



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    _lowercase (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    IFSuperResolutionPipeline
				lowercase__     =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
				lowercase__     =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowercase__     =    PipelineTesterMixin.required_optional_params - {"""latents"""}

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self._get_superresolution_dummy_components()

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__=0     ):

									'''simple docstring'''


									if str(snake_case__     ).startswith("mps"     ):
														UpperCamelCase_			= torch.manual_seed(snake_case__     )
									else:
														UpperCamelCase_			= torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )

									UpperCamelCase_			= floats_tensor((1, 3, 32, 32)  ,			rng=random.Random(snake_case__     )     ).to(snake_case__     )

									UpperCamelCase_			= {
									    "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    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self._test_save_load_optional_components()

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

									'''simple docstring'''


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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self._test_save_load_local()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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






def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					53_81
      for x in s:
            lowerCamelCase__   	=					((hash_value << 5) + hash_value) + ord(lowerCamelCase    )
      return hash_value & 0Xffff_ffff