"""simple docstring"""





import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
@skip_mps
class        _snake_case   (    a__ ,       unittest.TestCase						):
						snake_case__	     =	TextToVideoSDPipeline
						snake_case__	     =	TEXT_TO_IMAGE_PARAMS
						snake_case__	     =	TEXT_TO_IMAGE_BATCH_PARAMS
						# No `output_type`.
						snake_case__	     =	frozenset(
						    [
						        "num_inference_steps",
						        "generator",
						        "latents",
						        "return_dict",
						        "callback",
						        "callback_steps",
						    ]						)






						def 					lowerCamelCase__   (				self					:	str			):
													torch.manual_seed(0			)
													__lowerCamelCase    :						Optional[int]						      =   UNetaDConditionModel(
													    block_out_channels=(32, 64, 64, 64)							,    layers_per_block=2							,    sample_size=32							,    in_channels=4							,    out_channels=4							,    down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")							,    up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")							,    cross_attention_dim=32							,    attention_head_dim=4							,    )
													__lowerCamelCase    :						Optional[int]						      =   DDIMScheduler(
													    beta_start=0.0_0_0_8_5							,    beta_end=0.0_1_2							,    beta_schedule="scaled_linear"							,    clip_sample=UpperCAmelCase							,    set_alpha_to_one=UpperCAmelCase							,    )
													torch.manual_seed(0			)
													__lowerCamelCase    :						Dict						      =   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							,    sample_size=128							,    )
													torch.manual_seed(0			)
													__lowerCamelCase    :						List[Any]						      =   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=1000							,    hidden_act="gelu"							,    projection_dim=512							,    )
													__lowerCamelCase    :						Dict						      =   CLIPTextModel(UpperCAmelCase			)
													__lowerCamelCase    :						Tuple						      =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													__lowerCamelCase    :						Any						      =   {
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """vae""": vae,
													    """text_encoder""": text_encoder,
													    """tokenizer""": tokenizer,
													}
													return components






						def 					lowerCamelCase__   (				self					:	int							,    UpperCAmelCase					:	Any							,    UpperCAmelCase					:	Optional[int]=0			):






													if str(UpperCAmelCase			).startswith("mps"			):
																				__lowerCamelCase    :						Tuple						      =   torch.manual_seed(UpperCAmelCase			)
													else:
																				__lowerCamelCase    :						str						      =   torch.Generator(device=UpperCAmelCase			).manual_seed(UpperCAmelCase			)
													__lowerCamelCase    :						Any						      =   {
													    """prompt""": """A painting of a squirrel eating a burger""",
													    """generator""": generator,
													    """num_inference_steps""": 2,
													    """guidance_scale""": 6.0,
													    """output_type""": """pt""",
													}
													return inputs






						def 					lowerCamelCase__   (				self					:	Tuple			):
													__lowerCamelCase    :						Tuple						      =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
													__lowerCamelCase    :						List[str]						      =   self.get_dummy_components()
													__lowerCamelCase    :						Tuple						      =   TextToVideoSDPipeline(**UpperCAmelCase			)
													__lowerCamelCase    :						str						      =   sd_pipe.to(UpperCAmelCase			)
													sd_pipe.set_progress_bar_config(disable=UpperCAmelCase			)

													__lowerCamelCase    :						Any						      =   self.get_dummy_inputs(UpperCAmelCase			)
													__lowerCamelCase    :						Union[str, Any]						      =   """np"""
													__lowerCamelCase    :						Any						      =   sd_pipe(**UpperCAmelCase			).frames
													__lowerCamelCase    :						str						      =   frames[0][-3:, -3:, -1]

													assert frames[0].shape == (64, 64, 3)
													__lowerCamelCase    :						Optional[Any]						      =   np.array([1_5_8.0, 1_6_0.0, 1_5_3.0, 1_2_5.0, 1_0_0.0, 1_2_1.0, 1_1_1.0, 9_3.0, 1_1_3.0]			)

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






						def 					lowerCamelCase__   (				self					:	Optional[Any]			):
													self._test_attention_slicing_forward_pass(test_mean_pixel_difference=UpperCAmelCase							,    expected_max_diff=3E-3			)






