
'''simple docstring'''


import math
from collections.abc import Generator




def lowercase_		(				_lowercase			)       ->							Generator[int, None, None]:
					'''simple docstring'''

					lowerCamelCase_   : Generator       =						(i for i in range(1     ,    (max_n + 1)			))
					for i in (n for n in numbers if n > 1):
										for j in range(2     ,    _lowercase			):
															if (i % j) == 0:
																				break
										else:
															yield i




def lowercase_		(				_lowercase			)       ->							Generator[int, None, None]:
					'''simple docstring'''

					lowerCamelCase_   : Generator       =						(i for i in range(1     ,    (max_n + 1)			))
					for i in (n for n in numbers if n > 1):
										# only need to check for factors up to sqrt(i)
										lowerCamelCase_   : Optional[int]       =						int(math.sqrt(_lowercase			)			) + 1
										for j in range(2     ,    _lowercase			):
															if (i % j) == 0:
																				break
										else:
															yield i




def lowercase_		(				_lowercase			)       ->							Generator[int, None, None]:
					'''simple docstring'''

					lowerCamelCase_   : Generator       =						(i for i in range(1     ,    (max_n + 1)     ,    2			))
					# It's useless to test even numbers as they will not be prime
					if max_n > 2:
										yield 2  # Because 2 will not be tested, it's necessary to yield it now
					for i in (n for n in numbers if n > 1):
										lowerCamelCase_   : Tuple       =						int(math.sqrt(_lowercase			)			) + 1
										for j in range(3     ,    _lowercase     ,    2			):
															# As we removed the even numbers, we don't need them now
															if (i % j) == 0:
																				break
										else:
															yield i




def lowercase_		(				)       ->							int:
					'''simple docstring'''

					from timeit import timeit

					lowerCamelCase_   : Union[str, Any]       =						'''from __main__ import slow_primes, primes, fast_primes'''
					print(timeit('''slow_primes(1_000_000_000_000)'''     ,    setup=_lowercase     ,    number=1_000_000			)			)
					print(timeit('''primes(1_000_000_000_000)'''     ,    setup=_lowercase     ,    number=1_000_000			)			)
					print(timeit('''fast_primes(1_000_000_000_000)'''     ,    setup=_lowercase     ,    number=1_000_000			)			)


if __name__ == "__main__":
					__lowercase							:				Tuple					=       int(input('''Calculate primes up to:\n>> ''').strip())
					for ret in primes(number):
										print(ret)
					benchmark()




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 		__lowercase			(					_lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						List[str]		 =     KandinskyPipeline
			lowerCamelCase     :						List[str]		 =     [
			    "prompt",
			    "image_embeds",
			    "negative_image_embeds",
			]
			lowerCamelCase     :						str		 =     ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
			lowerCamelCase     :						Dict		 =     [
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCamelCase     :						Union[str, Any]		 =     False

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

			@property
			def     UpperCAmelCase__	(self ):
								return self.time_input_dim

			@property
			def     UpperCAmelCase__	(self ):
								return self.time_input_dim * 4

			@property
			def     UpperCAmelCase__	(self ):
								return 1_0_0

			@property
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
								return tokenizer

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Any       =						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     ,				)

								lowerCamelCase_   : Union[str, Any]       =						MultilingualCLIP(A )
								lowerCamelCase_   : Dict       =						text_encoder.eval()

								return text_encoder

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )

								lowerCamelCase_   : Dict       =						{
								    '''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,
								}

								lowerCamelCase_   : Union[str, Any]       =						UNetaDConditionModel(**A )
								return model

			@property
			def     UpperCAmelCase__	(self ):
								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     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Dict       =						VQModel(**self.dummy_movq_kwargs )
								return model

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						self.dummy_text_encoder
								lowerCamelCase_   : Tuple       =						self.dummy_tokenizer
								lowerCamelCase_   : int       =						self.dummy_unet
								lowerCamelCase_   : str       =						self.dummy_movq

								lowerCamelCase_   : str       =						DDIMScheduler(
								    num_train_timesteps=1_0_0_0     ,				beta_schedule='''linear'''     ,				beta_start=0.0_00_85     ,				beta_end=0.0_12     ,				clip_sample=A     ,				set_alpha_to_one=A     ,				steps_offset=1     ,				prediction_type='''epsilon'''     ,				thresholding=A     ,				)

								lowerCamelCase_   : int       =						{
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}
								return components

			def     UpperCAmelCase__	(self     ,				A     ,				A=0 ):
								lowerCamelCase_   : Union[str, Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(A ) ).to(A )
								lowerCamelCase_   : List[Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(seed + 1 ) ).to(A )
								if str(A ).startswith('''mps''' ):
													lowerCamelCase_   : List[str]       =						torch.manual_seed(A )
								else:
													lowerCamelCase_   : Optional[Any]       =						torch.Generator(device=A ).manual_seed(A )
								lowerCamelCase_   : Any       =						{
								    '''prompt''': '''horse''',
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''guidance_scale''': 4.0,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''np''',
								}
								return inputs






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						'''cpu'''

								lowerCamelCase_   : Optional[Any]       =						self.get_dummy_components()

								lowerCamelCase_   : Tuple       =						self.pipeline_class(**A )
								lowerCamelCase_   : List[str]       =						pipe.to(A )

								pipe.set_progress_bar_config(disable=A )

								lowerCamelCase_   : Optional[Any]       =						pipe(**self.get_dummy_inputs(A ) )
								lowerCamelCase_   : int       =						output.images

								lowerCamelCase_   : List[Any]       =						pipe(
								    **self.get_dummy_inputs(A )     ,				return_dict=A     ,				)[0]

								lowerCamelCase_   : List[Any]       =						image[0, -3:, -3:, -1]
								lowerCamelCase_   : List[Any]       =						image_from_tuple[0, -3:, -3:, -1]

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

								lowerCamelCase_   : int       =						np.array(
								    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53] )

								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 		__lowercase			(					unittest.TestCase      ):

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






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinsky/kandinsky_text2img_cat_fp16.npy''' )

								lowerCamelCase_   : str       =						KandinskyPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-1-prior'''     ,				torch_dtype=torch.floataa )
								pipe_prior.to(A )

								lowerCamelCase_   : List[Any]       =						KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''     ,				torch_dtype=torch.floataa )
								lowerCamelCase_   : Tuple       =						pipeline.to(A )
								pipeline.set_progress_bar_config(disable=A )

								lowerCamelCase_   : List[str]       =						'''red cat, 4k photo'''

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_, lowerCamelCase_   : Any       =						pipe_prior(
								    A     ,				generator=A     ,				num_inference_steps=5     ,				negative_prompt=''''''     ,				).to_tuple()

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_   : Optional[Any]       =						pipeline(
								    A     ,				image_embeds=A     ,				negative_image_embeds=A     ,				generator=A     ,				num_inference_steps=1_0_0     ,				output_type='''np'''     ,				)

								lowerCamelCase_   : Union[str, Any]       =						output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)

								assert_mean_pixel_difference(A     ,				A )


