



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    _lowercase (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    IFSuperResolutionPipeline
				lowercase__     =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
				lowercase__     =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowercase__     =    PipelineTesterMixin.required_optional_params - {"""latents"""}

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self._get_superresolution_dummy_components()

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__=0     ):

									'''simple docstring'''


									if str(snake_case__     ).startswith("mps"     ):
														UpperCamelCase_			= torch.manual_seed(snake_case__     )
									else:
														UpperCamelCase_			= torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )

									UpperCamelCase_			= floats_tensor((1, 3, 32, 32)  ,			rng=random.Random(snake_case__     )     ).to(snake_case__     )

									UpperCamelCase_			= {
									    "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     ):

									'''simple docstring'''


									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"  ,			reason="float16 requires CUDA"     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self._test_save_load_local()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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






"""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


UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)
class      A__   (   lowercase_  ):
	A_				:					List[str]      =   """vision-text-dual-encoder"""
	A_				:					Tuple      =   True




	def __init__( self	:						Tuple   ,     lowercase	:						List[Any]=512   ,     lowercase	:						Any=2.65_92   ,     **lowercase	:						int						)       ->			int:
						super().__init__(**lowercase						)

						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`."""						)

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

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

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

						__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						)

						__snake_case        =						projection_dim
						__snake_case        =						logit_scale_init_value




	@classmethod
	def 				a_		( cls	:						Union[str, Any]   ,     lowercase	:						PretrainedConfig   ,     lowercase	:						PretrainedConfig   ,     **lowercase	:						Optional[int]						)       ->			Dict:
						return cls(vision_config=vision_config.to_dict()   ,     text_config=text_config.to_dict()   ,     **lowercase						)




	def 				a_		( self	:						Dict						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.vision_config.to_dict()
						__snake_case        =						self.text_config.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


