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 						a__							(       __snake_case				,						unittest.TestCase		):
			A__    :       Any        =      DiTPipeline
			A__    :       List[Any]        =      CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
			A__    :       Any        =      PipelineTesterMixin.required_optional_params - {
			    'latents',
			    'num_images_per_prompt',
			    'callback',
			    'callback_steps',
			}
			A__    :       Optional[Any]        =      CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
			A__    :       List[str]        =      False



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								torch.manual_seed(0   )
								__a		 =       TransformeraDModel(
								    sample_size=1_6				,						num_layers=2				,						patch_size=4				,						attention_head_dim=8				,						num_attention_heads=2				,						in_channels=4				,						out_channels=8				,						attention_bias=UpperCAmelCase				,						activation_fn='gelu-approximate'				,						num_embeds_ada_norm=1_0_0_0				,						norm_type='ada_norm_zero'				,						norm_elementwise_affine=UpperCAmelCase				,						)
								__a		 =       AutoencoderKL()
								__a		 =       DDIMScheduler()
								__a		 =       {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
								return components



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=0   )			->   Tuple:
								if str(UpperCAmelCase   ).startswith('mps'   ):
													__a		 =       torch.manual_seed(UpperCAmelCase   )
								else:
													__a		 =       torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
								__a		 =       {
								    'class_labels': [1],
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}
								return inputs



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       'cpu'

								__a		 =       self.get_dummy_components()
								__a		 =       self.pipeline_class(**UpperCAmelCase   )
								pipe.to(UpperCAmelCase   )
								pipe.set_progress_bar_config(disable=UpperCAmelCase   )

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

								self.assertEqual(image.shape				,						(1, 1_6, 1_6, 3)   )
								__a		 =       np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]   )
								__a		 =       np.abs(image_slice.flatten() - expected_slice   ).max()
								self.assertLessEqual(UpperCAmelCase				,						1e-3   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase				,						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   )			->   str:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )






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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       torch.manual_seed(0   )

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

								__a		 =       ['vase', 'umbrella', 'white shark', 'white wolf']
								__a		 =       pipe.get_label_ids(UpperCAmelCase   )

								__a		 =       pipe(UpperCAmelCase				,						generator=UpperCAmelCase				,						num_inference_steps=4_0				,						output_type='np'   ).images

								for word, image in zip(UpperCAmelCase				,						UpperCAmelCase   ):
													__a		 =       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   )			->   Any:
								__a		 =       DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'   )
								__a		 =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
								pipe.to('cuda'   )

								__a		 =       ['vase', 'umbrella']
								__a		 =       pipe.get_label_ids(UpperCAmelCase   )

								__a		 =       torch.manual_seed(0   )
								__a		 =       pipe(UpperCAmelCase				,						generator=UpperCAmelCase				,						num_inference_steps=2_5				,						output_type='np'   ).images

								for word, image in zip(UpperCAmelCase				,						UpperCAmelCase   ):
													__a		 =       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 datetime import datetime

import matplotlib.pyplot as plt
import torch
def 							lowerCAmelCase(     __lowerCamelCase						):
					for param in module.parameters():
										__a		 =       False
def 							lowerCAmelCase(     ):
					__a		 =       'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										__a		 =       'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'						)
					return device
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       plt.imshow(__lowerCamelCase						)
					fig.axes.get_xaxis().set_visible(__lowerCamelCase						)
					fig.axes.get_yaxis().set_visible(__lowerCamelCase						)
					plt.show()



def 							lowerCAmelCase(     ):
					__a		 =       datetime.now()
					__a		 =       current_time.strftime('%H:%M:%S'						)
					return timestamp




