






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   (       __a					,				__a					,				__a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	str     =					AltDiffusionPipeline
					_A  :	Any     =					TEXT_TO_IMAGE_PARAMS
					_A  :	Optional[Any]     =					TEXT_TO_IMAGE_BATCH_PARAMS
					_A  :	Any     =					TEXT_TO_IMAGE_IMAGE_PARAMS
					_A  :	str     =					TEXT_TO_IMAGE_IMAGE_PARAMS



					def 					lowerCAmelCase (     self :		Any							)	->     List[Any]:


												"""simple docstring"""

												torch.manual_seed(0							)
												__lowercase					:							Union[str, 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						,   )
												__lowercase					:							str			=   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					:							Union[str, Any]			=   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							)
												__lowercase					:							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						,   )
												__lowercase					:							int			=   CLIPTextModel(__a							)

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

												__lowercase					:							Optional[int]			=   {
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """vae""": vae,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												    """safety_checker""": None,
												    """feature_extractor""": None,
												}
												return components



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		Any						,   __a :		int=0							)	->     List[str]:


												"""simple docstring"""

												if str(__a							).startswith("""mps"""							):
																			__lowercase					:							List[str]			=   torch.manual_seed(__a							)
												else:
																			__lowercase					:							Union[str, Any]			=   torch.Generator(device=__a							).manual_seed(__a							)
												__lowercase					:							List[str]			=   {
												    """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 :		Tuple							)	->     Dict:


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		Dict							)	->     Any:


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		Dict							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Dict			=   """cpu"""  # ensure determinism for the device-dependent torch.Generator

												__lowercase					:							int			=   self.get_dummy_components()
												torch.manual_seed(0							)
												__lowercase					:							Optional[int]			=   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
												__lowercase					:							int			=   RobertaSeriesModelWithTransformation(__a							)
												__lowercase					:							List[Any]			=   text_encoder

												__lowercase					:							Tuple			=   AltDiffusionPipeline(**__a							)
												__lowercase					:							Any			=   alt_pipe.to(__a							)
												alt_pipe.set_progress_bar_config(disable=__a							)

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

												assert image.shape == (1, 64, 64, 3)
												__lowercase					:							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 :		List[str]							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   """cpu"""  # ensure determinism for the device-dependent torch.Generator

												__lowercase					:							List[Any]			=   self.get_dummy_components()
												__lowercase					:							Optional[int]			=   PNDMScheduler(skip_prk_steps=__a							)
												torch.manual_seed(0							)
												__lowercase					:							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=5002						,   )
												# TODO: remove after fixing the non-deterministic text encoder
												__lowercase					:							Dict			=   RobertaSeriesModelWithTransformation(__a							)
												__lowercase					:							str			=   text_encoder
												__lowercase					:							Dict			=   AltDiffusionPipeline(**__a							)
												__lowercase					:							Any			=   alt_pipe.to(__a							)
												alt_pipe.set_progress_bar_config(disable=__a							)

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

												assert image.shape == (1, 64, 64, 3)
												__lowercase					:							List[str]			=   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 					lowerCAmelCase (     self :		str							)	->     Optional[Any]:


												"""simple docstring"""

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



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Any			=   AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""						,   safety_checker=__a							)
												__lowercase					:							Optional[int]			=   alt_pipe.to(__a							)
												alt_pipe.set_progress_bar_config(disable=__a							)

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

												__lowercase					:							Tuple			=   output.images

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

												assert image.shape == (1, 512, 512, 3)
												__lowercase					:							int			=   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 :		List[str]							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""						,   subfolder="""scheduler"""							)

												__lowercase					:							List[Any]			=   AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""						,   scheduler=__a						,   safety_checker=__a							)
												__lowercase					:							Dict			=   alt_pipe.to(__a							)
												alt_pipe.set_progress_bar_config(disable=__a							)

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

												__lowercase					:							Any			=   alt_pipe([prompt]						,   generator=__a						,   num_inference_steps=2						,   output_type="""numpy"""							)
												__lowercase					:							Optional[Any]			=   output.images

												__lowercase					:							Tuple			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 512, 512, 3)
												__lowercase					:							Any			=   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







from math import pi
def 					snake_case_						(   lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  int					):
							return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
	print(arc_length(90, 10))