

import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Union[str, Any]      							=					DiTPipeline
			_UpperCAmelCase       :							Union[str, Any]      							=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
			_UpperCAmelCase       :							int      							=					PipelineTesterMixin.required_optional_params - {
			    '''latents''',
			    '''num_images_per_prompt''',
			    '''callback''',
			    '''callback_steps''',
			}
			_UpperCAmelCase       :							Dict      							=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
			_UpperCAmelCase       :							List[Any]      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			str  		=	TransformeraDModel(
									    sample_size=16       ,						num_layers=2       ,						patch_size=4       ,						attention_head_dim=8       ,						num_attention_heads=2       ,						in_channels=4       ,						out_channels=8       ,						attention_bias=lowerCAmelCase__       ,						activation_fn="gelu-approximate"       ,						num_embeds_ada_norm=1000       ,						norm_type="ada_norm_zero"       ,						norm_elementwise_affine=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoencoderKL()
									SCREAMING_SNAKE_CASE_:			int  		=	DDIMScheduler()
									SCREAMING_SNAKE_CASE_:			Dict  		=	{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
									return components



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[str]=0):
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	{
									    "class_labels": [1],
									    "generator": generator,
									    "num_inference_steps": 2,
									    "output_type": "numpy",
									}
									return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									SCREAMING_SNAKE_CASE_:			Any  		=	"cpu"

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:			str  		=	self.pipeline_class(**lowerCAmelCase__)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Any  		=	image[0, -3:, -3:, -1]

									self.assertEqual(image.shape       ,						(1, 16, 16, 3))
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457])
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	np.abs(image_slice.flatten() - expected_slice).max()
									self.assertLessEqual(lowerCAmelCase__       ,						1E-3)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									self._test_inference_batch_single_identical(relax_max_difference=lowerCAmelCase__       ,						expected_max_diff=1E-3)




			@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  :					List[Any]):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)







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




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.manual_seed(0)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	DiTPipeline.from_pretrained("facebook/DiT-XL-2-256")
									pipe.to("cuda")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	["vase", "umbrella", "white shark", "white wolf"]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pipe.get_label_ids(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pipe(lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						num_inference_steps=40       ,						output_type="np").images

									for word, image in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			str  		=	load_numpy(
															    F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy")
															assert np.abs((expected_image - image).max()) < 1E-2




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Any  		=	DiTPipeline.from_pretrained("facebook/DiT-XL-2-512")
									SCREAMING_SNAKE_CASE_:			str  		=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
									pipe.to("cuda")

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	["vase", "umbrella"]
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	pipe.get_label_ids(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	pipe(lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						num_inference_steps=25       ,						output_type="np").images

									for word, image in zip(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_numpy(
															    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
															    F"/dit/{word}_512.npy")

															assert np.abs((expected_image - image).max()) < 1E-1









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCAmelCase							:  Optional[Any]            =						logging.get_logger(__name__)



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					['''pixel_values''']



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Dict[str, int] = None       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Union[int, float] = 1 / 255       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					bool = True       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else {"height": 384, "width": 384}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Dict  		=	size
									SCREAMING_SNAKE_CASE_:			int  		=	resample
									SCREAMING_SNAKE_CASE_:			str  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			str  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_std if image_std is not None else OPENAI_CLIP_STD
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Dict[str, int]       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									if "height" not in size or "width" not in size:
															raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
									SCREAMING_SNAKE_CASE_:			int  		=	(size["height"], size["width"])
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[int, float]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return rescale(lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return normalize(lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					ImageInput       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Dict[str, int]] = None       ,						lowerCAmelCase__  :					PILImageResampling = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[float] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[str, TensorType]] = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					ChannelDimension = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Dict       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Any  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	make_list_of_images(lowerCAmelCase__)

									if not valid_images(lowerCAmelCase__):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray.")

									if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True.")

									if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True.")

									if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True.")

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[convert_to_rgb(lowerCAmelCase__) for image in images]

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[to_numpy_array(lowerCAmelCase__) for image in images]

									if do_resize:
															SCREAMING_SNAKE_CASE_:			int  		=	[self.resize(image=lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__) for image in images]

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__) for image in images]

									if do_normalize:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.normalize(image=lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			str  		=	[to_channel_dimension_format(lowerCAmelCase__       ,						lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BatchFeature(data={"pixel_values": images}       ,						tensor_type=lowerCAmelCase__)

									return encoded_outputs






