import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()

@skip_mps
class 					_lowercase    (     UpperCAmelCase__ ,  UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[Any]          =    StableDiffusionPanoramaPipeline
    SCREAMING_SNAKE_CASE:				List[str]          =    TEXT_TO_IMAGE_PARAMS
    SCREAMING_SNAKE_CASE:				int          =    TEXT_TO_IMAGE_BATCH_PARAMS
    SCREAMING_SNAKE_CASE:				List[Any]          =    TEXT_TO_IMAGE_IMAGE_PARAMS
    SCREAMING_SNAKE_CASE:				Any          =    TEXT_TO_IMAGE_IMAGE_PARAMS




    def      _a    (    self ):
       torch.manual_seed(0 )
       lowerCAmelCase_:							Optional[int]       =	UNetaDConditionModel(
           block_out_channels=(32, 64)       ,						layers_per_block=1       ,						sample_size=32       ,						in_channels=4       ,						out_channels=4       ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,						cross_attention_dim=32       ,						)
       lowerCAmelCase_:							Dict       =	DDIMScheduler()
       torch.manual_seed(0 )
       lowerCAmelCase_:							str       =	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       ,						)
       torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	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=1_000       ,						)
       lowerCAmelCase_:							Tuple       =	CLIPTextModel(lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

       lowerCAmelCase_:							str       =	{
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__=0 ):
       lowerCAmelCase_:							str       =	torch.manual_seed(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	{
           "prompt": "a photo of the dolomites",
           "generator": generator,
           # Setting height and width to None to prevent OOMs on CPU.
           "height": None,
           "width": None,
           "num_inference_steps": 1,
           "guidance_scale": 6.0,
           "output_type": "numpy",
       }
       return inputs




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowerCAmelCase_:							Union[str, Any]       =	self.get_dummy_components()
       lowerCAmelCase_:							Optional[int]       =	StableDiffusionPanoramaPipeline(**lowerCamelCase__ )
       lowerCAmelCase_:							str       =	sd_pipe.to(lowerCamelCase__ )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	self.get_dummy_inputs(lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	sd_pipe(**lowerCamelCase__ ).images
       lowerCAmelCase_:							List[Any]       =	image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       lowerCAmelCase_:							Optional[Any]       =	np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7] )

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




    def      _a    (    self ):
       super().test_inference_batch_consistent(batch_sizes=[1, 2] )




    def      _a    (    self ):
       super().test_inference_batch_single_identical(batch_size=2       ,						expected_max_diff=3.25E-3 )




    def      _a    (    self ):
       lowerCAmelCase_:							Any       =	"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowerCAmelCase_:							Tuple       =	self.get_dummy_components()
       lowerCAmelCase_:							Optional[Any]       =	StableDiffusionPanoramaPipeline(**lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	sd_pipe.to(lowerCamelCase__ )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	self.get_dummy_inputs(lowerCamelCase__ )
       lowerCAmelCase_:							str       =	"french fries"
       lowerCAmelCase_:							Optional[int]       =	sd_pipe(**lowerCamelCase__       ,						negative_prompt=lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	output.images
       lowerCAmelCase_:							Dict       =	image[0, -3:, -3:, -1]

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

       lowerCAmelCase_:							Union[str, Any]       =	np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )

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




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowerCAmelCase_:							int       =	self.get_dummy_components()
       lowerCAmelCase_:							Any       =	StableDiffusionPanoramaPipeline(**lowerCamelCase__ )
       lowerCAmelCase_:							str       =	sd_pipe.to(lowerCamelCase__ )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	self.get_dummy_inputs(lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	sd_pipe(**lowerCamelCase__       ,						view_batch_size=2 )
       lowerCAmelCase_:							Any       =	output.images
       lowerCAmelCase_:							str       =	image[0, -3:, -3:, -1]

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

       lowerCAmelCase_:							Dict       =	np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )

