





"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : UNetaDModel
							snake_case       : KarrasVeScheduler






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														super().__init__()
														self.register_modules(unet=__lowerCAmelCase					,	scheduler=__lowerCAmelCase					)







							@torch.no_grad()
							def __call__(  self					,	__lowerCAmelCase = 1					,	__lowerCAmelCase = 50					,	__lowerCAmelCase = None					,	__lowerCAmelCase = "pil"					,	__lowerCAmelCase = True					,	**__lowerCAmelCase					,	):
														UpperCamelCase__			       =     self.unet.config.sample_size
														UpperCamelCase__			       =     (batch_size, 3, img_size, img_size)

														UpperCamelCase__			       =     self.unet

														# sample x_0 ~ N(0, sigma_0^2 * I)
														UpperCamelCase__			       =     randn_tensor(__lowerCAmelCase					,	generator=__lowerCAmelCase					,	device=self.device					) * self.scheduler.init_noise_sigma

														self.scheduler.set_timesteps(__lowerCAmelCase					)

														for t in self.progress_bar(self.scheduler.timesteps					):
																					# here sigma_t == t_i from the paper
																					UpperCamelCase__			       =     self.scheduler.schedule[t]
																					UpperCamelCase__			       =     self.scheduler.schedule[t - 1] if t > 0 else 0

																					# 1. Select temporarily increased noise level sigma_hat
																					# 2. Add new noise to move from sample_i to sample_hat
																					UpperCamelCase__       ,       UpperCamelCase__			       =     self.scheduler.add_noise_to_input(__lowerCAmelCase					,	__lowerCAmelCase					,	generator=__lowerCAmelCase					)

																					# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																					# The model inputs and output are adjusted by following eq. (213) in [1].
																					UpperCamelCase__			       =     (sigma_hat / 2) * model((sample_hat + 1) / 2					,	sigma_hat / 2					).sample

																					# 4. Evaluate dx/dt at sigma_hat
																					# 5. Take Euler step from sigma to sigma_prev
																					UpperCamelCase__			       =     self.scheduler.step(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)

																					if sigma_prev != 0:
																												# 6. Apply 2nd order correction
																												# The model inputs and output are adjusted by following eq. (213) in [1].
																												UpperCamelCase__			       =     (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2					,	sigma_prev / 2					).sample
																												UpperCamelCase__			       =     self.scheduler.step_correct(
																												    __lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	step_output.prev_sample					,	step_output["""derivative"""]					,	)
																					UpperCamelCase__			       =     step_output.prev_sample

														UpperCamelCase__			       =     (sample / 2 + 0.5).clamp(0					,	1					)
														UpperCamelCase__			       =     sample.cpu().permute(0					,	2					,	3					,	1					).numpy()
														if output_type == "pil":
																					UpperCamelCase__			       =     self.numpy_to_pil(__lowerCAmelCase					)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=__lowerCAmelCase					)











import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
					from transformers import TvltImageProcessor

if is_speech_available():
					from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """ZinengTang/tvlt-base"""
														UpperCamelCase__			       =     tempfile.mkdtemp()






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														return TvltImageProcessor.from_pretrained(self.checkpoint					,	**__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														return TvltFeatureExtractor.from_pretrained(self.checkpoint					,	**__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														shutil.rmtree(self.tmpdirname					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)
														processor.save_pretrained(self.tmpdirname					)
														UpperCamelCase__			       =     TvltProcessor.from_pretrained(self.tmpdirname					)

														self.assertIsInstance(processor.feature_extractor					,	__lowerCAmelCase					)
														self.assertIsInstance(processor.image_processor					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([12000]					)

														UpperCamelCase__			       =     feature_extractor(__lowerCAmelCase					,	return_tensors="""np"""					)
														UpperCamelCase__			       =     processor(audio=__lowerCAmelCase					,	return_tensors="""np"""					)

														for key in audio_dict.keys():
																					self.assertAlmostEqual(audio_dict[key].sum()					,	input_processor[key].sum()					,	delta=1E-2					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([3, 224, 224]					)

														UpperCamelCase__			       =     image_processor(__lowerCAmelCase					,	return_tensors="""np"""					)
														UpperCamelCase__			       =     processor(images=__lowerCAmelCase					,	return_tensors="""np"""					)

														for key in image_dict.keys():
																					self.assertAlmostEqual(image_dict[key].sum()					,	input_processor[key].sum()					,	delta=1E-2					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([12000]					)
														UpperCamelCase__			       =     np.ones([3, 224, 224]					)

														UpperCamelCase__			       =     processor(audio=__lowerCAmelCase					,	images=__lowerCAmelCase					)

														self.assertListEqual(list(inputs.keys()					)					,	["""audio_values""", """audio_mask""", """pixel_values""", """pixel_mask"""]					)

														# test if it raises when no input is passed
														with pytest.raises(__lowerCAmelCase					):
																					processor()







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														self.assertListEqual(
														    processor.model_input_names					,	image_processor.model_input_names + feature_extractor.model_input_names					,	msg="""`processor` and `image_processor`+`feature_extractor` model input names do not match"""					,	)




