





"""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, KandinskyImgaImgPipeline, 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 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					KandinskyImgaImgPipeline
 _lowerCamelCase				=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
 _lowerCamelCase				=					[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 _lowerCamelCase				=					[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 _lowerCamelCase				=					False

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return 32

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return 32

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return self.time_input_dim

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return self.time_input_dim * 4

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return 100

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''  )
     return tokenizer

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A   :						Dict									=					MCLIPConfig(
         numDims=self.cross_attention_dim ,						transformerDimensions=self.text_embedder_hidden_size ,						hidden_size=self.text_embedder_hidden_size ,						intermediate_size=37 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						vocab_size=1005 ,						)

     __A   :						int									=					MultilingualCLIP(__lowerCamelCase  )
     __A   :						Union[str, Any]									=					text_encoder.eval()

     return text_encoder

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     torch.manual_seed(0  )

     __A   :						Optional[int]									=					{
         '''in_channels''': 4,
         # 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,
     }

     __A   :						List[str]									=					UNetaDConditionModel(**__lowerCamelCase  )
     return model

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return {
         "block_out_channels": [32, 64],
         "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": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A   :						List[str]									=					VQModel(**self.dummy_movq_kwargs  )
     return model

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					self.dummy_text_encoder
     __A   :						int									=					self.dummy_tokenizer
     __A   :						List[Any]									=					self.dummy_unet
     __A   :						Tuple									=					self.dummy_movq

     __A   :						Any									=					{
         '''num_train_timesteps''': 1000,
         '''beta_schedule''': '''linear''',
         '''beta_start''': 0.0_0_0_8_5,
         '''beta_end''': 0.0_1_2,
         '''clip_sample''': False,
         '''set_alpha_to_one''': False,
         '''steps_offset''': 0,
         '''prediction_type''': '''epsilon''',
         '''thresholding''': False,
     }

     __A   :						Any									=					DDIMScheduler(**__lowerCamelCase  )

     __A   :						Dict									=					{
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''movq''': movq,
     }

     return components

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase=0  ):



     '''simple docstring'''




     __A   :						Tuple									=					floats_tensor((1, self.cross_attention_dim) ,						rng=random.Random(__lowerCamelCase  )  ).to(__lowerCamelCase  )
     __A   :						str									=					floats_tensor((1, self.cross_attention_dim) ,						rng=random.Random(seed + 1  )  ).to(__lowerCamelCase  )
     # create init_image
     __A   :						int									=					floats_tensor((1, 3, 64, 64) ,						rng=random.Random(__lowerCamelCase  )  ).to(__lowerCamelCase  )
     __A   :						Tuple									=					image.cpu().permute(0 ,						2 ,						3 ,						1  )[0]
     __A   :						Dict									=					Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((256, 256)  )

     if str(__lowerCamelCase  ).startswith('''mps'''  ):
         __A   :						Union[str, Any]									=					torch.manual_seed(__lowerCamelCase  )
     else:
         __A   :						Dict									=					torch.Generator(device=__lowerCamelCase  ).manual_seed(__lowerCamelCase  )
     __A   :						Dict									=					{
         '''prompt''': '''horse''',
         '''image''': init_image,
         '''image_embeds''': image_embeds,
         '''negative_image_embeds''': negative_image_embeds,
         '''generator''': generator,
         '''height''': 64,
         '''width''': 64,
         '''num_inference_steps''': 10,
         '''guidance_scale''': 7.0,
         '''strength''': 0.2,
         '''output_type''': '''np''',
     }
     return inputs







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					'''cpu'''

     __A   :						List[str]									=					self.get_dummy_components()

     __A   :						int									=					self.pipeline_class(**__lowerCamelCase  )
     __A   :						Union[str, Any]									=					pipe.to(__lowerCamelCase  )

     pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A   :						Optional[int]									=					pipe(**self.get_dummy_inputs(__lowerCamelCase  )  )
     __A   :						Dict									=					output.images

     __A   :						int									=					pipe(
         **self.get_dummy_inputs(__lowerCamelCase  ) ,						return_dict=__lowerCamelCase ,						)[0]

     __A   :						int									=					image[0, -3:, -3:, -1]
     __A   :						Tuple									=					image_from_tuple[0, -3:, -3:, -1]

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

     __A   :						int									=					np.array(
         [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]  )
     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()}"""






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





 """simple docstring"""


 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/kandinsky/kandinsky_img2img_frog.npy'''  )

     __A   :						int									=					load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''  )
     __A   :						Tuple									=					'''A red cartoon frog, 4k'''

     __A   :						Dict									=					KandinskyPriorPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-1-prior''' ,						torch_dtype=torch.floataa  )
     pipe_prior.to(__lowerCamelCase  )

     __A   :						int									=					KandinskyImgaImgPipeline.from_pretrained(
         '''kandinsky-community/kandinsky-2-1''' ,						torch_dtype=torch.floataa  )
     __A   :						Optional[int]									=					pipeline.to(__lowerCamelCase  )

     pipeline.set_progress_bar_config(disable=__lowerCamelCase  )

     __A   :						Any									=					torch.Generator(device='''cpu'''  ).manual_seed(0  )
     __A			,	__A   :						List[Any]									=					pipe_prior(
         __lowerCamelCase ,						generator=__lowerCamelCase ,						num_inference_steps=5 ,						negative_prompt='''''' ,						).to_tuple()

     __A   :						Any									=					pipeline(
         __lowerCamelCase ,						image=__lowerCamelCase ,						image_embeds=__lowerCamelCase ,						negative_image_embeds=__lowerCamelCase ,						generator=__lowerCamelCase ,						num_inference_steps=100 ,						height=768 ,						width=768 ,						strength=0.2 ,						output_type='''np''' ,						)

     __A   :						Dict									=					output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(__lowerCamelCase ,						__lowerCamelCase  )











"""simple docstring"""



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







def 		__lowercase					(       snake_case_  :  int						)       ->str:



    '''simple docstring'''







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

    __A   :						int									=					precision
    __A   :						Tuple									=					ceil(precision / 14						)
    __A   :						Dict									=					426880 * Decimal(10005						).sqrt()
    __A   :						Optional[Any]									=					1
    __A   :						int									=					13591409
    __A   :						Optional[int]									=					Decimal(snake_case_						)
    for k in range(1    ,snake_case_						):
        __A   :						int									=					factorial(6 * k						) // (factorial(3 * k						) * factorial(snake_case_						) ** 3)
        linear_term += 545140134
        exponential_term *= -262537412640768000
        partial_sum += Decimal(multinomial_term * linear_term						) / exponential_term
    return str(constant_term / partial_sum						)[:-1]


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




