

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      _a				( snake_case_    ,							snake_case_    ,							unittest.TestCase							):
      _lowerCamelCase					: List[Any]					=					IFSuperResolutionPipeline
      _lowerCamelCase					: Tuple					=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
      _lowerCamelCase					: Optional[Any]					=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      _lowerCamelCase					: Any					=					PipelineTesterMixin.required_optional_params - {'latents'}





      def 		__A  (      self       :							List[Any]  ):
            return self._get_superresolution_dummy_components()





      def 		__A  (      self       :							Optional[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Any=0  ):
            if str(UpperCAmelCase  ).startswith("mps"  ):
                  A_	     =							torch.manual_seed(UpperCAmelCase  )
            else:
                  A_	     =							torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )

            A_	     =							floats_tensor((1, 3, 32, 32)	, rng=random.Random(UpperCAmelCase  )  ).to(UpperCAmelCase  )

            A_	     =							{
                "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 		__A  (      self       :							Optional[int]  ):
            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )





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





      @unittest.skipIf(torch_device != "cuda"	, reason="float16 requires CUDA"  )
      def 		__A  (      self       :							Union[str, 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 		__A  (      self       :							int  ):
            self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )





      def 		__A  (      self       :							Optional[int]  ):
            self._test_save_load_local()







      def 		__A  (      self       :							List[str]  ):
            self._test_inference_batch_single_identical(
                expected_max_diff=1E-2	, )


import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


__a						:Optional[int]            = logging.get_logger(__name__)

class      _a				( snake_case_							):




      """simple docstring"""

      def __init__(      self       :							List[Any]	, *UpperCAmelCase       :							Any	, **UpperCAmelCase       :							int  ):
            warnings.warn(
                "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                " Please use MobileNetV2ImageProcessor instead."	, UpperCAmelCase	, )
            super().__init__(*UpperCAmelCase	, **UpperCAmelCase  )