




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Dict    , __snake_case   :TransformeraDModel    , __snake_case   :AutoencoderKL    , __snake_case   :KarrasDiffusionSchedulers    , __snake_case   :Optional[Dict[int, str]] = None    , ):
										'''simple docstring'''

										super().__init__()
										self.register_modules(transformer=__snake_case    , vae=__snake_case    , scheduler=__snake_case )

										# create a imagenet -> id dictionary for easier use
										__magic_name__	:	List[str]								={}
										if idalabel is not None:
														for key, value in idalabel.items():
																		for label in value.split(""",""" ):
																						__magic_name__	:	Optional[Any]								=int(__snake_case )
														__magic_name__	:	Optional[Any]								=dict(sorted(self.labels.items() ) )

						def     A__						(		self   :Optional[int]    , __snake_case   :Union[str, List[str]] ):
										'''simple docstring'''

										if not isinstance(__snake_case    , __snake_case ):
														__magic_name__	:	Dict								=list(__snake_case )

										for l in label:
														if l not in self.labels:
																		raise ValueError(
																		    f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}." )

										return [self.labels[l] for l in label]


						@torch.no_grad()
						def __call__(		self   :Optional[Any]    , __snake_case   :List[int]    , __snake_case   :float = 4.0    , __snake_case   :Optional[Union[torch.Generator, List[torch.Generator]]] = None    , __snake_case   :int = 50    , __snake_case   :Optional[str] = "pil"    , __snake_case   :bool = True    , ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=len(__snake_case )
										__magic_name__	:	int								=self.transformer.config.sample_size
										__magic_name__	:	Tuple								=self.transformer.config.in_channels

										__magic_name__	:	int								=randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)    , generator=__snake_case    , device=self.device    , dtype=self.transformer.dtype    , )
										__magic_name__	:	Optional[Any]								=torch.cat([latents] * 2 ) if guidance_scale > 1 else latents

										__magic_name__	:	str								=torch.tensor(__snake_case    , device=self.device ).reshape(-1 )
										__magic_name__	:	Union[str, Any]								=torch.tensor([10_00] * batch_size    , device=self.device )
										__magic_name__	:	Optional[int]								=torch.cat([class_labels, class_null]    , 0 ) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__snake_case )

										for t in self.progress_bar(self.scheduler.timesteps ):
														if guidance_scale > 1:
																		__magic_name__	:	Dict								=latent_model_input[: len(__snake_case ) // 2]
																		__magic_name__	:	Tuple								=torch.cat([half, half]    , dim=0 )
														__magic_name__	:	Optional[Any]								=self.scheduler.scale_model_input(__snake_case    , __snake_case )

														__magic_name__	:	List[Any]								=t
														if not torch.is_tensor(__snake_case ):
																		# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																		# This would be a good case for the `match` statement (Python 3.10+)
																		__magic_name__	:	str								=latent_model_input.device.type == """mps"""
																		if isinstance(__snake_case    , __snake_case ):
																						__magic_name__	:	int								=torch.floataa if is_mps else torch.floataa
																		else:
																						__magic_name__	:	List[str]								=torch.intaa if is_mps else torch.intaa
																		__magic_name__	:	List[str]								=torch.tensor([timesteps]    , dtype=__snake_case    , device=latent_model_input.device )
														elif len(timesteps.shape ) == 0:
																		__magic_name__	:	Any								=timesteps[None].to(latent_model_input.device )
														# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
														__magic_name__	:	List[str]								=timesteps.expand(latent_model_input.shape[0] )
														# predict noise model_output
														__magic_name__	:	Optional[int]								=self.transformer(
														    __snake_case    , timestep=__snake_case    , class_labels=__snake_case ).sample

														# perform guidance
														if guidance_scale > 1:
																		__magic_name__				, __magic_name__	:	Tuple								=noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																		__magic_name__				, __magic_name__	:	Tuple								=torch.split(__snake_case    , len(__snake_case ) // 2    , dim=0 )

