



'''simple docstring'''






import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
						import jax.numpy as jnp

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def __get__(						self			:     List[str]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     int=None		)			->		int:


								if obj is None:
															return self
								if self.fget is None:
															raise AttributeError('''unreadable attribute'''		)
								UpperCAmelCase :       int 							=		'''__cached_''' + self.fget.__name__
								UpperCAmelCase :       Optional[int] 							=		getattr(_snake_case		,			_snake_case		,			_snake_case		)
								if cached is None:
															UpperCAmelCase :       Any 							=		self.fget(_snake_case		)
															setattr(_snake_case		,			_snake_case		,			_snake_case		)
								return cached


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   str:
							UpperCAmelCase :       Optional[int] 							=		val.lower()
							if val in {"y", "yes", "t", "true", "on", "1"}:
														return 1
							if val in {"n", "no", "f", "false", "off", "0"}:
														return 0
							raise ValueError(f"""invalid truth value {val!r}"""  )


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   str:


							if is_torch_fx_proxy(__A  ):
														return True
							if is_torch_available():
														import torch

														if isinstance(__A   ,      torch.Tensor  ):
																					return True
							if is_tf_available():
														import tensorflow as tf

														if isinstance(__A   ,      tf.Tensor  ):
																					return True

							if is_flax_available():
														import jax.numpy as jnp
														from jax.core import Tracer

														if isinstance(__A   ,      (jnp.ndarray, Tracer)  ):
																					return True

							return isinstance(__A   ,      np.ndarray  )


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   Dict:
							return isinstance(__A   ,      np.ndarray  )


def  snake_case_     (					_lowerCAmelCase  :						Dict  )      ->   Any:
							return _is_numpy(__A  )


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   int:
							import torch

							return isinstance(__A   ,      torch.Tensor  )


def  snake_case_     (					_lowerCAmelCase  :						Any  )      ->   List[str]:
							return False if not is_torch_available() else _is_torch(__A  )


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]  )      ->   List[str]:
							import torch

							return isinstance(__A   ,      torch.device  )


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   Any:
							return False if not is_torch_available() else _is_torch_device(__A  )


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   Tuple:
							import torch

							if isinstance(__A   ,      __A  ):
														if hasattr(__A   ,      __A  ):
																					UpperCAmelCase :       List[str] 							=		getattr(__A   ,      __A  )
														else:
																					return False
							return isinstance(__A   ,      torch.dtype  )


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]  )      ->   Tuple:
							return False if not is_torch_available() else _is_torch_dtype(__A  )


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   List[Any]:
							import tensorflow as tf

							return isinstance(__A   ,      tf.Tensor  )


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   int:
							return False if not is_tf_available() else _is_tensorflow(__A  )


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   int:
							import tensorflow as tf

							# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
							if hasattr(__A   ,      '''is_symbolic_tensor'''  ):
														return tf.is_symbolic_tensor(__A  )
							return type(__A  ) == tf.Tensor


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   Union[str, Any]:
							return False if not is_tf_available() else _is_tf_symbolic_tensor(__A  )


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]  )      ->   str:
							import jax.numpy as jnp  # noqa: F811

							return isinstance(__A   ,      jnp.ndarray  )


def  snake_case_     (					_lowerCAmelCase  :						int  )      ->   Optional[Any]:
							return False if not is_flax_available() else _is_jax(__A  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   int:


							if isinstance(__A   ,      (dict, UserDict)  ):
														return {k: to_py_obj(__A  ) for k, v in obj.items()}
							elif isinstance(__A   ,      (list, tuple)  ):
														return [to_py_obj(__A  ) for o in obj]
							elif is_tf_tensor(__A  ):
														return obj.numpy().tolist()
							elif is_torch_tensor(__A  ):
														return obj.detach().cpu().tolist()
							elif is_jax_tensor(__A  ):
														return np.asarray(__A  ).tolist()
							elif isinstance(__A   ,      (np.ndarray, np.number)  ):  # tolist also works on 0d np arrays
														return obj.tolist()
							else:
														return obj


def  snake_case_     (					_lowerCAmelCase  :						Dict  )      ->   List[Any]:


							if isinstance(__A   ,      (dict, UserDict)  ):
														return {k: to_numpy(__A  ) for k, v in obj.items()}
							elif isinstance(__A   ,      (list, tuple)  ):
														return np.array(__A  )
							elif is_tf_tensor(__A  ):
														return obj.numpy()
							elif is_torch_tensor(__A  ):
														return obj.detach().cpu().numpy()
							elif is_jax_tensor(__A  ):
														return np.asarray(__A  )
							else:
														return obj