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




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowerCAmelCase_:							Optional[int]       =	self.get_dummy_components()
       lowerCAmelCase_:							Any       =	EulerAncestralDiscreteScheduler(
           beta_start=0.0_0_0_8_5       ,						beta_end=0.0_1_2       ,						beta_schedule="scaled_linear" )
       lowerCAmelCase_:							List[Any]       =	StableDiffusionPanoramaPipeline(**lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	sd_pipe.to(lowerCamelCase__ )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Optional[Any]       =	self.get_dummy_inputs(lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	sd_pipe(**lowerCamelCase__ ).images
       lowerCAmelCase_:							List[str]       =	image[0, -3:, -3:, -1]

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

       lowerCAmelCase_:							Union[str, Any]       =	np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2] )

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




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[Any]       =	"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowerCAmelCase_:							List[Any]       =	self.get_dummy_components()
       lowerCAmelCase_:							Tuple       =	PNDMScheduler(
           beta_start=0.0_0_0_8_5       ,						beta_end=0.0_1_2       ,						beta_schedule="scaled_linear"       ,						skip_prk_steps=lowerCamelCase__ )
       lowerCAmelCase_:							Union[str, Any]       =	StableDiffusionPanoramaPipeline(**lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	sd_pipe.to(lowerCamelCase__ )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Optional[int]       =	self.get_dummy_inputs(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	sd_pipe(**lowerCamelCase__ ).images
       lowerCAmelCase_:							Optional[Any]       =	image[0, -3:, -3:, -1]

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

       lowerCAmelCase_:							List[str]       =	np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9] )

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





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






    '''simple docstring'''




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




    def      _a    (    self       ,						lowerCamelCase__=0 ):
       lowerCAmelCase_:							Optional[Any]       =	torch.manual_seed(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	{
           "prompt": "a photo of the dolomites",
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	"stabilityai/stable-diffusion-2-base"
       lowerCAmelCase_:							str       =	DDIMScheduler.from_pretrained(lowerCamelCase__       ,						subfolder="scheduler" )
       lowerCAmelCase_:							int       =	StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase__       ,						scheduler=lowerCamelCase__       ,						safety_checker=lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )
       pipe.enable_attention_slicing()

       lowerCAmelCase_:							int       =	self.get_inputs()
       lowerCAmelCase_:							List[str]       =	pipe(**lowerCamelCase__ ).images
       lowerCAmelCase_:							Union[str, Any]       =	image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 2_048, 3)

       lowerCAmelCase_:							Dict       =	np.array(
           [
               0.3_6_9_6_8_3_9_2,
               0.2_7_0_2_5_3_7_2,
               0.3_2_4_4_6_7_6_6,
               0.2_8_3_7_9_3_8_7,
               0.3_6_3_6_3_2_7_4,
               0.3_0_7_3_3_3_4_7,
               0.2_7_1_0_0_0_2_7,
               0.2_7_0_5_4_1_2_5,
               0.2_5_5_3_6_0_9_6,
           ] )

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




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	StableDiffusionPanoramaPipeline.from_pretrained(
           "stabilityai/stable-diffusion-2-base"       ,						safety_checker=lowerCamelCase__ )
       lowerCAmelCase_:							Union[str, Any]       =	LMSDiscreteScheduler.from_config(pipe.scheduler.config )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )
       pipe.enable_attention_slicing()

       lowerCAmelCase_:							Optional[int]       =	self.get_inputs()
       lowerCAmelCase_:							Union[str, Any]       =	pipe(**lowerCamelCase__ ).images
       lowerCAmelCase_:							Optional[Any]       =	image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 2_048, 3)

       lowerCAmelCase_:							Dict       =	np.array(
           [
               [
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
               ]
           ] )

       assert np.abs(expected_slice - image_slice ).max() < 1E-3




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	0

       def callback_fn(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ) -> None:
          lowerCAmelCase_:							Any       =	True
          nonlocal number_of_steps
          number_of_steps += 1
          if step == 1:
             lowerCAmelCase_:							int       =	latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 256)
             lowerCAmelCase_:							Optional[int]       =	latents[0, -3:, -3:, -1]

