




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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__ (      UpperCamelCase__     ,      UpperCamelCase__     ,      unittest.TestCase   ):
     a	:    Optional[Any]	      =			IFImgaImgPipeline
     a	:    List[str]	      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
     a	:    Optional[Any]	      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     a	:    Tuple	      =			PipelineTesterMixin.required_optional_params - {"""latents"""}







     def     lowerCAmelCase_       (			self				)						->					Union[str, Any]:



          '''simple docstring'''



          return self._get_dummy_components()







     def     lowerCAmelCase_       (			self						,	A						,	A=0				)						->					Dict:



          '''simple docstring'''



          if str(A				).startswith("mps"				):
               a											=   torch.manual_seed(A				)
          else:
               a											=   torch.Generator(device=A				).manual_seed(A				)

          a											=   floats_tensor((1, 3, 32, 32)						,	rng=random.Random(A				)				).to(A				)

          a											=   {
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs







     def     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          self._test_save_load_optional_components()







     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()						,	reason="XFormers attention is only available with CUDA and `xformers` installed"						,	)
     def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



          '''simple docstring'''



          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)







     @unittest.skipIf(torch_device != "cuda"						,	reason="float16 requires CUDA"				)
     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



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







     @unittest.skipIf(torch_device != "cuda"						,	reason="float16 requires CUDA"				)
     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					Optional[Any]:



          '''simple docstring'''



          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          self._test_save_load_local()







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''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,
)


lowercase__   :    str 					=      {
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    int 					=      [
       "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RwkvForCausalLM",
       "RwkvModel",
       "RwkvPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

   lowercase__   :    List[Any] 					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