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def      A	(						self			:     str		)			->		List[Any]:
								UpperCAmelCase :       List[Any] 							=		fields(self		)

								# Safety and consistency checks
								if not len(_snake_case		):
															raise ValueError(F"""{self.__class__.__name__} has no fields."""		)
								if not all(field.default is None for field in class_fields[1:]		):
															raise ValueError(F"""{self.__class__.__name__} should not have more than one required field."""		)

								UpperCAmelCase :       Dict 							=		getattr(self		,			class_fields[0].name		)
								UpperCAmelCase :       str 							=		all(getattr(self		,			field.name		) is None for field in class_fields[1:]		)

								if other_fields_are_none and not is_tensor(_snake_case		):
															if isinstance(_snake_case		,			_snake_case		):
																						UpperCAmelCase :       Dict 							=		first_field.items()
																						UpperCAmelCase :       Optional[Any] 							=		True
															else:
																						try:
																													UpperCAmelCase :       str 							=		iter(_snake_case		)
																													UpperCAmelCase :       Dict 							=		True
																						except TypeError:
																													UpperCAmelCase :       List[Any] 							=		False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
															if first_field_iterator:
																						for idx, element in enumerate(_snake_case		):
																													if (
																													    not isinstance(_snake_case		,			(list, tuple)		)
																													    or not len(_snake_case		) == 2
																													    or not isinstance(element[0]		,			_snake_case		)
																													):
																																				if idx == 0:
																																											# If we do not have an iterator of key/values, set it as attribute
																																											UpperCAmelCase :       Any 							=		first_field
																																				else:
																																											# If we have a mixed iterator, raise an error
																																											raise ValueError(
																																											    F"""Cannot set key/value for {element}. It needs to be a tuple (key, value)."""		)
																																				break
																													setattr(self		,			element[0]		,			element[1]		)
																													if element[1] is not None:
																																				UpperCAmelCase :       List[str] 							=		element[1]
															elif first_field is not None:
																						UpperCAmelCase :       Any 							=		first_field
								else:
															for field in class_fields:
																						UpperCAmelCase :       Optional[Any] 							=		getattr(self		,			field.name		)
																						if v is not None:
																													UpperCAmelCase :       Optional[Any] 							=		v







	def __delitem__(						self			:     Tuple		,			*__snake_case			:     str		,			**__snake_case			:     Dict		)			->		List[Any]:
								raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""		)







	def      A	(						self			:     int		,			*__snake_case			:     Optional[int]		,			**__snake_case			:     Dict		)			->		int:
								raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""		)







	def      A	(						self			:     Dict		,			*__snake_case			:     Optional[int]		,			**__snake_case			:     Optional[int]		)			->		str:
								raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""		)







	def      A	(						self			:     Optional[int]		,			*__snake_case			:     int		,			**__snake_case			:     Optional[Any]		)			->		Union[str, Any]:
								raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance."""		)







	def __getitem__(						self			:     Any		,			__snake_case			:     Optional[int]		)			->		Any:


								if isinstance(_snake_case		,			_snake_case		):
															UpperCAmelCase :       Union[str, Any] 							=		dict(self.items()		)
															return inner_dict[k]
								else:
															return self.to_tuple()[k]







	def __setattr__(						self			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     List[str]		)			->		Optional[Any]:


								if name in self.keys() and value is not None:
															# Don't call self.__setitem__ to avoid recursion errors
															super().__setitem__(_snake_case		,			_snake_case		)
								super().__setattr__(_snake_case		,			_snake_case		)







	def __setitem__(						self			:     Dict		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		)			->		Union[str, Any]:
								super().__setitem__(_snake_case		,			_snake_case		)
								# Don't call self.__setattr__ to avoid recursion errors
								super().__setattr__(_snake_case		,			_snake_case		)







	def      A	(						self			:     Dict		)			->		Union[str, Any]:
								return tuple(self[k] for k in self.keys()		)

class 				SCREAMING_SNAKE_CASE(							A__							,		A__			):







	"""simple docstring"""






	@classmethod
	def      A	(						cls			:     List[Any]		,			__snake_case			:     Optional[Any]		)			->		Any:
								raise ValueError(
								    F"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()		)}"""		)

