

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()


class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):
    __lowerCamelCase						:       Dict														=     StableDiffusionLDMaDPipeline
    __lowerCamelCase						:       Any														=     TEXT_TO_IMAGE_PARAMS
    __lowerCamelCase						:       Any														=     TEXT_TO_IMAGE_BATCH_PARAMS
    __lowerCamelCase						:       List[Any]														=     TEXT_TO_IMAGE_IMAGE_PARAMS





    def       snake_case_				(      self):
         torch.manual_seed(0)
         lowercase__    :   str      =       UNetaDConditionModel(
             block_out_channels=(32, 64)						,   layers_per_block=2						,   sample_size=32						,   in_channels=4						,   out_channels=4						,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,   cross_attention_dim=32						,   )
         lowercase__    :   List[str]      =       DDIMScheduler(
             beta_start=0.00_085						,   beta_end=0.012						,   beta_schedule='scaled_linear'						,   clip_sample=a						,   set_alpha_to_one=a						,   )
         torch.manual_seed(0)
         lowercase__    :   Tuple      =       AutoencoderKL(
             block_out_channels=[32, 64]						,   in_channels=6						,   out_channels=6						,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,   latent_channels=4						,   )
         torch.manual_seed(0)
         lowercase__    :   Dict      =       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						,   )
         lowercase__    :   List[Any]      =       CLIPTextModel(a)
         lowercase__    :   Optional[int]      =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

         lowercase__    :   Any      =       {
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components





    def       snake_case_				(      self						,   a						,   a=0):
         if str(a).startswith('mps'):
              lowercase__    :   str      =       torch.manual_seed(a)
         else:
              lowercase__    :   Dict      =       torch.Generator(device=a).manual_seed(a)
         lowercase__    :   Optional[int]      =       {
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 6.0,
             'output_type': 'numpy',
         }
         return inputs





    def       snake_case_				(      self):
         lowercase__    :   Union[str, Any]      =       'cpu'  # ensure determinism for the device-dependent torch.Generator

         lowercase__    :   List[str]      =       self.get_dummy_components()
         lowercase__    :   int      =       StableDiffusionLDMaDPipeline(**a)
         lowercase__    :   Optional[Any]      =       ldmad_pipe.to(a)
         ldmad_pipe.set_progress_bar_config(disable=a)

         lowercase__    :   List[str]      =       self.get_dummy_inputs(a)
         lowercase__    :   Tuple      =       ldmad_pipe(**a)
         lowercase__      , lowercase__    :   str      =       output.rgb, output.depth

         lowercase__    :   Dict      =       rgb[0, -3:, -3:, -1]
         lowercase__    :   Union[str, Any]      =       depth[0, -3:, -1]

         assert rgb.shape == (1, 64, 64, 3)
         assert depth.shape == (1, 64, 64)

         lowercase__    :   List[str]      =       np.array(
             [0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262])
         lowercase__    :   Any      =       np.array([103.46_727, 85.812_004, 87.849_236])

         assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1e-2
         assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1e-2





    def       snake_case_				(      self):
         lowercase__    :   Optional[int]      =       self.get_dummy_components()
         lowercase__    :   int      =       StableDiffusionLDMaDPipeline(**a)
         lowercase__    :   Union[str, Any]      =       ldmad_pipe.to(a)
         ldmad_pipe.set_progress_bar_config(disable=a)

         lowercase__    :   int      =       self.get_dummy_inputs(a)
         lowercase__    :   Dict      =       3 * [inputs['prompt']]

         # forward
         lowercase__    :   Union[str, Any]      =       ldmad_pipe(**a)
         lowercase__      , lowercase__    :   Union[str, Any]      =       output.rgb, output.depth
         lowercase__    :   Optional[int]      =       rgb_slice_a[0, -3:, -3:, -1]
         lowercase__    :   Dict      =       depth_slice_a[0, -3:, -1]

         lowercase__    :   Tuple      =       self.get_dummy_inputs(a)
         lowercase__    :   Any      =       3 * [inputs.pop('prompt')]

         lowercase__    :   Union[str, Any]      =       ldmad_pipe.tokenizer(
             a						,   padding='max_length'						,   max_length=ldmad_pipe.tokenizer.model_max_length						,   truncation=a						,   return_tensors='pt'						,   )
         lowercase__    :   Dict      =       text_inputs['input_ids'].to(a)

         lowercase__    :   Any      =       ldmad_pipe.text_encoder(a)[0]

         lowercase__    :   List[Any]      =       prompt_embeds

         # forward
         lowercase__    :   Optional[int]      =       ldmad_pipe(**a)
         lowercase__      , lowercase__    :   Dict      =       output.rgb, output.depth
         lowercase__    :   int      =       rgb_slice_a[0, -3:, -3:, -1]
         lowercase__    :   Any      =       depth_slice_a[0, -3:, -1]

         assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()).max() < 1e-4
         assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()).max() < 1e-4





    def       snake_case_				(      self):
         lowercase__    :   str      =       'cpu'  # ensure determinism for the device-dependent torch.Generator
         lowercase__    :   str      =       self.get_dummy_components()
         lowercase__    :   str      =       PNDMScheduler(skip_prk_steps=a)
         lowercase__    :   List[str]      =       StableDiffusionLDMaDPipeline(**a)
         lowercase__    :   List[Any]      =       ldmad_pipe.to(a)
         ldmad_pipe.set_progress_bar_config(disable=a)

         lowercase__    :   List[Any]      =       self.get_dummy_inputs(a)
         lowercase__    :   List[Any]      =       'french fries'
         lowercase__    :   Tuple      =       ldmad_pipe(**a						,   negative_prompt=a)

         lowercase__      , lowercase__    :   Optional[Any]      =       output.rgb, output.depth
         lowercase__    :   int      =       rgb[0, -3:, -3:, -1]
         lowercase__    :   str      =       depth[0, -3:, -1]

         assert rgb.shape == (1, 64, 64, 3)
         assert depth.shape == (1, 64, 64)

         lowercase__    :   List[Any]      =       np.array(
             [0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217])
         lowercase__    :   List[Any]      =       np.array([107.84_738, 84.62_802, 89.962_135])
         assert np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1e-2
         assert np.abs(depth_slice.flatten() - expected_slice_depth).max() < 1e-2