             lowerCAmelCase_:							Union[str, Any]       =	np.array(
                 [
                     0.1_8_6_8_1_8_6_9,
                     0.3_3_9_0_7_8_1_6,
                     0.5_3_6_1_2_7_6,
                     0.1_4_4_3_2_8_6_5,
                     -0.0_2_8_5_6_6_1_1,
                     -0.7_3_9_4_1_1_2_3,
                     0.2_3_3_9_7_9_8_7,
                     0.4_7_3_2_2_6_8_2,
                     -0.3_7_8_2_3_1_6_4,
                 ] )
             assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
          elif step == 2:
             lowerCAmelCase_:							List[Any]       =	latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 256)
             lowerCAmelCase_:							Tuple       =	latents[0, -3:, -3:, -1]

             lowerCAmelCase_:							Union[str, Any]       =	np.array(
                 [
                     0.1_8_5_3_9_6_4_5,
                     0.3_3_9_8_7_2_4_8,
                     0.5_3_7_8_5_5_9,
                     0.1_4_4_3_7_1_4_2,
                     -0.0_2_4_5_5_2_6_1,
                     -0.7_3_3_8_3_1_7,
                     0.2_3_9_9_0_7_5_5,
                     0.4_7_3_5_6_2_7_2,
                     -0.3_7_8_6_5_0_5,
                 ] )

             assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2

       lowerCAmelCase_:							int       =	False

       lowerCAmelCase_:							Union[str, Any]       =	"stabilityai/stable-diffusion-2-base"
       lowerCAmelCase_:							Any       =	DDIMScheduler.from_pretrained(lowerCamelCase__       ,						subfolder="scheduler" )
       lowerCAmelCase_:							Dict       =	StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase__       ,						scheduler=lowerCamelCase__       ,						safety_checker=lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )
       pipe.enable_attention_slicing()

       lowerCAmelCase_:							Tuple       =	self.get_inputs()
       pipe(**lowerCamelCase__       ,						callback=lowerCamelCase__       ,						callback_steps=1 )
       assert callback_fn.has_been_called
       assert number_of_steps == 3




    def      _a    (    self ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       lowerCAmelCase_:							List[Any]       =	"stabilityai/stable-diffusion-2-base"
       lowerCAmelCase_:							Optional[int]       =	DDIMScheduler.from_pretrained(lowerCamelCase__       ,						subfolder="scheduler" )
       lowerCAmelCase_:							Union[str, Any]       =	StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase__       ,						scheduler=lowerCamelCase__       ,						safety_checker=lowerCamelCase__ )
       lowerCAmelCase_:							int       =	pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )
       pipe.enable_attention_slicing(1 )
       pipe.enable_sequential_cpu_offload()

       lowerCAmelCase_:							Any       =	self.get_inputs()
       lowerCAmelCase_:							Dict       =	pipe(**lowerCamelCase__ )

       lowerCAmelCase_:							str       =	torch.cuda.max_memory_allocated()
       # make sure that less than 5.2 GB is allocated
       assert mem_bytes < 5.5 * 10**9
from math import isqrt
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							List[str]       =	[True] * max_number
   for i in range(2							,				isqrt(max_number - 1					) + 1					):
      if is_prime[i]:
         for j in range(i**2							,				lowercase							,				lowercase					):
            lowerCAmelCase_:							Dict       =	False

   return [i for i in range(2							,				lowercase					) if is_prime[i]]
def 		snake_case__	(		lowercase = 10**8					):
   lowerCAmelCase_:							Union[str, Any]       =	calculate_prime_numbers(max_number // 2					)

   lowerCAmelCase_:							Tuple       =	0
   lowerCAmelCase_:							List[Any]       =	0
   lowerCAmelCase_:							Dict       =	len(lowercase					) - 1
   while left <= right:
      while prime_numbers[left] * prime_numbers[right] >= max_number:
         right -= 1
      semiprimes_count += right - left + 1
      left += 1

   return semiprimes_count


if __name__ == "__main__":
 print(f'''{solution() = }''')