

'''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 lowerCamelCase			(  lowercase_	,						lowercase_	,						lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	AltDiffusionPipeline
			__snake_case        =	TEXT_TO_IMAGE_PARAMS
			__snake_case        =	TEXT_TO_IMAGE_BATCH_PARAMS
			__snake_case        =	TEXT_TO_IMAGE_IMAGE_PARAMS
			__snake_case        =	TEXT_TO_IMAGE_IMAGE_PARAMS


			def lowercase__						(				self	:						Union[str, Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									torch.manual_seed(0			)
									A__					:       List[Any]     =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		,      )
									A__					:       Union[str, Any]     =DDIMScheduler(
									    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="""scaled_linear"""		,      clip_sample=lowerCAmelCase_		,      set_alpha_to_one=lowerCAmelCase_		,      )
									torch.manual_seed(0			)
									A__					:       Dict     =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			)
									A__					:       Optional[int]     =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=50_02		,      )
									A__					:       Tuple     =CLIPTextModel(lowerCAmelCase_			)

									A__					:       Dict     =XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""			)
									A__					:       List[str]     =77

									A__					:       Tuple     ={
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components


			def lowercase__						(				self	:						List[Any]		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Optional[int]=0			)      ->		Optional[int]:





									'''simple docstring'''
									if str(lowerCAmelCase_			).startswith("""mps"""			):
															A__					:       Optional[Any]     =torch.manual_seed(lowerCAmelCase_			)
									else:
															A__					:       Any     =torch.Generator(device=lowerCAmelCase_			).manual_seed(lowerCAmelCase_			)
									A__					:       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 lowercase__						(				self	:						str			)      ->		str:





									'''simple docstring'''
									super().test_attention_slicing_forward_pass(expected_max_diff=3e-3			)


			def lowercase__						(				self	:						List[Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=3e-3			)


			def lowercase__						(				self	:						int			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       str     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator

									A__					:       Union[str, Any]     =self.get_dummy_components()
									torch.manual_seed(0			)
									A__					:       Any     =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=50_02		,      )
									# TODO: remove after fixing the non-deterministic text encoder
									A__					:       int     =RobertaSeriesModelWithTransformation(lowerCAmelCase_			)
									A__					:       int     =text_encoder

									A__					:       Dict     =AltDiffusionPipeline(**lowerCAmelCase_			)
									A__					:       Dict     =alt_pipe.to(lowerCAmelCase_			)
									alt_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       List[str]     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       Dict     ="""A photo of an astronaut"""
									A__					:       Dict     =alt_pipe(**lowerCAmelCase_			)
									A__					:       List[Any]     =output.images
									A__					:       List[Any]     =image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									A__					:       Union[str, 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 lowercase__						(				self	:						Tuple			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       int     ="""cpu"""  # ensure determinism for the device-dependent torch.Generator

									A__					:       Any     =self.get_dummy_components()
									A__					:       str     =PNDMScheduler(skip_prk_steps=lowerCAmelCase_			)
									torch.manual_seed(0			)
									A__					:       Any     =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=50_02		,      )
									# TODO: remove after fixing the non-deterministic text encoder
									A__					:       List[Any]     =RobertaSeriesModelWithTransformation(lowerCAmelCase_			)
									A__					:       List[Any]     =text_encoder
									A__					:       int     =AltDiffusionPipeline(**lowerCAmelCase_			)
									A__					:       Union[str, Any]     =alt_pipe.to(lowerCAmelCase_			)
									alt_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Dict     =self.get_dummy_inputs(lowerCAmelCase_			)
									A__					:       List[Any]     =alt_pipe(**lowerCAmelCase_			)
									A__					:       Dict     =output.images
									A__					:       str     =image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									A__					:       Optional[int]     =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 lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Union[str, Any]			)      ->		Any:





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


			def lowercase__						(				self	:						Optional[int]			)      ->		int:





									'''simple docstring'''
									A__					:       List[str]     =AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""		,      safety_checker=lowerCAmelCase_			)
									A__					:       List[str]     =alt_pipe.to(lowerCAmelCase_			)
									alt_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Union[str, Any]     ="""A painting of a squirrel eating a burger"""
									A__					:       Optional[int]     =torch.manual_seed(0			)
									A__					:       Optional[Any]     =alt_pipe([prompt]		,      generator=lowerCAmelCase_		,      guidance_scale=6.0		,      num_inference_steps=20		,      output_type="""np"""			)

									A__					:       Tuple     =output.images

									A__					:       Union[str, Any]     =image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       Tuple     =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 lowercase__						(				self	:						Tuple			)      ->		Dict:





									'''simple docstring'''
									A__					:       Any     =DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""		,      subfolder="""scheduler"""			)

									A__					:       List[str]     =AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""		,      scheduler=lowerCAmelCase_		,      safety_checker=lowerCAmelCase_			)
									A__					:       int     =alt_pipe.to(lowerCAmelCase_			)
									alt_pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Optional[int]     ="""A painting of a squirrel eating a burger"""
									A__					:       List[Any]     =torch.manual_seed(0			)

									A__					:       List[Any]     =alt_pipe([prompt]		,      generator=lowerCAmelCase_		,      num_inference_steps=2		,      output_type="""numpy"""			)
									A__					:       List[Any]     =output.images

									A__					:       Optional[int]     =image[0, -3:, -3:, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									A__					:       List[str]     =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




'''simple docstring'''



from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						float			)      ->		float:





									'''simple docstring'''
									return 0.0


def 						__lowerCamelCase	(       __snake_case					:		np.ndarray,		__snake_case					:		int	)	->							tuple[int | float, int | float]:






						"""simple docstring"""





						A__					:       Tuple     =min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
						A__					:       str     =max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
						return lowest, highest
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       Any     =512
						A__					:       int     =[1] + [0] * (size - 1)
						A__					:       int     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       List[Any]     =np.abs(np.fft.fft(__snake_case	)	)
						A__					:       int     =20 * np.logaa(__snake_case	)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24,		samplerate / 2 - 1	)
						plt.xlabel("""Frequency (Hz)"""	)
						plt.xscale("""log"""	)

						# Display within reasonable bounds
						A__					:       Union[str, Any]     =get_bounds(__snake_case,		__snake_case	)
						plt.ylim(max([-80, bounds[0]]	),		min([80, bounds[1]]	)	)
						plt.ylabel("""Gain (dB)"""	)

						plt.plot(__snake_case	)
						plt.show()
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       List[Any]     =512
						A__					:       List[Any]     =[1] + [0] * (size - 1)
						A__					:       Dict     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       str     =np.angle(np.fft.fft(__snake_case	)	)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24,		samplerate / 2 - 1	)
						plt.xlabel("""Frequency (Hz)"""	)
						plt.xscale("""log"""	)

						plt.ylim(-2 * pi,		2 * pi	)
						plt.ylabel("""Phase shift (Radians)"""	)
						plt.plot(np.unwrap(__snake_case,		-2 * pi	)	)
						plt.show()

