


'''simple docstring'''
import mpmath  # for roots of unity
import numpy as np
class        lowercase     :

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			list(poly_a or [0]							)[:]
							UpperCamelCase__       :str							 =			list(poly_b or [0]							)[:]

							# Remove leading zero coefficients
							while self.polyA[-1] == 0:
												self.polyA.pop()
							UpperCamelCase__       :Optional[int]							 =			len(self.polyA							)

							while self.polyB[-1] == 0:
												self.polyB.pop()
							UpperCamelCase__       :Dict							 =			len(self.polyB							)

							# Add 0 to make lengths equal a power of 2
							UpperCamelCase__       :Tuple							 =			int(
							    2 ** np.ceil(np.loga(len(self.polyA							) + len(self.polyB							) - 1							)							)							)

							while len(self.polyA							) < self.c_max_length:
												self.polyA.append(0							)
							while len(self.polyB							) < self.c_max_length:
												self.polyB.append(0							)
							# A complex root used for the fourier transform
							UpperCamelCase__       :str							 =			complex(mpmath.root(x=1   ,       n=self.c_max_length   ,       k=1							)							)

							# The product
							UpperCamelCase__       :Optional[Any]							 =			self.__multiply()




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
							# Corner case
							if len(_SCREAMING_SNAKE_CASE							) <= 1:
												return dft[0]
							#
							UpperCamelCase__       :Optional[Any]							 =			self.c_max_length // 2
							while next_ncol > 0:
												UpperCamelCase__       :Dict							 =			[[] for i in range(_SCREAMING_SNAKE_CASE							)]
												UpperCamelCase__       :int							 =			self.root**next_ncol

												# First half of next step
												UpperCamelCase__       :List[Any]							 =			1
												for j in range(self.c_max_length // (next_ncol * 2)							):
																	for i in range(_SCREAMING_SNAKE_CASE							):
																						new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]							)
																	current_root *= root
												# Second half of next step
												UpperCamelCase__       :List[Any]							 =			1
												for j in range(self.c_max_length // (next_ncol * 2)							):
																	for i in range(_SCREAMING_SNAKE_CASE							):
																						new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]							)
																	current_root *= root
												# Update
												UpperCamelCase__       :Any							 =			new_dft
												UpperCamelCase__       :List[str]							 =			next_ncol // 2
							return dft[0]




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			self.__dft('''A'''							)
							UpperCamelCase__       :List[str]							 =			self.__dft('''B'''							)
							UpperCamelCase__       :List[str]							 =			[[dft_a[i] * dft_b[i] for i in range(self.c_max_length							)]]
							del dft_a
							del dft_b

							# Corner Case
							if len(inverce_c[0]							) <= 1:
												return inverce_c[0]
							# Inverse DFT
							UpperCamelCase__       :List[Any]							 =			2
							while next_ncol <= self.c_max_length:
												UpperCamelCase__       :str							 =			[[] for i in range(_SCREAMING_SNAKE_CASE							)]
												UpperCamelCase__       :Tuple							 =			self.root ** (next_ncol // 2)
												UpperCamelCase__       :Dict							 =			1
												# First half of next step
												for j in range(self.c_max_length // next_ncol							):
																	for i in range(next_ncol // 2							):
																						# Even positions
																						new_inverse_c[i].append(
																						    (
																						        inverce_c[i][j]
																						        + inverce_c[i][j + self.c_max_length // next_ncol]
																						    )
																						    / 2							)
																						# Odd positions
																						new_inverse_c[i + next_ncol // 2].append(
																						    (
																						        inverce_c[i][j]
																						        - inverce_c[i][j + self.c_max_length // next_ncol]
																						    )
																						    / (2 * current_root)							)
																	current_root *= root
												# Update
												UpperCamelCase__       :Optional[int]							 =			new_inverse_c
												next_ncol *= 2
							# Unpack
							UpperCamelCase__       :Optional[Any]							 =			[round(x[0].real   ,       8							) + round(x[0].imag   ,       8							) * 1j for x in inverce_c]

							# Remove leading 0's
							while inverce_c[-1] == 0:
												inverce_c.pop()
							return inverce_c




		def __str__(		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			'''A = ''' + ''' + '''.join(
							    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]							)							)
							UpperCamelCase__       :List[Any]							 =			'''B = ''' + ''' + '''.join(
							    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]							)							)
							UpperCamelCase__       :Optional[Any]							 =			'''A*B = ''' + ''' + '''.join(
							    F'''{coef}*x^{i}''' for coef, i in enumerate(self.product							)							)

							return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''simple docstring'''
import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class        lowercase     (							A__     ,		A__     ,		A__     ,		unittest.TestCase ):