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       '''longest'''
	lowerCamelCase__			 =       '''max_length'''
	lowerCamelCase__			 =       '''do_not_pad'''

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       '''pt'''
	lowerCamelCase__			 =       '''tf'''
	lowerCamelCase__			 =       '''np'''
	lowerCamelCase__			 =       '''jax'''

class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self			:     List[str]		,			__snake_case			:     List[ContextManager]		)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		context_managers
								UpperCAmelCase :       Any 							=		ExitStack()







	def __enter__(						self			:     str		)			->		Optional[Any]:


								for context_manager in self.context_managers:
															self.stack.enter_context(_snake_case		)







	def __exit__(						self			:     int		,			*__snake_case			:     List[str]		,			**__snake_case			:     Optional[Any]		)			->		Optional[int]:
								self.stack.__exit__(*_snake_case		,			**_snake_case		)


def  snake_case_     (					_lowerCAmelCase  :						List[Any]  )      ->   str:
							UpperCAmelCase :       Optional[Any] 							=		infer_framework(__A  )
							if framework == "tf":
														UpperCAmelCase :       str 							=		inspect.signature(model_class.call  )  # TensorFlow models
							elif framework == "pt":
														UpperCAmelCase :       str 							=		inspect.signature(model_class.forward  )  # PyTorch models
							else:
														UpperCAmelCase :       Union[str, Any] 							=		inspect.signature(model_class.__call__  )  # Flax models

							for p in signature.parameters:
														if p == "return_loss" and signature.parameters[p].default is True:
																					return True

							return False


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   int:
							UpperCAmelCase :       Union[str, Any] 							=		model_class.__name__
							UpperCAmelCase :       int 							=		infer_framework(__A  )
							if framework == "tf":
														UpperCAmelCase :       List[Any] 							=		inspect.signature(model_class.call  )  # TensorFlow models
							elif framework == "pt":
														UpperCAmelCase :       Optional[int] 							=		inspect.signature(model_class.forward  )  # PyTorch models
							else:
														UpperCAmelCase :       int 							=		inspect.signature(model_class.__call__  )  # Flax models

							if "QuestionAnswering" in model_name:
														return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
							else:
														return [p for p in signature.parameters if "label" in p]


def  snake_case_     (					_lowerCAmelCase  :						MutableMapping   ,      _lowerCAmelCase  :						str = ""   ,      _lowerCAmelCase  :						str = "."  )      ->   Optional[Any]:


							def _flatten_dict(_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						int=""   ,      _lowerCAmelCase  :						Any="."  ):
														for k, v in d.items():
																					UpperCAmelCase :       str 							=		str(__A  ) + delimiter + str(__A  ) if parent_key else k
																					if v and isinstance(__A   ,      __A  ):
																												yield from flatten_dict(__A   ,      __A   ,      delimiter=__A  ).items()
																					else:
																												yield key, v

							return dict(_flatten_dict(__A   ,      __A   ,      __A  )  )


@contextmanager
def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						bool = False  )      ->   str:


							if use_temp_dir:
														with tempfile.TemporaryDirectory() as tmp_dir:
																					yield tmp_dir
							else:
														yield working_dir


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[int]=None  )      ->   Any:


							if is_numpy_array(__A  ):
														return np.transpose(__A   ,      axes=__A  )
							elif is_torch_tensor(__A  ):
														return array.T if axes is None else array.permute(*__A  )
							elif is_tf_tensor(__A  ):
														import tensorflow as tf

														return tf.transpose(__A   ,      perm=__A  )
							elif is_jax_tensor(__A  ):
														return jnp.transpose(__A   ,      axes=__A  )
							else:
														raise ValueError(f"""Type not supported for transpose: {type(__A  )}."""  )


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						List[Any]  )      ->   Union[str, Any]:


							if is_numpy_array(__A  ):
														return np.reshape(__A   ,      __A  )
							elif is_torch_tensor(__A  ):
														return array.reshape(*__A  )
							elif is_tf_tensor(__A  ):
														import tensorflow as tf

														return tf.reshape(__A   ,      __A  )
							elif is_jax_tensor(__A  ):
														return jnp.reshape(__A   ,      __A  )
							else:
														raise ValueError(f"""Type not supported for reshape: {type(__A  )}."""  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Dict=None  )      ->   int:


							if is_numpy_array(__A  ):
														return np.squeeze(__A   ,      axis=__A  )
							elif is_torch_tensor(__A  ):
														return array.squeeze() if axis is None else array.squeeze(dim=__A  )
							elif is_tf_tensor(__A  ):
														import tensorflow as tf

														return tf.squeeze(__A   ,      axis=__A  )
							elif is_jax_tensor(__A  ):
														return jnp.squeeze(__A   ,      axis=__A  )
							else:
														raise ValueError(f"""Type not supported for squeeze: {type(__A  )}."""  )


