

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 			__lowerCAmelCase					( lowerCAmelCase__      ,      lowerCAmelCase__      ,      unittest.TestCase							):
  _a						      =  IFSuperResolutionPipeline
  _a						      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  _a						      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  _a						      =  PipelineTesterMixin.required_optional_params - {"""latents"""}




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        return self._get_superresolution_dummy_components()




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase=0 )  ->			Union[str, Any]:
        '''simple docstring'''






        if str(UpperCamelCase__ ).startswith('mps' ):
              _lowercase     	=torch.manual_seed(UpperCamelCase__ )
        else:
              _lowercase     	=torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )

        _lowercase     	=floats_tensor((1, 3, 32, 32)							,       rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )

        _lowercase     	={
            "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 )  ->			int:
        '''simple docstring'''






        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        self._test_save_load_optional_components()




  @unittest.skipIf(torch_device != 'cuda'							,       reason='float16 requires CUDA' )
  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






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




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        self._test_save_load_local()




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






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





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
            ' Please use LayoutLMv3ImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


