






import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class       UpperCAmelCase__(							lowerCamelCase     ,							lowerCamelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  Dict       				=    IFInpaintingSuperResolutionPipeline
   A    :  int       				=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
   A    :  Union[str, Any]       				=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}	)
   A    :  Union[str, Any]       				=    PipelineTesterMixin.required_optional_params - {"latents"}


   def 			UpperCAmelCase		(						self  :			List[str])	->				List[Any]:




         """simple docstring"""

         return self._get_superresolution_dummy_components()


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Union[str, Any]=0)	->				str:




         """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, 16, 16)			,				rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
         lowercase__     =  floats_tensor((1, 3, 32, 32)			,				rng=random.Random(UpperCamelCase__)).to(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,
             'original_image': original_image,
             'mask_image': mask_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 			UpperCAmelCase		(						self  :			Any)	->				Optional[Any]:




         """simple docstring"""

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				str:




         """simple docstring"""

         self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != 'cuda'			,				reason='float16 requires CUDA')
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Optional[int]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Tuple:




         """simple docstring"""

         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)


   def 			UpperCAmelCase		(						self  :			Dict)	->				List[Any]:




         """simple docstring"""

         self._test_save_load_local()



   def 			UpperCAmelCase		(						self  :			Tuple)	->				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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Any   	=			{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Optional[int]   	=			["ReformerTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Union[str, Any]   	=			["ReformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			[
           "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ReformerAttention",
           "ReformerForMaskedLM",
           "ReformerForQuestionAnswering",
           "ReformerForSequenceClassification",
           "ReformerLayer",
           "ReformerModel",
           "ReformerModelWithLMHead",
           "ReformerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer import ReformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer_fast import ReformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_reformer import (
                  REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ReformerAttention,
                  ReformerForMaskedLM,
                  ReformerForQuestionAnswering,
                  ReformerForSequenceClassification,
                  ReformerLayer,
                  ReformerModel,
                  ReformerModelWithLMHead,
                  ReformerPreTrainedModel,
              )

else:
       import sys

       a__      :				Tuple   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