		"""simple docstring"""

		_a   			=    AltDiffusionPipeline
		_a   			=    TEXT_TO_IMAGE_PARAMS
		_a   			=    TEXT_TO_IMAGE_BATCH_PARAMS
		_a   			=    TEXT_TO_IMAGE_IMAGE_PARAMS
		_a   			=    TEXT_TO_IMAGE_IMAGE_PARAMS




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			UNetaDConditionModel(
							    block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,       cross_attention_dim=32   ,       )
							UpperCamelCase__       :int							 =			DDIMScheduler(
							    beta_start=0.00085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=UpperCamelCase_   ,       set_alpha_to_one=UpperCamelCase_   ,       )
							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			AutoencoderKL(
							    block_out_channels=[32, 64]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,       latent_channels=4   ,       )

							# TODO: address the non-deterministic text encoder (fails for save-load tests)
							# torch.manual_seed(0)
							# text_encoder_config = RobertaSeriesConfig(
							#     hidden_size=32,
							#     project_dim=32,
							#     intermediate_size=37,
							#     layer_norm_eps=1e-05,
							#     num_attention_heads=4,
							#     num_hidden_layers=5,
							#     vocab_size=5002,
							# )
							# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

							torch.manual_seed(0							)
							UpperCamelCase__       :Union[str, Any]							 =			CLIPTextConfig(
							    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       projection_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=5002   ,       )
							UpperCamelCase__       :Optional[Any]							 =			CLIPTextModel(UpperCamelCase_							)

							UpperCamelCase__       :int							 =			XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
							UpperCamelCase__       :Dict							 =			77

							UpperCamelCase__       :Dict							 =			{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_=0							):

							'''simple docstring'''


							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__       :Dict							 =			torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__       :List[str]							 =			torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__       :Tuple							 =			self.get_dummy_components()
							torch.manual_seed(0							)
							UpperCamelCase__       :Dict							 =			RobertaSeriesConfig(
							    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=5002   ,       )
							# TODO: remove after fixing the non-deterministic text encoder
							UpperCamelCase__       :Union[str, Any]							 =			RobertaSeriesModelWithTransformation(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			text_encoder

							UpperCamelCase__       :Any							 =			AltDiffusionPipeline(**UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :int							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			'''A photo of an astronaut'''
							UpperCamelCase__       :Union[str, Any]							 =			alt_pipe(**UpperCamelCase_							)
							UpperCamelCase__       :Any							 =			output.images
							UpperCamelCase__       :List[Any]							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__       :List[Any]							 =			np.array(
							    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]							)

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__       :int							 =			self.get_dummy_components()
							UpperCamelCase__       :Union[str, Any]							 =			PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							torch.manual_seed(0							)
							UpperCamelCase__       :Dict							 =			RobertaSeriesConfig(
							    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=5002   ,       )
							# TODO: remove after fixing the non-deterministic text encoder
							UpperCamelCase__       :str							 =			RobertaSeriesModelWithTransformation(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			text_encoder
							UpperCamelCase__       :Union[str, Any]							 =			AltDiffusionPipeline(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			alt_pipe(**UpperCamelCase_							)
							UpperCamelCase__       :str							 =			output.images
							UpperCamelCase__       :int							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase__       :List[Any]							 =			np.array(
							    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]							)

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



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

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       safety_checker=UpperCamelCase_							)
							UpperCamelCase__       :str							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :int							 =			'''A painting of a squirrel eating a burger'''
							UpperCamelCase__       :int							 =			torch.manual_seed(0							)
							UpperCamelCase__       :Optional[Any]							 =			alt_pipe([prompt]   ,       generator=UpperCamelCase_   ,       guidance_scale=6.0   ,       num_inference_steps=20   ,       output_type='''np'''							)

							UpperCamelCase__       :Union[str, Any]							 =			output.images

							UpperCamelCase__       :Optional[Any]							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__       :Union[str, Any]							 =			np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]							)

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,       subfolder='''scheduler'''							)

							UpperCamelCase__       :List[str]							 =			AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       scheduler=UpperCamelCase_   ,       safety_checker=UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			alt_pipe.to(UpperCamelCase_							)
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :List[Any]							 =			'''A painting of a squirrel eating a burger'''
							UpperCamelCase__       :str							 =			torch.manual_seed(0							)

							UpperCamelCase__       :Optional[int]							 =			alt_pipe([prompt]   ,       generator=UpperCamelCase_   ,       num_inference_steps=2   ,       output_type='''numpy'''							)
							UpperCamelCase__       :Any							 =			output.images

							UpperCamelCase__       :int							 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__       :int							 =			np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]							)

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