
'''simple docstring'''







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 			lowerCAmelCase (				a__		,     unittest.TestCase			):
		a							:					Union[str, Any]       =					DiTPipeline
		a							:					Union[str, Any]       =					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
		a							:					Optional[int]       =					PipelineTesterMixin.required_optional_params - {
		    """latents""",
		    """num_images_per_prompt""",
		    """callback""",
		    """callback_steps""",
		}
		a							:					List[str]       =					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
		a							:					Dict       =					False




		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= 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=_A					,						activation_fn="gelu-approximate"					,						num_embeds_ada_norm=1_000					,						norm_type="ada_norm_zero"					,						norm_elementwise_affine=_A					,						)
								_SCREAMING_SNAKE_CASE				= AutoencoderKL()
								_SCREAMING_SNAKE_CASE				= DDIMScheduler()
								_SCREAMING_SNAKE_CASE				= {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
								return components




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):

								if str(_A      ).startswith("mps"      ):
														_SCREAMING_SNAKE_CASE				= torch.manual_seed(_A      )
								else:
														_SCREAMING_SNAKE_CASE				= torch.Generator(device=_A      ).manual_seed(_A      )
								_SCREAMING_SNAKE_CASE				= {
								    'class_labels': [1],
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}
								return inputs




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= 'cpu'

								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**_A      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(_A      )
								_SCREAMING_SNAKE_CASE				= pipe(**_A      ).images
								_SCREAMING_SNAKE_CASE				= image[0, -3:, -3:, -1]

								self.assertEqual(image.shape					,						(1, 16, 16, 3)      )
								_SCREAMING_SNAKE_CASE				= np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]      )
								_SCREAMING_SNAKE_CASE				= np.abs(image_slice.flatten() - expected_slice      ).max()
								self.assertLessEqual(_A					,						1e-3      )




		def     lowercase       (				self      ):
								self._test_inference_batch_single_identical(relax_max_difference=_A					,						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     lowercase       (				self      ):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )


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




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= torch.manual_seed(0      )

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

								_SCREAMING_SNAKE_CASE				= ['vase', 'umbrella', 'white shark', 'white wolf']
								_SCREAMING_SNAKE_CASE				= pipe.get_label_ids(_A      )

								_SCREAMING_SNAKE_CASE				= pipe(_A					,						generator=_A					,						num_inference_steps=40					,						output_type="np"      ).images

								for word, image in zip(_A					,						_A      ):
														_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"      )
								_SCREAMING_SNAKE_CASE				= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
								pipe.to("cuda"      )

								_SCREAMING_SNAKE_CASE				= ['vase', 'umbrella']
								_SCREAMING_SNAKE_CASE				= pipe.get_label_ids(_A      )

								_SCREAMING_SNAKE_CASE				= torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= pipe(_A					,						generator=_A					,						num_inference_steps=25					,						output_type="np"      ).images

								for word, image in zip(_A					,						_A      ):
														_SCREAMING_SNAKE_CASE				= 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

'''simple docstring'''







import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 			lowerCAmelCase :




		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(
								    sample_size=32					,						layers_per_block=1					,						block_out_channels=[32, 64]					,						down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]					,						mid_block_type="UNetMidBlock2DSimpleCrossAttn"					,						up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]					,						in_channels=3					,						out_channels=6					,						cross_attention_dim=32					,						encoder_hid_dim=32					,						attention_head_dim=8					,						addition_embed_type="text"					,						addition_embed_type_num_heads=2					,						cross_attention_norm="group_norm"					,						resnet_time_scale_shift="scale_shift"					,						act_fn="gelu"					,						)
								unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						thresholding=UpperCamelCase					,						dynamic_thresholding_ratio=0.95					,						sample_max_value=1.0					,						prediction_type="epsilon"					,						variance_type="learned_range"					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}




		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(
								    sample_size=32					,						layers_per_block=[1, 2]					,						block_out_channels=[32, 64]					,						down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]					,						mid_block_type="UNetMidBlock2DSimpleCrossAttn"					,						up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]					,						in_channels=6					,						out_channels=6					,						cross_attention_dim=32					,						encoder_hid_dim=32					,						attention_head_dim=8					,						addition_embed_type="text"					,						addition_embed_type_num_heads=2					,						cross_attention_norm="group_norm"					,						resnet_time_scale_shift="scale_shift"					,						act_fn="gelu"					,						class_embed_type="timestep"					,						mid_block_scale_factor=1.4_14					,						time_embedding_act_fn="gelu"					,						time_embedding_dim=32					,						)
								unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						thresholding=UpperCamelCase					,						dynamic_thresholding_ratio=0.95					,						sample_max_value=1.0					,						prediction_type="epsilon"					,						variance_type="learned_range"					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= inputs["prompt"]
								_SCREAMING_SNAKE_CASE				= inputs["generator"]
								_SCREAMING_SNAKE_CASE				= inputs["num_inference_steps"]
								_SCREAMING_SNAKE_CASE				= inputs["output_type"]

								if "image" in inputs:
														_SCREAMING_SNAKE_CASE				= inputs["image"]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if "mask_image" in inputs:
														_SCREAMING_SNAKE_CASE				= inputs["mask_image"]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if "original_image" in inputs:
														_SCREAMING_SNAKE_CASE				= inputs["original_image"]
								else:
														_SCREAMING_SNAKE_CASE				= None

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= pipe.encode_prompt(UpperCamelCase      )

								# inputs with prompt converted to embeddings
								_SCREAMING_SNAKE_CASE				= {
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
														_SCREAMING_SNAKE_CASE				= image

								if mask_image is not None:
														_SCREAMING_SNAKE_CASE				= mask_image

								if original_image is not None:
														_SCREAMING_SNAKE_CASE				= original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
														setattr(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.pipeline_class.from_pretrained(UpperCamelCase      )
														pipe_loaded.to(UpperCamelCase      )
														pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								for optional_component in pipe._optional_components:
														self.assertTrue(
														    getattr(UpperCamelCase					,						UpperCamelCase      ) is None					,						F'`{optional_component}` did not stay set to None after loading.'					,						)

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= inputs["generator"]
								_SCREAMING_SNAKE_CASE				= inputs["num_inference_steps"]
								_SCREAMING_SNAKE_CASE				= inputs["output_type"]

								# inputs with prompt converted to embeddings
								_SCREAMING_SNAKE_CASE				= {
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
														_SCREAMING_SNAKE_CASE				= image

								if mask_image is not None:
														_SCREAMING_SNAKE_CASE				= mask_image

								if original_image is not None:
														_SCREAMING_SNAKE_CASE				= original_image

								_SCREAMING_SNAKE_CASE				= pipe_loaded(**UpperCamelCase      )[0]

								_SCREAMING_SNAKE_CASE				= np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
								self.assertLess(UpperCamelCase					,						1e-4      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.pipeline_class.from_pretrained(UpperCamelCase      )
														pipe_loaded.to(UpperCamelCase      )
														pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe_loaded(**UpperCamelCase      )[0]

								_SCREAMING_SNAKE_CASE				= np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
								self.assertLess(UpperCamelCase					,						1e-4      )