

'''simple docstring'''





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 (							UpperCAmelCase__  , UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					IFInpaintingSuperResolutionPipeline
		SCREAMING_SNAKE_CASE_           =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
		SCREAMING_SNAKE_CASE_           =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}    )
		SCREAMING_SNAKE_CASE_           =					PipelineTesterMixin.required_optional_params - {'latents'}
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							return self._get_superresolution_dummy_components()
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=0		)  ->     int:






							'''simple docstring'''



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

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

							lowerCamelCase_  				=      {
							    '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		)  ->     Optional[int]:






							'''simple docstring'''



							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



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






							'''simple docstring'''



							super().test_save_load_floataa(expected_max_diff=1E-1		)
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							self._test_save_load_local()






		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



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











# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE   =  {
    "configuration_xmod": [
        "XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XmodConfig",
        "XmodOnnxConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE   =  [
        "XMOD_PRETRAINED_MODEL_ARCHIVE_LIST",
        "XmodForCausalLM",
        "XmodForMaskedLM",
        "XmodForMultipleChoice",
        "XmodForQuestionAnswering",
        "XmodForSequenceClassification",
        "XmodForTokenClassification",
        "XmodModel",
        "XmodPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xmod import (
            XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
            XmodForCausalLM,
            XmodForMaskedLM,
            XmodForMultipleChoice,
            XmodForQuestionAnswering,
            XmodForSequenceClassification,
            XmodForTokenClassification,
            XmodModel,
            XmodPreTrainedModel,
        )

else:
    import sys

    _SCREAMING_SNAKE_CASE   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