						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()							,    reason="XFormers attention is only available with CUDA and `xformers` installed"							,    )
						def 					lowerCamelCase__   (				self					:	Tuple			):
													self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=UpperCAmelCase							,    expected_max_diff=1E-2			)






						@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."			)
						def 					lowerCamelCase__   (				self					:	Dict			):
													pass






						@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."			)
						def 					lowerCamelCase__   (				self					:	List[str]			):
													pass






						@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."			)
						def 					lowerCamelCase__   (				self					:	List[Any]			):
													pass







						def 					lowerCamelCase__   (				self					:	Optional[int]			):
													return super().test_progress_bar()

@slow
@skip_mps
class        _snake_case   (    unittest.TestCase						):






						def 					lowerCamelCase__   (				self					:	List[Any]			):
													__lowerCamelCase    :						Optional[Any]						      =   load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"			)

													__lowerCamelCase    :						int						      =   TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"			)
													__lowerCamelCase    :						Any						      =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
													__lowerCamelCase    :						Optional[int]						      =   pipe.to("cuda"			)

													__lowerCamelCase    :						Optional[Any]						      =   """Spiderman is surfing"""
													__lowerCamelCase    :						Tuple						      =   torch.Generator(device="cpu"			).manual_seed(0			)

													__lowerCamelCase    :						Tuple						      =   pipe(UpperCAmelCase							,    generator=UpperCAmelCase							,    num_inference_steps=25							,    output_type="pt"			).frames
													__lowerCamelCase    :						Optional[int]						      =   video_frames.cpu().numpy()

													assert np.abs(expected_video - video			).mean() < 5E-2







						def 					lowerCamelCase__   (				self					:	Optional[int]			):
													__lowerCamelCase    :						Optional[Any]						      =   load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"			)

													__lowerCamelCase    :						Any						      =   TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b"			)
													__lowerCamelCase    :						Union[str, Any]						      =   pipe.to("cuda"			)

													__lowerCamelCase    :						Optional[int]						      =   """Spiderman is surfing"""
													__lowerCamelCase    :						Optional[int]						      =   torch.Generator(device="cpu"			).manual_seed(0			)

													__lowerCamelCase    :						List[str]						      =   pipe(UpperCAmelCase							,    generator=UpperCAmelCase							,    num_inference_steps=2							,    output_type="pt"			).frames
													__lowerCamelCase    :						Dict						      =   video_frames.cpu().numpy()

													assert np.abs(expected_video - video			).mean() < 5E-2



"""simple docstring"""






import math
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       list[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						List[Any]      							=					[]
							__UpperCAmelCase   :						Dict      							=					2
							__UpperCAmelCase   :						Union[str, Any]      							=					int(math.sqrt(_UpperCamelCase							)							)  # Size of every segment
							__UpperCAmelCase   :						Tuple      							=					[True] * (end + 1)
							__UpperCAmelCase   :						int      							=					[]

							while start <= end:
														if temp[start] is True:
																					in_prime.append(_UpperCamelCase							)
																					for i in range(start * start       ,			end + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Dict      							=					False
														start += 1
							prime += in_prime

							__UpperCAmelCase   :						Optional[int]      							=					end + 1
							__UpperCAmelCase   :						Dict      							=					min(2 * end       ,			_UpperCamelCase							)

							while low <= n:
														__UpperCAmelCase   :						Union[str, Any]      							=					[True] * (high - low + 1)
														for each in in_prime:
																					__UpperCAmelCase   :						Dict      							=					math.floor(low / each							) * each
																					if t < low:
																												t += each

																					for j in range(_UpperCamelCase       ,			high + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Tuple      							=					False

														for j in range(len(_UpperCamelCase							)							):
																					if temp[j] is True:
																												prime.append(j + low							)

														__UpperCAmelCase   :						Tuple      							=					high + 1
														__UpperCAmelCase   :						Optional[int]      							=					min(high + end       ,			_UpperCamelCase							)

							return prime
print(sieve(10**6))
