


import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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   :  Union[str, Any]				=       IFSuperResolutionPipeline
       __UpperCamelCase   :  List[Any]				=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
       __UpperCamelCase   :  Optional[Any]				=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       __UpperCamelCase   :  List[str]				=       PipelineTesterMixin.required_optional_params - {"latents"}






       def 			_lowercase (  self    ):




              """simple docstring"""




              return self._get_superresolution_dummy_components()






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=0    ):




              """simple docstring"""




              if str(__SCREAMING_SNAKE_CASE    ).startswith('''mps'''    ):
                     UpperCamelCase     :						Optional[int] 						=		torch.manual_seed(__SCREAMING_SNAKE_CASE    )
              else:
                     UpperCamelCase     :						Dict 						=		torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Optional[Any] 						=		floats_tensor((1, 3, 32, 32)			,       rng=random.Random(__SCREAMING_SNAKE_CASE    )    ).to(__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Dict 						=		{
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''image''': 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    ):




              """simple docstring"""




              self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              self._test_save_load_optional_components()






       @unittest.skipIf(torch_device != '''cuda'''			,       reason='''float16 requires CUDA'''    )
       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




              self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              self._test_save_load_local()






       def 			_lowercase (  self    ):




              """simple docstring"""




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





import sys


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		list[int]							):






       """simple docstring"""





       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(SCREAMING_SNAKE_CASE_				,      (list, tuple)							) or not all(
           isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for number in numbers							):
              raise ValueError('''numbers must be an iterable of integers'''							)

       UpperCamelCase     :						Dict 						=		0
       UpperCamelCase     :						Tuple 						=		sys.maxsize

       while right < len(SCREAMING_SNAKE_CASE_							):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     UpperCamelCase     :						int 						=		min(SCREAMING_SNAKE_CASE_				,      right - left + 1							)
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len