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Dict  )      ->   List[str]:


							if is_numpy_array(__A  ):
														return np.expand_dims(__A   ,      __A  )
							elif is_torch_tensor(__A  ):
														return array.unsqueeze(dim=__A  )
							elif is_tf_tensor(__A  ):
														import tensorflow as tf

														return tf.expand_dims(__A   ,      axis=__A  )
							elif is_jax_tensor(__A  ):
														return jnp.expand_dims(__A   ,      axis=__A  )
							else:
														raise ValueError(f"""Type not supported for expand_dims: {type(__A  )}."""  )


def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   Optional[Any]:


							if is_numpy_array(__A  ):
														return np.size(__A  )
							elif is_torch_tensor(__A  ):
														return array.numel()
							elif is_tf_tensor(__A  ):
														import tensorflow as tf

														return tf.size(__A  )
							elif is_jax_tensor(__A  ):
														return array.size
							else:
														raise ValueError(f"""Type not supported for expand_dims: {type(__A  )}."""  )


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Dict  )      ->   Dict:


							for key, value in auto_map.items():
														if isinstance(__A   ,      (tuple, list)  ):
																					UpperCAmelCase :       Tuple 							=		[f"""{repo_id}--{v}""" if (v is not None and '''--''' not in v) else v for v in value]
														elif value is not None and "--" not in value:
																					UpperCAmelCase :       Optional[int] 							=		f"""{repo_id}--{value}"""

							return auto_map




def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   List[str]:


							for base_class in inspect.getmro(__A  ):
														UpperCAmelCase :       Optional[int] 							=		base_class.__module__
														UpperCAmelCase :       Any 							=		base_class.__name__
														if module.startswith('''tensorflow'''  ) or module.startswith('''keras'''  ) or name == "TFPreTrainedModel":
																					return "tf"
														elif module.startswith('''torch'''  ) or name == "PreTrainedModel":
																					return "pt"
														elif module.startswith('''flax'''  ) or module.startswith('''jax'''  ) or name == "FlaxPreTrainedModel":
																					return "flax"
							else:
														raise TypeError(f"""Could not infer framework from class {model_class}."""  )











# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()







class __snake_case			(			a ,  a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  StableDiffusionControlNetImgaImgPipeline
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCAmelCase__  :    Union[str, Any]          =  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}		)
   UpperCAmelCase__  :    Optional[Any]          =  IMAGE_TO_IMAGE_IMAGE_PARAMS







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							UNetaDConditionModel(
            block_out_channels=(32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=4					,						out_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,						cross_attention_dim=32					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							ControlNetModel(
            block_out_channels=(32, 64)					,						layers_per_block=2					,						in_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						cross_attention_dim=32					,						conditioning_embedding_out_channels=(16, 32)					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							DDIMScheduler(
            beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=_snake_case					,						set_alpha_to_one=_snake_case					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							AutoencoderKL(
            block_out_channels=[32, 64]					,						in_channels=3					,						out_channels=3					,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,						latent_channels=4					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							CLIPTextConfig(
            bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1e-05					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1000					,						)
        UpperCAmelCase_          =							CLIPTextModel(_snake_case)
        UpperCAmelCase_          =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        UpperCAmelCase_          =							{
            '''unet''': unet,
            '''controlnet''': controlnet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						Dict=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_          =							2
        UpperCAmelCase_          =							randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,						generator=_snake_case					,						device=torch.device(_snake_case)					,						)
        UpperCAmelCase_          =							floats_tensor(control_image.shape					,						rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1)[0]
        UpperCAmelCase_          =							Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_          =							{
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
            '''image''': image,
            '''control_image''': control_image,
        }

        return inputs







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)







