




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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 		__UpperCAmelCase(		A_							,						A_							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     IFImgaImgPipeline
     __UpperCAmelCase						:			Dict							=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
     __UpperCAmelCase						:			List[str]							=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __UpperCAmelCase						:			List[str]							=     PipelineTesterMixin.required_optional_params - {"""latents"""}

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[str]:






         """simple docstring"""
         return self._get_dummy_components()

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							str    ,					A			:							Dict=0     )      ->      Tuple:






         """simple docstring"""



         if str(__lowerCamelCase     ).startswith("""mps"""     ):
             a__  			=							torch.manual_seed(__lowerCamelCase     )
         else:
             a__  			=							torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )

         a__  			=							floats_tensor((1, 3, 32, 32)    ,					rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )

         a__  			=							{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Tuple:






         """simple docstring"""
         self._test_save_load_optional_components()

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






         """simple docstring"""
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )

     @unittest.skipIf(torch_device != """cuda"""    ,					reason="""float16 requires CUDA"""     )
     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      str:






         """simple docstring"""
         super().test_save_load_floataa(expected_max_diff=1E-1     )

     @unittest.skipIf(torch_device != """cuda"""    ,					reason="""float16 requires CUDA"""     )
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[Any]:






         """simple docstring"""
         super().test_floataa_inference(expected_max_diff=1E-1     )

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






         """simple docstring"""
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Optional[int]:






         """simple docstring"""
         self._test_save_load_local()






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      List[str]:






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







"""simple docstring"""







from typing import Dict, Optional, Tuple

import torch




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor						)    ->     torch.Tensor:







     '''simple docstring'''


     __A					:					List[str]								=		boundaries[1] - boundaries[0]
     __A					:					int								=		boundaries + step / 2
     __A					:					Union[str, Any]								=		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1						)]  ,       dim=0						)
     return bin_centers




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       )    ->     Tuple[torch.Tensor, torch.Tensor]:







     '''simple docstring'''


     __A					:					str								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     return (
         torch.sum(aligned_distance_error_probs * bin_centers  ,       dim=-1						),
         bin_centers[-1],
     )




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       **_SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       )    ->     Dict[str, torch.Tensor]:







     '''simple docstring'''


     __A					:					int								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Any								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)
     __A						,		__A					:					Tuple								=		_calculate_expected_aligned_error(
         alignment_confidence_breaks=_SCREAMING_SNAKE_CASE  ,       aligned_distance_error_probs=_SCREAMING_SNAKE_CASE  ,       )

     return {
         "aligned_confidence_probs": aligned_confidence_probs,
         "predicted_aligned_error": predicted_aligned_error,
         "max_predicted_aligned_error": max_predicted_aligned_error,
     }




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				Optional[torch.Tensor] = None  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       _SCREAMING_SNAKE_CASE				:				float = 1E-8  ,       **_SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       )    ->     torch.Tensor:







     '''simple docstring'''


     if residue_weights is None:
          __A					:					Optional[int]								=		logits.new_ones(logits.shape[-2]						)

     __A					:					List[str]								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Tuple								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     torch.sum(_SCREAMING_SNAKE_CASE						)
     __A					:					int								=		logits.shape[-2]
     __A					:					str								=		max(_SCREAMING_SNAKE_CASE  ,       19						)

     __A					:					Tuple								=		1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

     __A					:					Tuple								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)

     __A					:					List[str]								=		1.0 / (1 + (bin_centers**2) / (da**2))
     __A					:					int								=		torch.sum(probs * tm_per_bin  ,       dim=-1						)

     __A					:					Any								=		residue_weights / (eps + residue_weights.sum())
     __A					:					List[Any]								=		torch.sum(predicted_tm_term * normed_residue_mask  ,       dim=-1						)

     __A					:					int								=		per_alignment * residue_weights

     __A					:					Optional[int]								=		(weighted == torch.max(_SCREAMING_SNAKE_CASE						)).nonzero()[0]
     return per_alignment[tuple(_SCREAMING_SNAKE_CASE						)]



