



'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class _UpperCamelCase		(     A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    KandinskyInpaintPipeline
				lowerCAmelCase__					    =    ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
				lowerCAmelCase__					    =    [
				    """prompt""",
				    """negative_prompt""",
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				    """mask_image""",
				]
				lowerCAmelCase__					    =    [
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """negative_prompt""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				lowerCAmelCase__					    =    False

				@property
				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											return 3_2

				@property
				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											return 3_2

				@property
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											return self.time_input_dim

				@property
				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											return self.time_input_dim * 4

				@property
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											return 1_0_0

				@property
				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											__lowercase       	=XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
											return tokenizer

				@property
				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											torch.manual_seed(0)
											__lowercase       	=MCLIPConfig(
											    numDims=self.cross_attention_dim  ,       transformerDimensions=self.text_embedder_hidden_size  ,       hidden_size=self.text_embedder_hidden_size  ,       intermediate_size=3_7  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       vocab_size=1_0_0_5  ,       )

											__lowercase       	=MultilingualCLIP(_lowerCAmelCase)
											__lowercase       	=text_encoder.eval()

											return text_encoder

				@property
				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											torch.manual_seed(0)

											__lowercase       	={
											    'in_channels': 9,
											    # Out channels is double in channels because predicts mean and variance
											    'out_channels': 8,
											    'addition_embed_type': 'text_image',
											    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
											    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
											    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
											    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
											    'layers_per_block': 1,
											    'encoder_hid_dim': self.text_embedder_hidden_size,
											    'encoder_hid_dim_type': 'text_image_proj',
											    'cross_attention_dim': self.cross_attention_dim,
											    'attention_head_dim': 4,
											    'resnet_time_scale_shift': 'scale_shift',
											    'class_embed_type': None,
											}

											__lowercase       	=UNetaDConditionModel(**_lowerCAmelCase)
											return model

				@property
				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											return {
											    "block_out_channels": [3_2, 6_4],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 1_2,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}

				@property
				def 				__lowerCamelCase     (							self  :  Any):







											'''simple docstring'''

											torch.manual_seed(0)
											__lowercase       	=VQModel(**self.dummy_movq_kwargs)
											return model

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=self.dummy_text_encoder
											__lowercase       	=self.dummy_tokenizer
											__lowercase       	=self.dummy_unet
											__lowercase       	=self.dummy_movq

											__lowercase       	=DDIMScheduler(
											    num_train_timesteps=1_0_0_0  ,       beta_schedule='linear'  ,       beta_start=0.0_0085  ,       beta_end=0.012  ,       clip_sample=_lowerCAmelCase  ,       set_alpha_to_one=_lowerCAmelCase  ,       steps_offset=1  ,       prediction_type='epsilon'  ,       thresholding=_lowerCAmelCase  ,       )

											__lowercase       	={
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'unet': unet,
											    'scheduler': scheduler,
											    'movq': movq,
											}

											return components

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  List[str]=0):







											'''simple docstring'''

											__lowercase       	=floats_tensor((1, self.cross_attention_dim)  ,       rng=random.Random(_lowerCAmelCase)).to(_lowerCAmelCase)
											__lowercase       	=floats_tensor((1, self.cross_attention_dim)  ,       rng=random.Random(seed + 1)).to(_lowerCAmelCase)
											# create init_image
											__lowercase       	=floats_tensor((1, 3, 6_4, 6_4)  ,       rng=random.Random(_lowerCAmelCase)).to(_lowerCAmelCase)
											__lowercase       	=image.cpu().permute(0  ,       2  ,       3  ,       1)[0]
											__lowercase       	=Image.fromarray(np.uinta(_lowerCAmelCase)).convert('RGB').resize((2_5_6, 2_5_6))
											# create mask
											__lowercase       	=np.ones((6_4, 6_4)  ,       dtype=np.floataa)
											__lowercase       	=0

											if str(_lowerCAmelCase).startswith('mps'):
																		__lowercase       	=torch.manual_seed(_lowerCAmelCase)
											else:
																		__lowercase       	=torch.Generator(device=_lowerCAmelCase).manual_seed(_lowerCAmelCase)
											__lowercase       	={
											    'prompt': 'horse',
											    'image': init_image,
											    'mask_image': mask,
											    'image_embeds': image_embeds,
											    'negative_image_embeds': negative_image_embeds,
											    'generator': generator,
											    'height': 6_4,
											    'width': 6_4,
											    'num_inference_steps': 2,
											    'guidance_scale': 4.0,
											    'output_type': 'np',
											}
											return inputs

