





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				__lowerCAmelCase       (    UpperCamelCase__						,				UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					Optional[int]						=			IFInpaintingPipeline
      _lowercase				:					int						=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
      _lowercase				:					Union[str, Any]						=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      _lowercase				:					Any						=			PipelineTesterMixin.required_optional_params - {"""latents"""}







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        return self._get_dummy_components()







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__=0							)   ->						Union[str, Any]:



        '''simple docstring'''




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

        a__	:					int										=floats_tensor((1, 3, 3_2, 3_2)		,      rng=random.Random(lowerCAmelCase__							)							).to(lowerCAmelCase__							)
        a__	:					Optional[Any]										=floats_tensor((1, 3, 3_2, 3_2)		,      rng=random.Random(lowerCAmelCase__							)							).to(lowerCAmelCase__							)

        a__	:					str										={
            "prompt": "A painting of a squirrel eating a burger",
            "image": image,
            "mask_image": mask_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    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        self._test_save_load_optional_components()







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



        '''simple docstring'''




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







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



        '''simple docstring'''




        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        self._test_save_load_local()







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




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







import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [2, 1, 2, -1]
     SCREAMING_SNAKE_CASE__										=      [1, 2, 3, 4]




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.first_signal      )
     SCREAMING_SNAKE_CASE__										=      len(self.second_signal      )

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

     # create a zero matrix of max_length x max_length
     SCREAMING_SNAKE_CASE__										=      [[0] * max_length for i in range(_lowercase      )]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
       self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
       self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      deque(self.second_signal      )
       rotated_signal.rotate(_lowercase      )
       for j, item in enumerate(_lowercase      ):
         matrix[i][j] += item

        # multiply the matrix with the first signal
     SCREAMING_SNAKE_CASE__										=      np.matmul(np.transpose(_lowercase      )       ,	np.transpose(self.first_signal      )      )

     # rounding-off to two decimal places
     return [round(_lowercase       ,	2      ) for i in final_signal]


if __name__ == "__main__":
   doctest.testmod()