   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available()					,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,						)
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self._test_inference_batch_single_identical(expected_max_diff=2e-3)







class __snake_case			(			a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    str          =  StableDiffusionControlNetImgaImgPipeline
   UpperCAmelCase__  :    List[str]          =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCAmelCase__  :    str          =  frozenset([]		)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							UNetaDConditionModel(
            block_out_channels=(32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=4					,						out_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,						cross_attention_dim=32					,						)
        torch.manual_seed(0)

        def init_weights(_snake_case		:						Optional[int]):
             if isinstance(_snake_case					,						torch.nn.Convad):
                  torch.nn.init.normal(m.weight)
                  m.bias.data.fill_(1.0)

        UpperCAmelCase_          =							ControlNetModel(
            block_out_channels=(32, 64)					,						layers_per_block=2					,						in_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						cross_attention_dim=32					,						conditioning_embedding_out_channels=(16, 32)					,						)
        controlneta.controlnet_down_blocks.apply(_snake_case)

        torch.manual_seed(0)
        UpperCAmelCase_          =							ControlNetModel(
            block_out_channels=(32, 64)					,						layers_per_block=2					,						in_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						cross_attention_dim=32					,						conditioning_embedding_out_channels=(16, 32)					,						)
        controlneta.controlnet_down_blocks.apply(_snake_case)

        torch.manual_seed(0)
        UpperCAmelCase_          =							DDIMScheduler(
            beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=_snake_case					,						set_alpha_to_one=_snake_case					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							AutoencoderKL(
            block_out_channels=[32, 64]					,						in_channels=3					,						out_channels=3					,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,						latent_channels=4					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							CLIPTextConfig(
            bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1e-05					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1000					,						)
        UpperCAmelCase_          =							CLIPTextModel(_snake_case)
        UpperCAmelCase_          =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        UpperCAmelCase_          =							MultiControlNetModel([controlneta, controlneta])

        UpperCAmelCase_          =							{
            '''unet''': unet,
            '''controlnet''': controlnet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						str=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_          =							2

        UpperCAmelCase_          =							[
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,						generator=_snake_case					,						device=torch.device(_snake_case)					,						),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,						generator=_snake_case					,						device=torch.device(_snake_case)					,						),
        ]

        UpperCAmelCase_          =							floats_tensor(control_image[0].shape					,						rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1)[0]
        UpperCAmelCase_          =							Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_          =							{
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
            '''image''': image,
            '''control_image''': control_image,
        }

        return inputs







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_dummy_components()
        UpperCAmelCase_          =							self.pipeline_class(**_snake_case)
        pipe.to(_snake_case)

        UpperCAmelCase_          =							1_0.0
        UpperCAmelCase_          =							4

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case)[0]

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case					,						control_guidance_start=0.1					,						control_guidance_end=0.2)[0]

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case					,						control_guidance_start=[0.1, 0.3]					,						control_guidance_end=[0.2, 0.7])[0]

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case					,						control_guidance_start=0.4					,						control_guidance_end=[0.5, 0.8])[0]

        # make sure that all outputs are different
        assert np.sum(np.abs(output_a - output_a)) > 1e-3
        assert np.sum(np.abs(output_a - output_a)) > 1e-3
        assert np.sum(np.abs(output_a - output_a)) > 1e-3







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)







   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available()					,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,						)
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        self._test_inference_batch_single_identical(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_dummy_components()
        UpperCAmelCase_          =							self.pipeline_class(**_snake_case)
        pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)
        with tempfile.TemporaryDirectory() as tmpdir:
             try:
                  # save_pretrained is not implemented for Multi-ControlNet
                  pipe.save_pretrained(_snake_case)
             except NotImplementedError:
                  pass







@slow
@require_torch_gpu
class __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny''')

        UpperCAmelCase_          =							StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''					,						safety_checker=_snake_case					,						controlnet=_snake_case)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_          =							torch.Generator(device='''cpu''').manual_seed(0)
        UpperCAmelCase_          =							'''evil space-punk bird'''
        UpperCAmelCase_          =							load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''').resize((512, 512))
        UpperCAmelCase_          =							load_image(
            '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png''').resize((512, 512))

        UpperCAmelCase_          =							pipe(
            _snake_case					,						_snake_case					,						control_image=_snake_case					,						generator=_snake_case					,						output_type='''np'''					,						num_inference_steps=50					,						strength=0.6					,						)

        UpperCAmelCase_          =							output.images[0]

        assert image.shape == (512, 512, 3)

        UpperCAmelCase_          =							load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy''')

        assert np.abs(expected_image - image).max() < 9e-2






