



"""simple docstring"""


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_    (  SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     unittest.TestCase       ):



						"""simple docstring"""
						__UpperCamelCase	       =							IFImgaImgPipeline
						__UpperCamelCase	       =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
						__UpperCamelCase	       =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						__UpperCamelCase	       =							PipelineTesterMixin.required_optional_params - {"""latents"""}






						def 			UpperCAmelCase__  (      self				:Any      )       ->    Optional[int]:
												return self._get_dummy_components()






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:List[str]     ,						lowercase_				:List[Any]=0      )       ->    Tuple:
												if str(lowercase_      ).startswith('mps'      ):
																		UpperCAmelCase    =				torch.manual_seed(lowercase_      )
												else:
																		UpperCAmelCase    =				torch.Generator(device=lowercase_      ).manual_seed(lowercase_      )

												UpperCAmelCase    =				floats_tensor((1, 3, 32, 32)     ,						rng=random.Random(lowercase_      )      ).to(lowercase_      )

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

												return inputs






						def 			UpperCAmelCase__  (      self				:Optional[int]      )       ->    Optional[int]:
												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 			UpperCAmelCase__  (      self				:Dict      )       ->    List[str]:
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )






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






						@unittest.skipIf(torch_device != 'cuda'     ,						reason='float16 requires CUDA'      )
						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    Optional[Any]:
												super().test_floataa_inference(expected_max_diff=1E-1      )






						def 			UpperCAmelCase__  (      self				:str      )       ->    Union[str, Any]:
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )






						def 			UpperCAmelCase__  (      self				:str      )       ->    List[Any]:
												self._test_save_load_local()






						def 			UpperCAmelCase__  (      self				:Optional[int]      )       ->    Union[str, Any]:
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2     ,						)










"""simple docstring"""


from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class    A_    (  datasets.BuilderConfig       ):



						"""simple docstring"""
						__UpperCamelCase	       =							None
						__UpperCamelCase	       =							None
						__UpperCamelCase	       =							None






						def 			UpperCAmelCase__  (      self				:str      )       ->    Optional[Any]:
												assert self.generator is not None, "generator must be specified"

												if self.gen_kwargs is None:
																		UpperCAmelCase    =				{}







class    A_    (  datasets.GeneratorBasedBuilder       ):



						"""simple docstring"""
						__UpperCamelCase	       =							GeneratorConfig






						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    str:
												return datasets.DatasetInfo(features=self.config.features      )






						def 			UpperCAmelCase__  (      self				:List[Any]     ,						lowercase_				:List[Any]      )       ->    Union[str, Any]:
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,						gen_kwargs=self.config.gen_kwargs      )]






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						**lowercase_				:Dict      )       ->    str:
												for idx, ex in enumerate(self.config.generator(**lowercase_      )      ):
																		yield idx, ex





