import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

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






@skip_mps
class      __lowercase					(    _A       ,     _A       ,     unittest.TestCase    ):
      lowercase					 =     IFPipeline
      lowercase					 =     TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
      lowercase					 =     TEXT_TO_IMAGE_BATCH_PARAMS
      lowercase					 =     PipelineTesterMixin.required_optional_params - {'latents'}

      def        __a		(	self				:      Any   )							->		Dict:


         '''simple docstring'''

         return self._get_dummy_components()

      def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Tuple=0   )							->		str:


         '''simple docstring'''

         if str(__lowerCamelCase   ).startswith('''mps'''   ):
            lowercase							=		torch.manual_seed(__lowerCamelCase   )
         else:
            lowercase							=		torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )

         lowercase							=		{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs

      def        __a		(	self				:      Optional[Any]   )							->		str:


         '''simple docstring'''

         self._test_save_load_optional_components()

      @unittest.skipIf(torch_device != '''cuda'''							,							reason='''float16 requires CUDA'''   )
      def        __a		(	self				:      Any   )							->		Optional[Any]:


         '''simple docstring'''

         super().test_save_load_floataa(expected_max_diff=1E-1   )

      def        __a		(	self				:      str   )							->		Union[str, Any]:


         '''simple docstring'''

         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )

      def        __a		(	self				:      Optional[int]   )							->		Tuple:


         '''simple docstring'''

         self._test_save_load_local()

      def        __a		(	self				:      Any   )							->		int:


         '''simple docstring'''

         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2							,							)



      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()							,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''							,							)
      def        __a		(	self				:      str   )							->		Optional[int]:


         '''simple docstring'''

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )






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

      def        __a		(	self				:      int   )							->		Any:


         '''simple docstring'''

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

      def        __a		(	self				:      Union[str, Any]   )							->		int:


         '''simple docstring'''

         lowercase							=		IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''							,							variant='''fp16'''							,							torch_dtype=torch.floataa   )

         lowercase							=		IFSuperResolutionPipeline.from_pretrained(
             '''DeepFloyd/IF-II-L-v1.0'''							,							variant='''fp16'''							,							torch_dtype=torch.floataa							,							text_encoder=__lowerCamelCase							,							tokenizer=__lowerCamelCase   )

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('''cuda'''   )

         lowercase		,lowercase							=		pipe_a.encode_prompt('''anime turtle'''							,							device='''cuda'''   )

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         lowercase							=		None
         lowercase							=		None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

         self._test_if(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         lowercase							=		IFImgaImgPipeline(**pipe_a.components   )
         lowercase							=		IFImgaImgSuperResolutionPipeline(**pipe_a.components   )

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

         self._test_if_imgaimg(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         lowercase							=		IFInpaintingPipeline(**pipe_a.components   )
         lowercase							=		IFInpaintingSuperResolutionPipeline(**pipe_a.components   )

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

         self._test_if_inpainting(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

      def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]   )							->		Union[str, Any]:


         '''simple docstring'''

         _start_torch_memory_measurement()

         lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )
         lowercase							=		pipe_a(
             prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							num_inference_steps=2							,							generator=__lowerCamelCase							,							output_type='''np'''							,							)

         lowercase							=		output.images[0]

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

         lowercase							=		torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         lowercase							=		load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''   )
         assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )

         # pipeline 2

         _start_torch_memory_measurement()

         lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )

         lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )

         lowercase							=		pipe_a(
             prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=2							,							output_type='''np'''							,							)

         lowercase							=		output.images[0]

         assert image.shape == (2_56, 2_56, 3)

         lowercase							=		torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         lowercase							=		load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''   )
         assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )

      def        __a		(	self				:      str							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[Any]   )							->		Optional[Any]:


         '''simple docstring'''

         _start_torch_memory_measurement()

         lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )

         lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )

         lowercase							=		pipe_a(
             prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							num_inference_steps=2							,							generator=__lowerCamelCase							,							output_type='''np'''							,							)

         lowercase							=		output.images[0]

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

         lowercase							=		torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         lowercase							=		load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''   )
         assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )

         # pipeline 2

         _start_torch_memory_measurement()

         lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )

         lowercase							=		floats_tensor((1, 3, 2_56, 2_56)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )
         lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )

         lowercase							=		pipe_a(
             prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							original_image=__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=2							,							output_type='''np'''							,							)

         lowercase							=		output.images[0]

         assert image.shape == (2_56, 2_56, 3)

         lowercase							=		torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         lowercase							=		load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''   )
         assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )



      def        __a		(	self				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Any   )							->		int:


         '''simple docstring'''

         _start_torch_memory_measurement()

         lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )
         lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(1   )   ).to(__lowerCamelCase   )

         lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )
         lowercase							=		pipe_a(
             prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							mask_image=__lowerCamelCase							,							num_inference_steps=2							,							generator=__lowerCamelCase							,							output_type='''np'''							,							)

         lowercase							=		output.images[0]

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

         lowercase							=		torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         lowercase							=		load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''   )
         assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )

         # pipeline 2

         _start_torch_memory_measurement()

         lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )

         lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )
         lowercase							=		floats_tensor((1, 3, 2_56, 2_56)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )
         lowercase							=		floats_tensor((1, 3, 2_56, 2_56)							,							rng=random.Random(1   )   ).to(__lowerCamelCase   )

         lowercase							=		pipe_a(
             prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							mask_image=__lowerCamelCase							,							original_image=__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=2							,							output_type='''np'''							,							)

         lowercase							=		output.images[0]

         assert image.shape == (2_56, 2_56, 3)

         lowercase							=		torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         lowercase							=		load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''   )
         assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )






def        __UpperCAmelCase    (       )->       Union[str, Any]:



   """simple docstring"""







   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()






from __future__ import annotations

import math






def        __UpperCAmelCase    (       UpperCAmelCase       )->       list[int]:



			"""simple docstring"""







			if num <= 0:
						lowercase							=		f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(UpperCAmelCase       )

			lowercase							=		[True] * (num + 1)
			lowercase							=		[]
			lowercase							=		2
			lowercase							=		int(math.sqrt(UpperCAmelCase       )       )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(UpperCAmelCase       )

									# Set multiples of start be False
									for i in range(start * start,		num + 1,		UpperCAmelCase       ):
												if sieve[i] is True:
															lowercase							=		False

						start += 1

			for j in range(end + 1,		num + 1       ):
						if sieve[j] is True:
									prime.append(UpperCAmelCase       )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input("Enter a positive integer: ").strip())))