																		__magic_name__	:	Optional[Any]								=uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																		__magic_name__	:	Union[str, Any]								=torch.cat([half_eps, half_eps]    , dim=0 )

																		__magic_name__	:	List[str]								=torch.cat([eps, rest]    , dim=1 )

														# learned sigma
														if self.transformer.config.out_channels // 2 == latent_channels:
																		__magic_name__				, __magic_name__	:	str								=torch.split(__snake_case    , __snake_case    , dim=1 )
														else:
																		__magic_name__	:	Optional[int]								=noise_pred

														# compute previous image: x_t -> x_t-1
														__magic_name__	:	Optional[Any]								=self.scheduler.step(__snake_case    , __snake_case    , __snake_case ).prev_sample

										if guidance_scale > 1:
														__magic_name__				, __magic_name__	:	Any								=latent_model_input.chunk(2    , dim=0 )
										else:
														__magic_name__	:	Optional[Any]								=latent_model_input

										__magic_name__	:	int								=1 / self.vae.config.scaling_factor * latents
										__magic_name__	:	Any								=self.vae.decode(__snake_case ).sample

										__magic_name__	:	Optional[Any]								=(samples / 2 + 0.5).clamp(0    , 1 )

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__magic_name__	:	Union[str, Any]								=samples.cpu().permute(0    , 2    , 3    , 1 ).float().numpy()

										if output_type == "pil":
														__magic_name__	:	Optional[int]								=self.numpy_to_pil(__snake_case )

										if not return_dict:
														return (samples,)

										return ImagePipelineOutput(images=__snake_case )










import pytest

import datasets


# Import fixture modules as plugins
UpperCAmelCase_		:				Union[str, Any]									=     ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
				for item in items:
								if any(marker in item.keywords for marker in ["""integration""", """unit"""]   ):
												continue
								item.add_marker(pytest.mark.unit   )




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				config.addinivalue_line("""markers"""	,     """torchaudio_latest: mark test to run with torchaudio>=0.12"""   )




@pytest.fixture(autouse=lowerCamelCase   )
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
				__magic_name__	:	Tuple								=tmp_path_factory.getbasetemp() / """cache"""
				__magic_name__	:	Optional[int]								=test_hf_cache_home / """datasets"""
				__magic_name__	:	List[str]								=test_hf_cache_home / """metrics"""
				__magic_name__	:	List[str]								=test_hf_cache_home / """modules"""
				monkeypatch.setattr("""datasets.config.HF_DATASETS_CACHE"""	,     str(lowerCamelCase   )   )
				monkeypatch.setattr("""datasets.config.HF_METRICS_CACHE"""	,     str(lowerCamelCase   )   )
				monkeypatch.setattr("""datasets.config.HF_MODULES_CACHE"""	,     str(lowerCamelCase   )   )
				__magic_name__	:	Optional[Any]								=test_hf_datasets_cache / """downloads"""
				monkeypatch.setattr("""datasets.config.DOWNLOADED_DATASETS_PATH"""	,     str(lowerCamelCase   )   )
				__magic_name__	:	Tuple								=test_hf_datasets_cache / """downloads""" / """extracted"""
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""	,     str(lowerCamelCase   )   )




@pytest.fixture(autouse=lowerCamelCase	,     scope="""session"""   )
def 		lowerCAmelCase_   (	):
				datasets.disable_progress_bar()




@pytest.fixture(autouse=lowerCamelCase   )
def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# don't take tests into account when counting downloads
				monkeypatch.setattr("""datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"""	,     lowerCamelCase   )




@pytest.fixture
def 		lowerCAmelCase_   (	lowerCamelCase   ):
				# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
				# To be removed once SQLAlchemy 2.0 supported
				monkeypatch.setattr("""sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"""	,     lowerCamelCase   )




