




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowerCAmelCase						:   Optional[Any]		=   {
    "configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"],
    "tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[int]		=   [
      "TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
      "AdaptiveEmbedding",
      "TransfoXLForSequenceClassification",
      "TransfoXLLMHeadModel",
      "TransfoXLModel",
      "TransfoXLPreTrainedModel",
      "load_tf_weights_in_transfo_xl",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFAdaptiveEmbedding",
      "TFTransfoXLForSequenceClassification",
      "TFTransfoXLLMHeadModel",
      "TFTransfoXLMainLayer",
      "TFTransfoXLModel",
      "TFTransfoXLPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
  from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_transfo_xl import (
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        AdaptiveEmbedding,
        TransfoXLForSequenceClassification,
        TransfoXLLMHeadModel,
        TransfoXLModel,
        TransfoXLPreTrainedModel,
        load_tf_weights_in_transfo_xl,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_transfo_xl import (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAdaptiveEmbedding,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLMainLayer,
        TFTransfoXLModel,
        TFTransfoXLPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase						:   Optional[int]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











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		):
      UpperCAmelCase_        =		IFInpaintingSuperResolutionPipeline
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}		)
      UpperCAmelCase_        =		PipelineTesterMixin.required_optional_params - {"""latents"""}
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[Any]:
       return self._get_superresolution_dummy_components()
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]=0      )     ->			int:
       if str(lowerCamelCase      ).startswith("mps"      ):
        UpperCAmelCase__							 =						torch.manual_seed(lowerCamelCase      )
       else:
        UpperCAmelCase__							 =						torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

       UpperCAmelCase__							 =						floats_tensor((1, 3, 16, 16) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
       UpperCAmelCase__							 =						floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
       UpperCAmelCase__							 =						floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )

       UpperCAmelCase__							 =						{
           "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   :Union[str, Any]      )     ->			Dict:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Dict:
       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != "cuda" ,      reason="float16 requires CUDA"      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       # 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 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._test_save_load_local()







      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[str]:
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2 ,      )





