
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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()


@skip_mps
class 	UpperCamelCase__       (  snake_case__						,			snake_case__						,			snake_case__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Any												=			StableDiffusionModelEditingPipeline
						__a     :    str												=			TEXT_TO_IMAGE_PARAMS
						__a     :    int												=			TEXT_TO_IMAGE_BATCH_PARAMS
						__a     :    Optional[int]												=			TEXT_TO_IMAGE_IMAGE_PARAMS
						__a     :    int												=			TEXT_TO_IMAGE_IMAGE_PARAMS


						def    A__       (  self	)  ->str:
									torch.manual_seed(0	)
									UpperCAmelCase__		:Dict 		=    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__		:Tuple 		=    DDIMScheduler()
									torch.manual_seed(0	)
									UpperCAmelCase__		: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  , )
									torch.manual_seed(0	)
									UpperCAmelCase__		:Tuple 		=    CLIPTextConfig(
									    bos_token_id=0  , eos_token_id=2  , hidden_size=32  , intermediate_size=37  , layer_norm_eps=1e-05  , num_attention_heads=4  , num_hidden_layers=5  , pad_token_id=1  , vocab_size=10_00  , )
									UpperCAmelCase__		:Any 		=    CLIPTextModel(__lowerCAmelCase	)
									UpperCAmelCase__		:str 		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

									UpperCAmelCase__		:Union[str, Any] 		=    {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components


						def    A__       (  self  , A  , A=0	)  ->Optional[int]:
									UpperCAmelCase__		:Optional[Any] 		=    torch.manual_seed(__lowerCAmelCase	)
									UpperCAmelCase__		:str 		=    {
									    """prompt""": """A field of roses""",
									    """generator""": generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    """height""": None,
									    """width""": None,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Tuple 		=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCAmelCase__		:List[str] 		=    self.get_dummy_components()
									UpperCAmelCase__		:List[str] 		=    StableDiffusionModelEditingPipeline(**__lowerCAmelCase	)
									UpperCAmelCase__		:Any 		=    sd_pipe.to(__lowerCAmelCase	)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

									UpperCAmelCase__		:Dict 		=    self.get_dummy_inputs(__lowerCAmelCase	)
									UpperCAmelCase__		:List[Any] 		=    sd_pipe(**__lowerCAmelCase	).images
									UpperCAmelCase__		:Optional[Any] 		=    image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									UpperCAmelCase__		:int 		=    np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]	)

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


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:Tuple 		=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCAmelCase__		:Dict 		=    self.get_dummy_components()
									UpperCAmelCase__		:int 		=    StableDiffusionModelEditingPipeline(**__lowerCAmelCase	)
									UpperCAmelCase__		:Optional[Any] 		=    sd_pipe.to(__lowerCAmelCase	)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

									UpperCAmelCase__		:Optional[Any] 		=    self.get_dummy_inputs(__lowerCAmelCase	)
									UpperCAmelCase__		:List[str] 		=    """french fries"""
									UpperCAmelCase__		:List[str] 		=    sd_pipe(**__lowerCAmelCase  , negative_prompt=__lowerCAmelCase	)
									UpperCAmelCase__		:List[Any] 		=    output.images
									UpperCAmelCase__		:Any 		=    image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									UpperCAmelCase__		:Dict 		=    np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]	)

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


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		:Optional[Any] 		=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCAmelCase__		:Tuple 		=    self.get_dummy_components()
									UpperCAmelCase__		:int 		=    EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0085  , beta_end=0.012  , beta_schedule='scaled_linear'	)
									UpperCAmelCase__		:List[Any] 		=    StableDiffusionModelEditingPipeline(**__lowerCAmelCase	)
									UpperCAmelCase__		:List[str] 		=    sd_pipe.to(__lowerCAmelCase	)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

									UpperCAmelCase__		:List[str] 		=    self.get_dummy_inputs(__lowerCAmelCase	)
									UpperCAmelCase__		:str 		=    sd_pipe(**__lowerCAmelCase	).images
									UpperCAmelCase__		:str 		=    image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									UpperCAmelCase__		:Dict 		=    np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]	)

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


						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:Tuple 		=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCAmelCase__		:Any 		=    self.get_dummy_components()
									UpperCAmelCase__		:Union[str, Any] 		=    PNDMScheduler()
									UpperCAmelCase__		:List[str] 		=    StableDiffusionModelEditingPipeline(**__lowerCAmelCase	)
									UpperCAmelCase__		:List[Any] 		=    sd_pipe.to(__lowerCAmelCase	)
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

