




'''simple docstring'''




import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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       ( _lowerCamelCase			,					_lowerCamelCase			,					unittest.TestCase				):
       lowercase	:    Any      						=IFImgaImgSuperResolutionPipeline
       lowercase	:    Any      						=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
       lowercase	:    str      						=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}				)
       lowercase	:    Union[str, Any]      						=PipelineTesterMixin.required_optional_params - {'latents'}






       def      lowercase__     (							self  ):



              """simple docstring"""







              return self._get_superresolution_dummy_components()






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase=0  ):



              """simple docstring"""







              if str(_UpperCamelCase  ).startswith('''mps'''  ):
                     lowerCamelCase_							=torch.manual_seed(_UpperCamelCase  )
              else:
                     lowerCamelCase_							=torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )

              lowerCamelCase_							=floats_tensor((1, 3, 32, 32),		rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )
              lowerCamelCase_							=floats_tensor((1, 3, 16, 16),		rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )

              lowerCamelCase_							={
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''image''': image,
                  '''original_image''': original_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,		)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__					:							Optional[Any]													=     {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							List[str]													=     [
           'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'IBertForMaskedLM',
           'IBertForMultipleChoice',
           'IBertForQuestionAnswering',
           'IBertForSequenceClassification',
           'IBertForTokenClassification',
           'IBertModel',
           'IBertPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_ibert import (
                  IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  IBertForMaskedLM,
                  IBertForMultipleChoice,
                  IBertForQuestionAnswering,
                  IBertForSequenceClassification,
                  IBertForTokenClassification,
                  IBertModel,
                  IBertPreTrainedModel,
              )

else:
       import sys

       snake_case__					:							Dict													=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





