



'''simple docstring'''



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 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__,	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 				lowerCAmelCase (     self		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							return self._get_superresolution_dummy_components()



			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						List[str]=0			)      ->      List[str]:
							"""simple docstring"""





							if str(__UpperCamelCase			).startswith('''mps'''			):
											snake_case__      :   Optional[Any]      =    torch.manual_seed(__UpperCamelCase			)
							else:
											snake_case__      :   Any      =    torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)

							snake_case__      :   Dict      =    floats_tensor((1, 3, 32, 32)    ,			rng=random.Random(__UpperCamelCase			)			).to(__UpperCamelCase			)

							snake_case__      :   List[Any]      =    {
							    '''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 				lowerCAmelCase (     self		:						str			)      ->      Any:
							"""simple docstring"""





							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							self._test_save_load_optional_components()



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





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



			def 				lowerCAmelCase (     self		:						Tuple			)      ->      Optional[Any]:
							"""simple docstring"""





							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)



			def 				lowerCAmelCase (     self		:						str			)      ->      Optional[int]:
							"""simple docstring"""





							self._test_save_load_local()







			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Tuple:
							"""simple docstring"""





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











from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_		  =			{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		  =			["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		  =			[
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE_		  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


