



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 typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase_:       Union[str, Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Dict			     =       {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}






class  a__     ( _a						):
   snake_case_				=   "vit-hybrid"


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=768,					_UpperCAmelCase=12,					_UpperCAmelCase=12,					_UpperCAmelCase=3072,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.02,					_UpperCAmelCase=1E-12,					_UpperCAmelCase=224,					_UpperCAmelCase=1,					_UpperCAmelCase=3,					_UpperCAmelCase=[1, 1024, 24, 24],					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)

        if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."	)
             lowercase__								=				{
                 "global_padding": "same",
                 "layer_type": "bottleneck",
                 "depths": [3, 4, 9],
                 "out_features": ["stage3"],
                 "embedding_dynamic_padding": True,
             }

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             if "model_type" in backbone_config:
                  lowercase__								=				CONFIG_MAPPING[backbone_config["model_type"]]
             else:
                  logger.info(
                      "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."	)
                  lowercase__								=				BitConfig
             lowercase__								=				backbone_config_class(**_UpperCAmelCase	)

        lowercase__								=				backbone_featmap_shape
        lowercase__								=				backbone_config
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				initializer_range
        lowercase__								=				layer_norm_eps
        lowercase__								=				image_size
        lowercase__								=				patch_size
        lowercase__								=				num_channels
        lowercase__								=				qkv_bias


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.backbone_config.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




