



"""simple docstring"""







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       UpperCamelCase__   (	lowercase_,					lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     IFInpaintingPipeline
      _SCREAMING_SNAKE_CASE  				=     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
      _SCREAMING_SNAKE_CASE  				=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      _SCREAMING_SNAKE_CASE  				=     PipelineTesterMixin.required_optional_params - {"""latents"""}



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        return self._get_dummy_components()



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Dict=0       ):
        if str(__UpperCamelCase       ).startswith('mps'       ):
          lowerCAmelCase_   :				List[str]								= torch.manual_seed(__UpperCamelCase       )
        else:
          lowerCAmelCase_   :				Any								= torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )

        lowerCAmelCase_   :				Any								= floats_tensor((1, 3, 3_2, 3_2)    ,			rng=random.Random(__UpperCamelCase       )       ).to(__UpperCamelCase       )
        lowerCAmelCase_   :				List[Any]								= floats_tensor((1, 3, 3_2, 3_2)    ,			rng=random.Random(__UpperCamelCase       )       ).to(__UpperCamelCase       )

        lowerCAmelCase_   :				Optional[Any]								= {
            '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      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        self._test_save_load_optional_components()



      @unittest.skipIf(torch_device != 'cuda'    ,			reason='float16 requires CUDA'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
        super().test_save_load_floataa(expected_max_diff=1E-1       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        self._test_save_load_local()





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        self._test_inference_batch_single_identical(
            expected_max_diff=1E-2    ,			)












"""simple docstring"""
from math import isqrt, loga






def 					A__				(			UpperCamelCase       ):
							A		=  [True] * max_number
							for i in range(2					,  isqrt(max_number - 1       ) + 1       ):
														if is_prime[i]:
																					for j in range(i**2					,  UpperCamelCase					,  UpperCamelCase       ):
																												A		=  False

							return [i for i in range(2					,  UpperCamelCase       ) if is_prime[i]]






def 					A__				(			UpperCamelCase = 800_800					,  UpperCamelCase = 800_800       ):
							A		=  degree * loga(UpperCamelCase       )
							A		=  int(UpperCamelCase       )
							A		=  calculate_prime_numbers(UpperCamelCase       )

							A		=  0
							A		=  0
							A		=  len(UpperCamelCase       ) - 1
							while left < right:
														while (
														    prime_numbers[right] * loga(prime_numbers[left]       )
														    + prime_numbers[left] * loga(prime_numbers[right]       )
														    > upper_bound
														):
																					right -= 1
														hybrid_integers_count += right - left
														left += 1

							return hybrid_integers_count


if __name__ == "__main__":
		print(F"""{solution() = }""")


