
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

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


enable_full_determinism()







@skip_mps
class        lowercase	(      SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Dict       =TextToVideoSDPipeline
							lowercase_ : Optional[Any]       =TEXT_TO_IMAGE_PARAMS
							lowercase_ : str       =TEXT_TO_IMAGE_BATCH_PARAMS
							# No `output_type`.
							lowercase_ : int       =frozenset(
							    [
							        '''num_inference_steps''',
							        '''generator''',
							        '''latents''',
							        '''return_dict''',
							        '''callback''',
							        '''callback_steps''',
							    ]	)




							def 				A__   (       self):
												torch.manual_seed(0)
												lowercase					=		UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4, 6_4, 6_4)     ,layers_per_block=2     ,sample_size=3_2     ,in_channels=4     ,out_channels=4     ,down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''')     ,up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''')     ,cross_attention_dim=3_2     ,attention_head_dim=4     ,)
												lowercase					=		DDIMScheduler(
												    beta_start=0.00085     ,beta_end=0.012     ,beta_schedule='''scaled_linear'''     ,clip_sample=A__     ,set_alpha_to_one=A__     ,)
												torch.manual_seed(0)
												lowercase					=		AutoencoderKL(
												    block_out_channels=[3_2, 6_4]     ,in_channels=3     ,out_channels=3     ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,latent_channels=4     ,sample_size=1_2_8     ,)
												torch.manual_seed(0)
												lowercase					=		CLIPTextConfig(
												    bos_token_id=0     ,eos_token_id=2     ,hidden_size=3_2     ,intermediate_size=3_7     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1_0_0_0     ,hidden_act='''gelu'''     ,projection_dim=5_1_2     ,)
												lowercase					=		CLIPTextModel(A__)
												lowercase					=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

												lowercase					=		{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												}
												return components




							def 				A__   (       self     ,A__     ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase					=		torch.manual_seed(A__)
												else:
																	lowercase					=		torch.Generator(device=A__).manual_seed(A__)
												lowercase					=		{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 6.0,
												    '''output_type''': '''pt''',
												}
												return inputs




							def 				A__   (       self):
												lowercase					=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
												lowercase					=		self.get_dummy_components()
												lowercase					=		TextToVideoSDPipeline(**A__)
												lowercase					=		sd_pipe.to(A__)
												sd_pipe.set_progress_bar_config(disable=A__)

												lowercase					=		self.get_dummy_inputs(A__)
												lowercase					=		'''np'''
												lowercase					=		sd_pipe(**A__).frames
												lowercase					=		frames[0][-3:, -3:, -1]

												assert frames[0].shape == (6_4, 6_4, 3)
												lowercase					=		np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0])

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




							def 				A__   (       self):
												self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A__     ,expected_max_diff=3E-3)




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()     ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''     ,)
							def 				A__   (       self):
												self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A__     ,expected_max_diff=1E-2)




							@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''')
							def 				A__   (       self):
												pass




							@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''')
							def 				A__   (       self):
												pass




							@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''')
							def 				A__   (       self):
												pass





							def 				A__   (       self):
												return super().test_progress_bar()







@slow
@skip_mps
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												lowercase					=		load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy''')

												lowercase					=		TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''')
												lowercase					=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
												lowercase					=		pipe.to('''cuda''')

												lowercase					=		'''Spiderman is surfing'''
												lowercase					=		torch.Generator(device='''cpu''').manual_seed(0)

												lowercase					=		pipe(A__     ,generator=A__     ,num_inference_steps=2_5     ,output_type='''pt''').frames
												lowercase					=		video_frames.cpu().numpy()

												assert np.abs(expected_video - video).mean() < 5E-2





							def 				A__   (       self):
												lowercase					=		load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy''')

												lowercase					=		TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''')
												lowercase					=		pipe.to('''cuda''')

												lowercase					=		'''Spiderman is surfing'''
												lowercase					=		torch.Generator(device='''cpu''').manual_seed(0)

												lowercase					=		pipe(A__     ,generator=A__     ,num_inference_steps=2     ,output_type='''pt''').frames
												lowercase					=		video_frames.cpu().numpy()

												assert np.abs(expected_video - video).mean() < 5E-2






class        lowercase	:




							def __init__(       self     ,A__):
												lowercase					=		val
												lowercase					=		None
												lowercase					=		None





							def 				A__   (       self     ,A__):
												if self.val:
																	if val < self.val:
																						if self.left is None:
																											lowercase					=		Node(A__)
																						else:
																											self.left.insert(A__)
																	elif val > self.val:
																						if self.right is None:
																											lowercase					=		Node(A__)
																						else:
																											self.right.insert(A__)
												else:
																	lowercase					=		val



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# Recursive traversal
					if root:
										inorder(root.left				,    lowerCAmelCase__	)
										res.append(root.val	)
										inorder(root.right				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					# Build BST
					if len(lowerCAmelCase__	) == 0:
										return arr
					lowercase					=		Node(arr[0]	)
					for i in range(1				,    len(lowerCAmelCase__	)	):
										root.insert(arr[i]	)
					# Traverse BST in order.
					lowercase					=		[]
					inorder(lowerCAmelCase__				,    lowerCAmelCase__	)
					return res


if __name__ == "__main__":
							print(tree_sort([10, 1, 3, 2, 9, 14, 13]))




