




'''simple docstring'''

import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class __magic_name__(						SCREAMING_SNAKE_CASE__    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					List[Any]    =  KandinskyVaaPriorEmbaEmbPipeline
						UpperCAmelCase_		:					Tuple    =  ['prompt', 'image']
						UpperCAmelCase_		:					List[str]    =  ['prompt', 'image']
						UpperCAmelCase_		:					List[str]    =  [
						    'num_images_per_prompt',
						    'strength',
						    'generator',
						    'num_inference_steps',
						    'latents',
						    'negative_prompt',
						    'guidance_scale',
						    'output_type',
						    'return_dict',
						]
						UpperCAmelCase_		:					List[str]    =  False






						@property
						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									return 3_2






						@property
						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									return 3_2






						@property
						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									return self.time_input_dim






						@property
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									return self.time_input_dim * 4






						@property
						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									return 1_0_0






						@property
						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									snake_case__          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
									return tokenizer






						@property
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									torch.manual_seed(0       )
									snake_case__          =						CLIPTextConfig(
									    bos_token_id=0					,   eos_token_id=2					,   hidden_size=self.text_embedder_hidden_size					,   projection_dim=self.text_embedder_hidden_size					,   intermediate_size=3_7					,   layer_norm_eps=1E-05					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1_0_0_0					,   )
									return CLIPTextModelWithProjection(a_       )






						@property
						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									torch.manual_seed(0       )

									snake_case__          =						{
									    """num_attention_heads""": 2,
									    """attention_head_dim""": 1_2,
									    """embedding_dim""": self.text_embedder_hidden_size,
									    """num_layers""": 1,
									}

									snake_case__          =						PriorTransformer(**a_       )
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									snake_case__          =						nn.Parameter(torch.ones(model.clip_std.shape       )       )
									return model






						@property
						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									torch.manual_seed(0       )
									snake_case__          =						CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size					,   image_size=2_2_4					,   projection_dim=self.text_embedder_hidden_size					,   intermediate_size=3_7					,   num_attention_heads=4					,   num_channels=3					,   num_hidden_layers=5					,   patch_size=1_4					,   )

									snake_case__          =						CLIPVisionModelWithProjection(a_       )
									return model






						@property
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						CLIPImageProcessor(
									    crop_size=2_2_4					,   do_center_crop=a_					,   do_normalize=a_					,   do_resize=a_					,   image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]					,   image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]					,   resample=3					,   size=2_2_4					,   )

									return image_processor






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						self.dummy_prior
									snake_case__          =						self.dummy_image_encoder
									snake_case__          =						self.dummy_text_encoder
									snake_case__          =						self.dummy_tokenizer
									snake_case__          =						self.dummy_image_processor

									snake_case__          =						UnCLIPScheduler(
									    variance_type="""fixed_small_log"""					,   prediction_type="""sample"""					,   num_train_timesteps=1_0_0_0					,   clip_sample=a_					,   clip_sample_range=10.0					,   )

									snake_case__          =						{
									    """prior""": prior,
									    """image_encoder""": image_encoder,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """scheduler""": scheduler,
									    """image_processor""": image_processor,
									}

									return components






						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						List[str]					,   __UpperCamelCase						:						Union[str, Any]=0       ):



									'''simple docstring'''






									if str(a_       ).startswith("""mps"""       ):
												snake_case__          =						torch.manual_seed(a_       )
									else:
												snake_case__          =						torch.Generator(device=a_       ).manual_seed(a_       )

									snake_case__          =						floats_tensor((1, 3, 6_4, 6_4)					,   rng=random.Random(a_       )       ).to(a_       )
									snake_case__          =						image.cpu().permute(0					,   2					,   3					,   1       )[0]
									snake_case__          =						Image.fromarray(np.uinta(a_       )       ).convert("""RGB"""       ).resize((2_5_6, 2_5_6)       )

									snake_case__          =						{
									    """prompt""": """horse""",
									    """image""": init_image,
									    """strength""": 0.5,
									    """generator""": generator,
									    """guidance_scale""": 4.0,
									    """num_inference_steps""": 2,
									    """output_type""": """np""",
									}
									return inputs






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



									'''simple docstring'''






									snake_case__          =						"""cpu"""

									snake_case__          =						self.get_dummy_components()

									snake_case__          =						self.pipeline_class(**a_       )
									snake_case__          =						pipe.to(a_       )

									pipe.set_progress_bar_config(disable=a_       )

									snake_case__          =						pipe(**self.get_dummy_inputs(a_       )       )
									snake_case__          =						output.image_embeds

									snake_case__          =						pipe(
									    **self.get_dummy_inputs(a_       )					,   return_dict=a_					,   )[0]

									snake_case__          =						image[0, -1_0:]
									snake_case__          =						image_from_tuple[0, -1_0:]

									assert image.shape == (1, 3_2)

									snake_case__          =						np.array(
									    [
									        0.1_07_12_84,
									        1.3_33_02_71,
									        0.61_26_02_23,
									        -0.6_69_10_65,
									        -0.3_84_68_52,
									        -1.0_30_36_61,
									        0.22_71_61_11,
									        0.03_34_89_01,
									        0.30_04_06_75,
									        -0.24_80_50_29,
									    ]       )

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






						@skip_mps
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						torch_device == """cpu"""
									snake_case__          =						True
									snake_case__          =						False

									self._test_inference_batch_single_identical(
									    test_max_difference=a_					,   relax_max_difference=a_					,   test_mean_pixel_difference=a_					,   )







						@skip_mps
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						torch_device == """cpu"""
									snake_case__          =						False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=a_					,   test_mean_pixel_difference=a_					,   )



'''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