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	='cpu'

											__lowercase       	=self.get_dummy_components()

											__lowercase       	=self.pipeline_class(**_lowerCAmelCase)
											__lowercase       	=pipe.to(_lowerCAmelCase)

											pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	=pipe(**self.get_dummy_inputs(_lowerCAmelCase))
											__lowercase       	=output.images

											__lowercase       	=pipe(
											    **self.get_dummy_inputs(_lowerCAmelCase)  ,       return_dict=_lowerCAmelCase  ,       )[0]

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

											print(f"""image.shape {image.shape}""")

											assert image.shape == (1, 6_4, 6_4, 3)

											__lowercase       	=np.array(
											    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786])

											assert (
											    np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""

				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

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





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





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

											__lowercase       	=load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
											__lowercase       	=np.ones((7_6_8, 7_6_8)  ,       dtype=np.floataa)
											__lowercase       	=0

											__lowercase       	='a hat'

											__lowercase       	=KandinskyPriorPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-1-prior'  ,       torch_dtype=torch.floataa)
											pipe_prior.to(_lowerCAmelCase)

											__lowercase       	=KandinskyInpaintPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-1-inpaint'  ,       torch_dtype=torch.floataa)
											__lowercase       	=pipeline.to(_lowerCAmelCase)
											pipeline.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	=torch.Generator(device='cpu').manual_seed(0)
											__lowercase	,				__lowercase       	=pipe_prior(
											    _lowerCAmelCase  ,       generator=_lowerCAmelCase  ,       num_inference_steps=5  ,       negative_prompt=''  ,       ).to_tuple()

											__lowercase       	=pipeline(
											    _lowerCAmelCase  ,       image=_lowerCAmelCase  ,       mask_image=_lowerCAmelCase  ,       image_embeds=_lowerCAmelCase  ,       negative_image_embeds=_lowerCAmelCase  ,       generator=_lowerCAmelCase  ,       num_inference_steps=1_0_0  ,       height=7_6_8  ,       width=7_6_8  ,       output_type='np'  ,       )

											__lowercase       	=output.images[0]

											assert image.shape == (7_6_8, 7_6_8, 3)

											assert_mean_pixel_difference(_lowerCAmelCase  ,       _lowerCAmelCase)








'''simple docstring'''



from decimal import Decimal, getcontext
from math import ceil, factorial



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if not isinstance(_lowerCAmelCase  ,   _lowerCAmelCase					):
														raise TypeError('Undefined for non-integers'					)
							elif precision < 1:
														raise ValueError('Undefined for non-natural numbers'					)

							__lowercase       	=precision
							__lowercase       	=ceil(precision / 14					)
							__lowercase       	=426_880 * Decimal(10_005					).sqrt()
							__lowercase       	=1
							__lowercase       	=13_591_409
							__lowercase       	=Decimal(_lowerCAmelCase					)
							for k in range(1  ,   _lowerCAmelCase					):
														__lowercase       	=factorial(6 * k					) // (factorial(3 * k					) * factorial(_lowerCAmelCase					) ** 3)
														linear_term += 545_140_134
														exponential_term *= -262_537_412_640_768_000
														partial_sum += Decimal(multinomial_term * linear_term					) / exponential_term
							return str(constant_term / partial_sum					)[:-1]


if __name__ == "__main__":
		lowerCamelCase	 =       50
		print(f"The first {n} digits of pi is: {pi(n)}")



