



"""simple docstring"""






import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class 			__SCREAMING_SNAKE_CASE      ( enum.Enum  ):


					'''simple docstring'''





					_a       =				'timm'
					_a       =				'transformers'






def 	snake_case__  (						__lowerCamelCase							:       Optional[Iterable[str]]				,						__lowerCamelCase							:       Optional[Iterable[int]]				,						__lowerCamelCase							:       Optional[Iterable[str]]      ):
			"""simple docstring"""
			if stage_names is None:
						raise ValueError('''Stage_names must be set for transformers backbones'''      )

			if out_features is not None:
						if not isinstance(__lowerCamelCase				,						(list,)      ):
									raise ValueError(f'''out_features must be a list {type(__lowerCamelCase      )}'''      )
						if any(feat not in stage_names for feat in out_features      ):
									raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''      )

			if out_indices is not None:
						if not isinstance(__lowerCamelCase				,						(list, tuple)      ):
									raise ValueError(f'''out_indices must be a list or tuple, got {type(__lowerCamelCase      )}'''      )
						if any(idx >= len(__lowerCamelCase      ) for idx in out_indices      ):
									raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''      )

			if out_features is not None and out_indices is not None:
						if len(__lowerCamelCase      ) != len(__lowerCamelCase      ):
									raise ValueError('''out_features and out_indices should have the same length if both are set'''      )
						if out_features != [stage_names[idx] for idx in out_indices]:
									raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''      )






def 	snake_case__  (						__lowerCamelCase							:       Optional[List[str]]				,						__lowerCamelCase							:       Optional[Union[List[int], Tuple[int]]]				,						__lowerCamelCase							:       List[str]				,						):
			"""simple docstring"""
			if out_indices is None and out_features is None:
						lowerCamelCase__						:						Union[str, Any]						      =[len(__lowerCamelCase      ) - 1]
						lowerCamelCase__						:						Any						      =[stage_names[-1]]
			elif out_indices is None and out_features is not None:
						lowerCamelCase__						:						List[Any]						      =[stage_names.index(__lowerCamelCase      ) for layer in out_features]
			elif out_features is None and out_indices is not None:
						lowerCamelCase__						:						Tuple						      =[stage_names[idx] for idx in out_indices]
			return out_features, out_indices






def 	snake_case__  (						__lowerCamelCase							:       Optional[List[str]]				,						__lowerCamelCase							:       Optional[Union[List[int], Tuple[int]]]				,						__lowerCamelCase							:       List[str]				,						):
			"""simple docstring"""
			verify_out_features_out_indices(out_features=__lowerCamelCase				,						out_indices=__lowerCamelCase				,						stage_names=__lowerCamelCase      )
			lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =_align_output_features_output_indices(
			    out_features=__lowerCamelCase				,						out_indices=__lowerCamelCase				,						stage_names=__lowerCamelCase      )
			# Verify that the aligned out_features and out_indices are valid
			verify_out_features_out_indices(out_features=__lowerCamelCase				,						out_indices=__lowerCamelCase				,						stage_names=__lowerCamelCase      )
			return output_features, output_indices

class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					_a       =				None



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    Optional[int]	)->   None:
								if getattr(self,    '''_backbone''',    lowerCamelCase	) is None:
											raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''	)

								# These will diagree with the defaults for the transformers models e.g. for resnet50
								# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
								# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
								lowerCamelCase__						:						List[str]						      =[stage['''module'''] for stage in self._backbone.feature_info.info]
								lowerCamelCase__						:						Dict						      =[stage['''num_chs'''] for stage in self._backbone.feature_info.info]
								lowerCamelCase__						:						Any						      =self._backbone.feature_info.out_indices
								lowerCamelCase__						:						Union[str, Any]						      =self._backbone.feature_info.module_name()

								# We verify the out indices and out features are valid
								verify_out_features_out_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)
								lowerCamelCase__      ,  lowerCamelCase__						:						List[Any]						      =out_features, out_indices



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    List[Any]	)->   None:
								lowerCamelCase__						:						List[Any]						      =getattr(lowerCamelCase,    '''stage_names'''	)
								lowerCamelCase__						:						Any						      =getattr(lowerCamelCase,    '''out_features''',    lowerCamelCase	)
								lowerCamelCase__						:						Union[str, Any]						      =getattr(lowerCamelCase,    '''out_indices''',    lowerCamelCase	)

								lowerCamelCase__						:						Tuple						      =stage_names
								lowerCamelCase__      ,  lowerCamelCase__						:						Any						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=lowerCamelCase	)
								# Number of channels for each stage. This is set in the transformer backbone model init
								lowerCamelCase__						:						Union[str, Any]						      =None



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str	)->   None:
								lowerCamelCase__						:						List[str]						      =config

								lowerCamelCase__						:						List[str]						      =getattr(lowerCamelCase,    '''use_timm_backbone''',    lowerCamelCase	)
								lowerCamelCase__						:						Tuple						      =BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

								if self.backbone_type == BackboneType.TIMM:
											self._init_timm_backbone(lowerCamelCase	)
								elif self.backbone_type == BackboneType.TRANSFORMERS:
											self._init_transformers_backbone(lowerCamelCase	)
								else:
											raise ValueError(F'''backbone_type {self.backbone_type} not supported.'''	)



					@property
					def 						snake_case			(			self				:    Union[str, Any]	)->   Optional[int]:
								return self._out_features



					@out_features.setter
					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    List[str]	)->   Optional[Any]:
								lowerCamelCase__      ,  lowerCamelCase__						:						Optional[Any]						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)



					@property
					def 						snake_case			(			self				:    Dict	)->   Tuple:
								return self._out_indices



					@out_indices.setter
					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    Union[Tuple[int], List[int]]	)->   List[Any]:
								lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)



