





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        __SCREAMING_SNAKE_CASE					(     _a   ,		_a   ,		_a   ,		unittest.TestCase       ):
							snake_case     :      Union[str, Any]						     =       AltDiffusionPipeline
							snake_case     :      Tuple						     =       TEXT_TO_IMAGE_PARAMS
							snake_case     :      Dict						     =       TEXT_TO_IMAGE_BATCH_PARAMS
							snake_case     :      List[str]						     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							snake_case     :      Optional[Any]						     =       TEXT_TO_IMAGE_IMAGE_PARAMS






							def 				_lowerCamelCase		(						self  ):
														torch.manual_seed(0  )
														UpperCamelCase__				     =			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__				     =			DDIMScheduler(
														    beta_start=0.0_0085  , beta_end=0.012  , beta_schedule="""scaled_linear"""  , clip_sample=__lowerCAmelCase  , set_alpha_to_one=__lowerCAmelCase  , )
														torch.manual_seed(0  )
														UpperCamelCase__				     =			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__				     =			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__				     =			CLIPTextModel(__lowerCAmelCase  )

														UpperCamelCase__				     =			XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""  )
														UpperCamelCase__				     =			77

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






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase=0  ):
														if str(__lowerCAmelCase  ).startswith("""mps"""  ):
																					UpperCamelCase__				     =			torch.manual_seed(__lowerCAmelCase  )
														else:
																					UpperCamelCase__				     =			torch.Generator(device=__lowerCAmelCase  ).manual_seed(__lowerCAmelCase  )
														UpperCamelCase__				     =			{
														    """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  ):
														super().test_attention_slicing_forward_pass(expected_max_diff=3E-3  )






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






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

														UpperCamelCase__				     =			self.get_dummy_components()
														torch.manual_seed(0  )
														UpperCamelCase__				     =			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__				     =			RobertaSeriesModelWithTransformation(__lowerCAmelCase  )
														UpperCamelCase__				     =			text_encoder

														UpperCamelCase__				     =			AltDiffusionPipeline(**__lowerCAmelCase  )
														UpperCamelCase__				     =			alt_pipe.to(__lowerCAmelCase  )
														alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

														UpperCamelCase__				     =			self.get_dummy_inputs(__lowerCAmelCase  )
														UpperCamelCase__				     =			"""A photo of an astronaut"""
														UpperCamelCase__				     =			alt_pipe(**__lowerCAmelCase  )
														UpperCamelCase__				     =			output.images
														UpperCamelCase__				     =			image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														UpperCamelCase__				     =			np.array(
														    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]  )

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







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

														UpperCamelCase__				     =			self.get_dummy_components()
														UpperCamelCase__				     =			PNDMScheduler(skip_prk_steps=__lowerCAmelCase  )
														torch.manual_seed(0  )
														UpperCamelCase__				     =			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__				     =			RobertaSeriesModelWithTransformation(__lowerCAmelCase  )
														UpperCamelCase__				     =			text_encoder
														UpperCamelCase__				     =			AltDiffusionPipeline(**__lowerCAmelCase  )
														UpperCamelCase__				     =			alt_pipe.to(__lowerCAmelCase  )
														alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

														UpperCamelCase__				     =			self.get_dummy_inputs(__lowerCAmelCase  )
														UpperCamelCase__				     =			alt_pipe(**__lowerCAmelCase  )
														UpperCamelCase__				     =			output.images
														UpperCamelCase__				     =			image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														UpperCamelCase__				     =			np.array(
														    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]  )

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






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






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






							def 				_lowerCamelCase		(						self  ):
														# make sure here that pndm scheduler skips prk
														UpperCamelCase__				     =			AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""  , safety_checker=__lowerCAmelCase  )
														UpperCamelCase__				     =			alt_pipe.to(__lowerCAmelCase  )
														alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

														UpperCamelCase__				     =			"""A painting of a squirrel eating a burger"""
														UpperCamelCase__				     =			torch.manual_seed(0  )
														UpperCamelCase__				     =			alt_pipe([prompt]  , generator=__lowerCAmelCase  , guidance_scale=6.0  , num_inference_steps=20  , output_type="""np"""  )

														UpperCamelCase__				     =			output.images

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

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__				     =			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  ):
														UpperCamelCase__				     =			DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""  , subfolder="""scheduler"""  )

														UpperCamelCase__				     =			AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""  , scheduler=__lowerCAmelCase  , safety_checker=__lowerCAmelCase  )
														UpperCamelCase__				     =			alt_pipe.to(__lowerCAmelCase  )
														alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

														UpperCamelCase__				     =			"""A painting of a squirrel eating a burger"""
														UpperCamelCase__				     =			torch.manual_seed(0  )

														UpperCamelCase__				     =			alt_pipe([prompt]  , generator=__lowerCAmelCase  , num_inference_steps=2  , output_type="""numpy"""  )
														UpperCamelCase__				     =			output.images

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

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__				     =			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 ceil, sqrt





def     _UpperCamelCase     (a__   :int = 100_0000       ):






							"""simple docstring"""





							UpperCamelCase__				     =			0

							for outer_width in range(3   ,			(limit // 4) + 2       ):
														if outer_width**2 > limit:
																					UpperCamelCase__				     =			max(ceil(sqrt(outer_width**2 - limit       )       )   ,			1       )
														else:
																					UpperCamelCase__				     =			1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


if __name__ == "__main__":
					print(f"""{solution() = }""")




