




'''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


UpperCAmelCase_       				=      logging.get_logger(__name__)

UpperCAmelCase_       				=      {
    '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 						lowerCAmelCase_   ( lowerCamelCase__   ):







		'''simple docstring'''







		lowerCAmelCase_				:					Tuple				     =						'vit-hybrid'


		def __init__(						self    :	Any						,		_UpperCAmelCase    :	int=None						,		_UpperCAmelCase    :	Union[str, Any]=7_68						,		_UpperCAmelCase    :	Any=12						,		_UpperCAmelCase    :	Optional[int]=12						,		_UpperCAmelCase    :	Dict=30_72						,		_UpperCAmelCase    :	Tuple="gelu"						,		_UpperCAmelCase    :	Tuple=0.0						,		_UpperCAmelCase    :	List[str]=0.0						,		_UpperCAmelCase    :	Optional[int]=0.02						,		_UpperCAmelCase    :	int=1E-12						,		_UpperCAmelCase    :	List[Any]=2_24						,		_UpperCAmelCase    :	List[str]=1						,		_UpperCAmelCase    :	Optional[Any]=3						,		_UpperCAmelCase    :	Dict=[1, 10_24, 24, 24]						,		_UpperCAmelCase    :	List[str]=True						,		**_UpperCAmelCase    :	Tuple						,		):






			"""simple docstring"""






			super().__init__(**lowercase_ )

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

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

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



		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






			"""simple docstring"""






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






'''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 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Any				     =						IFImgaImgPipeline
  lowerCAmelCase_				:					Dict				     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  lowerCAmelCase_				:					Union[str, Any]				     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowerCAmelCase_				:					Dict				     =						PipelineTesterMixin.required_optional_params - {"""latents"""}


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   return self._get_dummy_components()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	Any=0 ):






   """simple docstring"""






   if str(_UpperCAmelCase ).startswith("""mps""" ):
    UpperCAmelCase__										=    torch.manual_seed(_UpperCAmelCase )
   else:
    UpperCAmelCase__										=    torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )

   UpperCAmelCase__										=    floats_tensor((1, 3, 32, 32)						,		rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )

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

   return inputs


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """simple docstring"""






   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 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )


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






   """simple docstring"""






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


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   super().test_floataa_inference(expected_max_diff=1E-1 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_save_load_local()



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






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