





'''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						(							UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					unittest.TestCase				):
   _UpperCamelCase       		=   AltDiffusionPipeline
   _UpperCamelCase       		=   TEXT_TO_IMAGE_PARAMS
   _UpperCamelCase       		=   TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCamelCase       		=   TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase       		=   TEXT_TO_IMAGE_IMAGE_PARAMS


   def 					snake_case				( self					):
       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=_UpperCAmelCase    ,   set_alpha_to_one=_UpperCAmelCase    ,   )
       torch.manual_seed(0					)
       A       :       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					)
       A       :       int							=						CLIPTextConfig(
           bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   projection_dim=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=5_002    ,   )
       A       :       List[Any]							=						CLIPTextModel(_UpperCAmelCase					)

       A       :       Optional[Any]							=						XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''					)
       A       :       Optional[int]							=						77

       A       :       List[str]							=						{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=0					):
       if str(_UpperCAmelCase					).startswith('''mps'''					):
           A       :       Dict							=						torch.manual_seed(_UpperCAmelCase					)
       else:
           A       :       Any							=						torch.Generator(device=_UpperCAmelCase					).manual_seed(_UpperCAmelCase					)
       A       :       Optional[Any]							=						{
           '''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 					snake_case				( self					):
       super().test_attention_slicing_forward_pass(expected_max_diff=3E-3					)


   def 					snake_case				( self					):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3					)


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       A       :       Dict							=						self.get_dummy_components()
       torch.manual_seed(0					)
       A       :       List[str]							=						RobertaSeriesConfig(
           hidden_size=32    ,   project_dim=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   vocab_size=5_002    ,   )
       # TODO: remove after fixing the non-deterministic text encoder
       A       :       List[Any]							=						RobertaSeriesModelWithTransformation(_UpperCAmelCase					)
       A       :       List[Any]							=						text_encoder

       A       :       Union[str, Any]							=						AltDiffusionPipeline(**_UpperCAmelCase					)
       A       :       List[Any]							=						alt_pipe.to(_UpperCAmelCase					)
       alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

       assert image.shape == (1, 64, 64, 3)
       A       :       int							=						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 					snake_case				( self					):
       A       :       Union[str, Any]							=						'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       A       :       Any							=						self.get_dummy_components()
       A       :       List[str]							=						PNDMScheduler(skip_prk_steps=_UpperCAmelCase					)
       torch.manual_seed(0					)
       A       :       str							=						RobertaSeriesConfig(
           hidden_size=32    ,   project_dim=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   vocab_size=5_002    ,   )
       # TODO: remove after fixing the non-deterministic text encoder
       A       :       str							=						RobertaSeriesModelWithTransformation(_UpperCAmelCase					)
       A       :       List[str]							=						text_encoder
       A       :       Optional[int]							=						AltDiffusionPipeline(**_UpperCAmelCase					)
       A       :       Tuple							=						alt_pipe.to(_UpperCAmelCase					)
       alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

       assert image.shape == (1, 64, 64, 3)
       A       :       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   _lowercase						(							unittest.TestCase				):


   def 					snake_case				( self					):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


   def 					snake_case				( self					):
       # make sure here that pndm scheduler skips prk
       A       :       Any							=						AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''    ,   safety_checker=_UpperCAmelCase					)
       A       :       Optional[Any]							=						alt_pipe.to(_UpperCAmelCase					)
       alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

       A       :       Optional[int]							=						output.images

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

       assert image.shape == (1, 512, 512, 3)
       A       :       Optional[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 					snake_case				( self					):
       A       :       Optional[Any]							=						DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''    ,   subfolder='''scheduler'''					)

       A       :       List[Any]							=						AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''    ,   scheduler=_UpperCAmelCase    ,   safety_checker=_UpperCAmelCase					)
       A       :       str							=						alt_pipe.to(_UpperCAmelCase					)
       alt_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

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

       A       :       Dict							=						alt_pipe([prompt]    ,   generator=_UpperCAmelCase    ,   num_inference_steps=2    ,   output_type='''numpy'''					)
       A       :       Dict							=						output.images

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

       assert image.shape == (1, 512, 512, 3)
       A       :       Union[str, 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







'''simple docstring'''






from math import pi
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : int		)		->  float:
    return 2 * pi * radius * (angle / 360)


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