frames_to_global




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   ):





      """simple docstring"""



      _snake_case		:						List[Any]         =  group_idx[aatype, ...]

      # [*, N, 14, 8]
      _snake_case		:						torch.LongTensor         =  nn.functional.one_hot(
          lowerCAmelCase_      ,   num_classes=default_frames.shape[-3]      ,   )

      # [*, N, 14, 8]
      _snake_case		:						List[Any]         =  r[..., None, :] * group_mask_one_hot

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

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

      # [*, N, 14, 3]
      _snake_case		:						List[str]         =  lit_positions[aatype, ...]
      _snake_case		:						Optional[Any]         =  t_atoms_to_global.apply(lowerCAmelCase_						)
      _snake_case		:						List[Any]         =  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 							_a(     __A						, unittest.TestCase     ):
		lowerCamelCase__  :Optional[int] 					=		PaintByExamplePipeline
		lowerCamelCase__  :Union[str, Any] 					=		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		lowerCamelCase__  :Optional[Any] 					=		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowerCamelCase__  :int 					=		frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							torch.manual_seed(0					)
							_snake_case							:       Union[str, Any]												= UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)			,    layers_per_block=2			,    sample_size=3_2			,    in_channels=9			,    out_channels=4			,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,    cross_attention_dim=3_2			,    )
							_snake_case							:       int												= PNDMScheduler(skip_prk_steps=__snake_case					)
							torch.manual_seed(0					)
							_snake_case							:       Any												= AutoencoderKL(
							    block_out_channels=[3_2, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,    latent_channels=4			,    )
							torch.manual_seed(0					)
							_snake_case							:       Dict												= CLIPVisionConfig(
							    hidden_size=3_2			,    projection_dim=3_2			,    intermediate_size=3_7			,    layer_norm_eps=1E-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=3_2			,    patch_size=4			,    )
							_snake_case							:       Any												= PaintByExampleImageEncoder(__snake_case			,    proj_size=3_2					)
							_snake_case							:       Dict												= CLIPImageProcessor(crop_size=3_2			,    size=3_2					)

							_snake_case							:       Any												= {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components







		def 							lowercase							(     self			,    __snake_case					)   ->   Tuple:


							'''simple docstring'''

							_snake_case							:       List[str]												= np.array(image.convert("RGB"					)					)
							_snake_case							:       str												= image[None].transpose(0			,    3			,    1			,    2					)
							_snake_case							:       Dict												= torch.from_numpy(__snake_case					).to(dtype=torch.floataa					) / 1_27.5 - 1.0
							return image







		def 							lowercase							(     self			,    __snake_case="cpu"			,    __snake_case=0					)   ->   Any:


							'''simple docstring'''

							_snake_case							:       Tuple												= floats_tensor((1, 3, 3_2, 3_2)			,    rng=random.Random(__snake_case					)					).to(__snake_case					)
							_snake_case							:       Union[str, Any]												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       List[Any]												= Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					).resize((3_2, 3_2)					)

							if str(__snake_case					).startswith("mps"					):
												_snake_case							:       Union[str, Any]												= torch.manual_seed(__snake_case					)
							else:
												_snake_case							:       List[Any]												= torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
							_snake_case							:       Optional[int]												= {
							    "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 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							_snake_case							:       int												= PaintByExamplePipeline(**__snake_case					)
							_snake_case							:       List[Any]												= pipe.to("cpu"					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       List[str]												= self.get_dummy_inputs()
							_snake_case							:       int												= pipe(**__snake_case					)
							_snake_case							:       Dict												= output.images

							_snake_case							:       Optional[int]												= image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							_snake_case							:       int												= np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]					)

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







		def 							lowercase							(     self					)   ->   List[Any]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= "cpu"
							_snake_case							:       str												= self.get_dummy_inputs()
							inputs.pop("mask_image"					)
							_snake_case							:       Union[str, Any]												= self.convert_to_pt(inputs.pop("image"					)					)
							_snake_case							:       Union[str, Any]												= image.clamp(0			,    1					) / 2

							# make sure here that pndm scheduler skips prk
							_snake_case							:       Dict												= PaintByExamplePipeline(**self.get_dummy_components()					)
							_snake_case							:       Union[str, Any]												= pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

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

							_snake_case							:       Any												= image.cpu().permute(0			,    2			,    3			,    1					)[0]
							_snake_case							:       List[str]												= mask_image.cpu().permute(0			,    2			,    3			,    1					)[0]

							_snake_case							:       Any												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)
							_snake_case							:       int												= Image.fromarray(np.uinta(__snake_case					)					).convert("RGB"					)

							_snake_case							:       Optional[int]												= pipe(**self.get_dummy_inputs()					)
							_snake_case							:       Dict												= output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5E-2







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


							'''simple docstring'''

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






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







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


							'''simple docstring'''

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







		def 							lowercase							(     self					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Tuple												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"					)
							_snake_case							:       Optional[Any]												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"					)
							_snake_case							:       Optional[int]												= load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"					)

							_snake_case							:       Any												= PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"					)
							_snake_case							:       Any												= pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							_snake_case							:       Union[str, Any]												= torch.manual_seed(3_2_1					)
							_snake_case							:       int												= pipe(
							    image=__snake_case			,    mask_image=__snake_case			,    example_image=__snake_case			,    generator=__snake_case			,    guidance_scale=5.0			,    num_inference_steps=5_0			,    output_type="np"			,    )

							_snake_case							:       str												= output.images

							_snake_case							:       Optional[int]												= image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							_snake_case							:       int												= np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]					)

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