

import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					List[str]      =						IFImgaImgSuperResolutionPipeline
 _lowercase     :					Optional[Any]      =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
 _lowercase     :					str      =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''} )
 _lowercase     :					Any      =						PipelineTesterMixin.required_optional_params - {'''latents'''}

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






      '''simple docstring'''




      return self._get_superresolution_dummy_components()

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	int     ,						A_ :	int=0       ):






      '''simple docstring'''




      if str(A_       ).startswith("mps"       ):
           _lowerCAmelCase   :					List[str]        =    torch.manual_seed(A_       )
      else:
           _lowerCAmelCase   :					int        =    torch.Generator(device=A_       ).manual_seed(A_       )

      _lowerCAmelCase   :					Union[str, Any]        =    floats_tensor((1, 3, 3_2, 3_2)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					Tuple        =    floats_tensor((1, 3, 1_6, 1_6)     ,						rng=random.Random(A_       )       ).to(A_       )

      _lowerCAmelCase   :					List[Any]        =    {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "original_image": original_image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs

 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available()     ,						reason="XFormers attention is only available with CUDA and `xformers` installed"     ,						)
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      self._test_save_load_optional_components()

 @unittest.skipIf(torch_device != "cuda"     ,						reason="float16 requires CUDA"       )
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      self._test_save_load_local()


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






      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2     ,						)









from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, torch.Tensor]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    []
     _lowerCAmelCase   :					Tuple        =    []
     _lowerCAmelCase   :					List[Any]        =    []

     for rt in rc.restypes:
          _lowerCAmelCase   :					int        =    rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
          restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]				)
          _lowerCAmelCase   :					str        =    {name: i for i, name in enumerate(SCREAMING_SNAKE_CASE				)}
          restype_atomaa_to_atomaa_list.append(
              [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]				)

          restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]				)

     # Add dummy mapping for restype 'UNK'
     restype_atomaa_to_atomaa_list.append([0] * 14				)
     restype_atomaa_to_atomaa_list.append([0] * 37				)
     restype_atomaa_mask_list.append([0.0] * 14				)

     _lowerCAmelCase   :					List[str]        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					str        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					Optional[Any]        =    torch.tensor(
         SCREAMING_SNAKE_CASE		,							dtype=torch.floataa		,							device=protein["aatype"].device		,							)
     _lowerCAmelCase   :					List[str]        =    protein["aatype"].to(torch.long				)

     # create the mapping for (residx, atom14) --> atom37, i.e. an array
     # with shape (num_res, 14) containing the atom37 indices for this protein
     _lowerCAmelCase   :					Dict        =    restype_atomaa_to_atomaa[protein_aatype]
     _lowerCAmelCase   :					Optional[Any]        =    restype_atomaa_mask[protein_aatype]

     _lowerCAmelCase   :					Any        =    residx_atomaa_mask
     _lowerCAmelCase   :					Dict        =    residx_atomaa_to_atomaa.long()

     # create the gather indices for mapping back
     _lowerCAmelCase   :					Optional[int]        =    restype_atomaa_to_atomaa[protein_aatype]
     _lowerCAmelCase   :					str        =    residx_atomaa_to_atomaa.long()

     # create the corresponding mask
     _lowerCAmelCase   :					Dict        =    torch.zeros([21, 37]		,							dtype=torch.floataa		,							device=protein["aatype"].device				)
     for restype, restype_letter in enumerate(rc.restypes				):
          _lowerCAmelCase   :					Optional[Any]        =    rc.restype_atoa[restype_letter]
          _lowerCAmelCase   :					int        =    rc.residue_atoms[restype_name]
          for atom_name in atom_names:
               _lowerCAmelCase   :					Union[str, Any]        =    rc.atom_order[atom_name]
               _lowerCAmelCase   :					Dict        =    1

     _lowerCAmelCase   :					Tuple        =    restype_atomaa_mask[protein_aatype]
     _lowerCAmelCase   :					List[str]        =    residx_atomaa_mask

     return protein







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, np.ndarray]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    tree_map(lambda SCREAMING_SNAKE_CASE				: torch.tensor(SCREAMING_SNAKE_CASE		,							device=batch["aatype"].device				)		,							SCREAMING_SNAKE_CASE		,							np.ndarray				)
     _lowerCAmelCase   :					Optional[int]        =    tensor_tree_map(lambda SCREAMING_SNAKE_CASE				: np.array(SCREAMING_SNAKE_CASE				)		,							make_atomaa_masks(SCREAMING_SNAKE_CASE				)				)
     return out