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





    def       snake_case_				(      self):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





    def       snake_case_				(      self						,   a						,   a="cpu"						,   a=torch.floataa						,   a=0):
         lowercase__    :   Tuple      =       torch.Generator(device=a).manual_seed(a)
         lowercase__    :   str      =       np.random.RandomState(a).standard_normal((1, 4, 64, 64))
         lowercase__    :   List[Any]      =       torch.from_numpy(a).to(device=a						,   dtype=a)
         lowercase__    :   str      =       {
             'prompt': 'a photograph of an astronaut riding a horse',
             'latents': latents,
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
         }
         return inputs





    def       snake_case_				(      self):
         lowercase__    :   Any      =       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d')
         lowercase__    :   Union[str, Any]      =       ldmad_pipe.to(a)
         ldmad_pipe.set_progress_bar_config(disable=a)

         lowercase__    :   Optional[int]      =       self.get_inputs(a)
         lowercase__    :   Union[str, Any]      =       ldmad_pipe(**a)
         lowercase__      , lowercase__    :   List[str]      =       output.rgb, output.depth
         lowercase__    :   int      =       rgb[0, -3:, -3:, -1].flatten()
         lowercase__    :   str      =       rgb[0, -3:, -1].flatten()

         assert rgb.shape == (1, 512, 512, 3)
         assert depth.shape == (1, 512, 512)

         lowercase__    :   Tuple      =       np.array(
             [0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706])
         lowercase__    :   Tuple      =       np.array(
             [0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706])
         assert np.abs(rgb_slice - expected_slice_rgb).max() < 3e-3
         assert np.abs(depth_slice - expected_slice_depth).max() < 3e-3



@nightly
@require_torch_gpu
class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def       snake_case_				(      self):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





    def       snake_case_				(      self						,   a						,   a="cpu"						,   a=torch.floataa						,   a=0):
         lowercase__    :   List[Any]      =       torch.Generator(device=a).manual_seed(a)
         lowercase__    :   int      =       np.random.RandomState(a).standard_normal((1, 4, 64, 64))
         lowercase__    :   Any      =       torch.from_numpy(a).to(device=a						,   dtype=a)
         lowercase__    :   Optional[int]      =       {
             'prompt': 'a photograph of an astronaut riding a horse',
             'latents': latents,
             'generator': generator,
             'num_inference_steps': 50,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
         }
         return inputs





    def       snake_case_				(      self):
         lowercase__    :   Union[str, Any]      =       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d').to(a)
         ldmad_pipe.set_progress_bar_config(disable=a)

         lowercase__    :   Dict      =       self.get_inputs(a)
         lowercase__    :   Any      =       ldmad_pipe(**a)
         lowercase__      , lowercase__    :   str      =       output.rgb, output.depth

         lowercase__    :   List[str]      =       0.495_586
         lowercase__    :   Any      =       0.33_795_515
         lowercase__    :   Optional[Any]      =       112.48_518
         lowercase__    :   Any      =       98.489_746
         assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
         assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
         assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
         assert np.abs(expected_depth_std - depth.std()) < 1e-3





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c').to(a)
         ldmad_pipe.set_progress_bar_config(disable=a)

         lowercase__    :   Tuple      =       self.get_inputs(a)
         lowercase__    :   Dict      =       ldmad_pipe(**a)
         lowercase__      , lowercase__    :   List[Any]      =       output.rgb, output.depth

         lowercase__    :   Optional[Any]      =       0.4_194_127
         lowercase__    :   Optional[Any]      =       0.35_375_586
         lowercase__    :   Dict      =       0.5_638_502
         lowercase__    :   Union[str, Any]      =       0.34_686_103

         assert rgb.shape == (1, 512, 512, 3)
         assert depth.shape == (1, 512, 512, 1)
         assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
         assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
         assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
         assert np.abs(expected_depth_std - depth.std()) < 1e-3




import math

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       []
     lowercase__    :   str      =       2
     lowercase__    :   Optional[Any]      =       int(math.sqrt(SCREAMING_SNAKE_CASE_			)			)  # Size of every segment
     lowercase__    :   Dict      =       [True] * (end + 1)
     lowercase__    :   Union[str, Any]      =       []

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

     lowercase__    :   Optional[int]      =       end + 1
     lowercase__    :   List[str]      =       min(2 * end	,     SCREAMING_SNAKE_CASE_			)

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

               for j in range(SCREAMING_SNAKE_CASE_	,     high + 1	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[Any]      =       False

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

          lowercase__    :   Optional[Any]      =       high + 1
          lowercase__    :   Optional[int]      =       min(high + end	,     SCREAMING_SNAKE_CASE_			)

     return prime

print(sieve(10**6))

