"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


a							=logging.get_logger(__name__)






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       str  							=	'''vision-text-dual-encoder'''
				A__				:       Tuple  							=	True

				def __init__(				self     ,			_lowerCamelCase=512     ,			_lowerCamelCase=2.6_5_9_2     ,			**_lowerCamelCase				):
										super().__init__(**_lowerCamelCase				)

										if "vision_config" not in kwargs:
																raise ValueError("`vision_config` can not be `None`."				)

										if "text_config" not in kwargs:
																raise ValueError("`text_config` can not be `None`."				)

										lowerCamelCase__ =kwargs.pop("vision_config"				)
										lowerCamelCase__ =kwargs.pop("text_config"				)

										lowerCamelCase__ =vision_config.pop("model_type"				)
										lowerCamelCase__ =text_config.pop("model_type"				)

										if vision_model_type == "clip":
																lowerCamelCase__ =AutoConfig.for_model(_lowerCamelCase     ,			**_lowerCamelCase				).vision_config
										elif vision_model_type == "clip_vision_model":
																lowerCamelCase__ =CLIPVisionConfig(**_lowerCamelCase				)
										else:
																lowerCamelCase__ =AutoConfig.for_model(_lowerCamelCase     ,			**_lowerCamelCase				)

										lowerCamelCase__ =AutoConfig.for_model(_lowerCamelCase     ,			**_lowerCamelCase				)

										lowerCamelCase__ =projection_dim
										lowerCamelCase__ =logit_scale_init_value

				@classmethod
				def 		_a     (				cls     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase				):
										return cls(vision_config=vision_config.to_dict()     ,			text_config=text_config.to_dict()     ,			**_lowerCamelCase				)






				def 		_a     (				self				):
										lowerCamelCase__ =copy.deepcopy(self.__dict__				)
										lowerCamelCase__ =self.vision_config.to_dict()
										lowerCamelCase__ =self.text_config.to_dict()
										lowerCamelCase__ =self.__class__.model_type
										return output



"""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        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	IFSuperResolutionPipeline
				A__				:       Tuple  							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				A__				:       Optional[int]  							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				A__				:       List[Any]  							=	PipelineTesterMixin.required_optional_params - {'''latents'''}

				def 		_a     (				self				):
										return self._get_superresolution_dummy_components()

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

										lowerCamelCase__ ={
										    "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 		_a     (				self				):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

				def 		_a     (				self				):
										self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
				def 		_a     (				self				):
										# 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 		_a     (				self				):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

				def 		_a     (				self				):
										self._test_save_load_local()






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