					@property
					def 						snake_case			(			self				:    List[Any]	)->   List[Any]:
								# the current backbones will output the number of channels for each stage
								# even if that stage is not in the out_features list.
								return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names	)}



					@property
					def 						snake_case			(			self				:    int	)->   Optional[int]:
								return [self.out_feature_channels[name] for name in self.out_features]



					def 						snake_case			(			self				:    List[str],    *lowerCamelCase				:    Tuple,    **lowerCamelCase				:    Dict	)->   int:
								lowerCamelCase__						:						Optional[Any]						      =dict(inspect.signature(self.forward	).parameters	)
								lowerCamelCase__						:						Optional[int]						      ={k: v for k, v in kwargs.items() if k in signature}
								return self(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    Optional[Any],    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    Optional[bool] = None,    )->   Optional[int]:
								raise NotImplementedError('''This method should be implemented by the derived class.'''	)




					def 						snake_case			(			self				:    Union[str, Any]	)->   Tuple:
								lowerCamelCase__						:						Any						      =super().to_dict()
								lowerCamelCase__						:						Optional[Any]						      =output.pop('''_out_features'''	)
								lowerCamelCase__						:						List[str]						      =output.pop('''_out_indices'''	)
								return output





class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					@property
					def 						snake_case			(			self				:    Optional[int]	)->   int:
								return self._out_features



					@out_features.setter
					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    List[str]	)->   Any:
								lowerCamelCase__      ,  lowerCamelCase__						:						Any						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)



					@property
					def 						snake_case			(			self				:    List[Any]	)->   Tuple:
								return self._out_indices



					@out_indices.setter
					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    Union[Tuple[int], List[int]]	)->   List[Any]:
								lowerCamelCase__      ,  lowerCamelCase__						:						str						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)




					def 						snake_case			(			self				:    Any	)->   int:
								lowerCamelCase__						:						int						      =super().to_dict()
								lowerCamelCase__						:						List[str]						      =output.pop('''_out_features'''	)
								lowerCamelCase__						:						Optional[Any]						      =output.pop('''_out_indices'''	)
								return output






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						SCREAMING_SNAKE_CASE		,						unittest.TestCase				):
						__A:						Optional[Any]				   =      StableDiffusionParadigmsPipeline
						__A:						Optional[int]				   =      TEXT_TO_IMAGE_PARAMS
						__A:						Dict				   =      TEXT_TO_IMAGE_BATCH_PARAMS
						__A:						Any				   =      TEXT_TO_IMAGE_IMAGE_PARAMS
						__A:						List[Any]				   =      TEXT_TO_IMAGE_IMAGE_PARAMS

						def 						a__ (					self		:			Optional[Any]			):
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		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	,						attention_head_dim=(2, 4)	,						use_linear_projection=_lowerCamelCase	,						)
													_UpperCAmelCase  :			Dict	       =		DDIMParallelScheduler(
													    beta_start=0.0_00_85	,						beta_end=0.0_12	,						beta_schedule="scaled_linear"	,						clip_sample=_lowerCamelCase	,						set_alpha_to_one=_lowerCamelCase	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Tuple	       =		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	,						sample_size=1_28	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Optional[Any]	       =		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=10_00	,						hidden_act="gelu"	,						projection_dim=5_12	,						)
													_UpperCAmelCase  :			Tuple	       =		CLIPTextModel(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													_UpperCAmelCase  :			Optional[int]	       =		{
													    "unet": unet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components

						def 						a__ (					self		:			Dict	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			int=0			):
													if str(_lowerCamelCase			).startswith("mps"			):
																				_UpperCAmelCase  :			Tuple	       =		torch.manual_seed(_lowerCamelCase			)
													else:
																				_UpperCAmelCase  :			Optional[int]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 6.0,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Union[str, Any]	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			List[str]	       =		self.get_dummy_components()
													_UpperCAmelCase  :			List[Any]	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			str	       =		np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Dict	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Dict	       =		self.get_dummy_components()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		DDPMParallelScheduler()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			int	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			Any	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			int	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			str	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_consistent(batch_sizes=[1, 2]			)

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_single_identical(batch_size=2	,						expected_max_diff=3E-3			)






						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			int	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Tuple	       =		self.get_dummy_components()
													_UpperCAmelCase  :			Dict	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			Optional[int]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		"french fries"
													_UpperCAmelCase  :			Tuple	       =		sd_pipe(**_lowerCamelCase	,						negative_prompt=_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		output.images
													_UpperCAmelCase  :			Union[str, Any]	       =		image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







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

						def 						a__ (					self		:			int			):
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			Optional[int]=0			):
													_UpperCAmelCase  :			Union[str, Any]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 7.5,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs






						def 						a__ (					self		:			Any			):
													_UpperCAmelCase  :			str	       =		"stabilityai/stable-diffusion-2-base"
													_UpperCAmelCase  :			List[str]	       =		DDIMParallelScheduler.from_pretrained(_lowerCamelCase	,						subfolder="scheduler"			)
													_UpperCAmelCase  :			Union[str, Any]	       =		StableDiffusionParadigmsPipeline.from_pretrained(_lowerCamelCase	,						scheduler=_lowerCamelCase	,						safety_checker=_lowerCamelCase			)
													pipe.to(_lowerCamelCase			)
													pipe.set_progress_bar_config(disable=_lowerCamelCase			)
													pipe.enable_attention_slicing()

													_UpperCAmelCase  :			Optional[Any]	       =		self.get_inputs()
													_UpperCAmelCase  :			List[str]	       =		pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 5_12, 5_12, 3)

													_UpperCAmelCase  :			Union[str, Any]	       =		np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]			)

													assert np.abs(expected_slice - image_slice			).max() < 1E-2