									UpperCAmelCase__		:List[Any] 		=    self.get_dummy_inputs(__lowerCAmelCase	)
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(__lowerCAmelCase	):
												UpperCAmelCase__		:int 		=    sd_pipe(**__lowerCAmelCase	).images


						def    A__       (  self	)  ->Any:
									super().test_inference_batch_single_identical(expected_max_diff=5e-3	)


						def    A__       (  self	)  ->str:
									super().test_attention_slicing_forward_pass(expected_max_diff=5e-3	)


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

						'''simple docstring'''



						def    A__       (  self	)  ->Tuple:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


						def    A__       (  self  , A=0	)  ->str:
									UpperCAmelCase__		:Optional[Any] 		=    torch.manual_seed(__lowerCAmelCase	)
									UpperCAmelCase__		:List[str] 		=    {
									    """prompt""": """A field of roses""",
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs


						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:Union[str, Any] 		=    """CompVis/stable-diffusion-v1-4"""
									UpperCAmelCase__		:Tuple 		=    StableDiffusionModelEditingPipeline.from_pretrained(__lowerCAmelCase  , safety_checker=__lowerCAmelCase	)
									pipe.to(__lowerCAmelCase	)
									pipe.set_progress_bar_config(disable=__lowerCAmelCase	)
									pipe.enable_attention_slicing()

									UpperCAmelCase__		:List[str] 		=    self.get_inputs()
									UpperCAmelCase__		:str 		=    pipe(**__lowerCAmelCase	).images
									UpperCAmelCase__		:str 		=    image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)

									UpperCAmelCase__		:Tuple 		=    np.array(
									    [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]	)

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

									# make sure image changes after editing
									pipe.edit_model('A pack of roses'  , 'A pack of blue roses'	)

									UpperCAmelCase__		:Union[str, Any] 		=    pipe(**__lowerCAmelCase	).images
									UpperCAmelCase__		:Tuple 		=    image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_12, 5_12, 3)

									assert np.abs(expected_slice - image_slice	).max() > 1e-1


						def    A__       (  self	)  ->Any:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									UpperCAmelCase__		:Union[str, Any] 		=    """CompVis/stable-diffusion-v1-4"""
									UpperCAmelCase__		:Any 		=    DDIMScheduler.from_pretrained(__lowerCAmelCase  , subfolder='scheduler'	)
									UpperCAmelCase__		:List[Any] 		=    StableDiffusionModelEditingPipeline.from_pretrained(
									    __lowerCAmelCase  , scheduler=__lowerCAmelCase  , safety_checker=__lowerCAmelCase	)
									UpperCAmelCase__		:Optional[int] 		=    pipe.to(__lowerCAmelCase	)
									pipe.set_progress_bar_config(disable=__lowerCAmelCase	)
									pipe.enable_attention_slicing(1	)
									pipe.enable_sequential_cpu_offload()

									UpperCAmelCase__		:Dict 		=    self.get_inputs()
									UpperCAmelCase__		:Tuple 		=    pipe(**__lowerCAmelCase	)

									UpperCAmelCase__		:Any 		=    torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 10**9











'''simple docstring'''





from __future__ import annotations

import sys





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    # base cases
    if abs(UpperCAmelCase_						) == 1:
        return a, 0
    elif abs(UpperCAmelCase_						) == 1:
        return 0, b

    _a       , _a				:							List[Any]							       =      a, b
    _a       , _a				:							str							       =      1, 0
    _a       , _a				:							Union[str, Any]							       =      0, 1

    while remainder != 0:
        _a				:							List[Any]							       =      old_remainder // remainder
        _a       , _a				:							str							       =      remainder, old_remainder - quotient * remainder
        _a       , _a				:							Optional[Any]							       =      coeff_a, old_coeff_a - quotient * coeff_a
        _a       , _a				:							Tuple							       =      coeff_b, old_coeff_b - quotient * coeff_b

    # sign correction for negative numbers
    if a < 0:
        _a				:							Tuple							       =      -old_coeff_a
    if b < 0:
        _a				:							Any							       =      -old_coeff_b

    return old_coeff_a, old_coeff_b





def UpperCAmelCase    (       ):
    if len(sys.argv						) < 3:
        print("""2 integer arguments required"""						)
        return 1
    _a				:							Union[str, Any]							       =      int(sys.argv[1]						)
    _a				:							Any							       =      int(sys.argv[2]						)
    print(extended_euclidean_algorithm(UpperCAmelCase_   ,	UpperCAmelCase_						)						)
    return 0


if __name__ == "__main__":
  raise SystemExit(main())


