


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 	A_ (	__UpperCamelCase ,				__UpperCamelCase ,				unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       StableDiffusionPanoramaPipeline
      __snake_case 	=       TEXT_TO_IMAGE_PARAMS
      __snake_case 	=       TEXT_TO_IMAGE_BATCH_PARAMS
      __snake_case 	=       TEXT_TO_IMAGE_IMAGE_PARAMS
      __snake_case 	=       TEXT_TO_IMAGE_IMAGE_PARAMS




      def 					_snake_case     (    self:	List[str]    ):
       torch.manual_seed(0    )
       __lowerCamelCase		:							Any		 =						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		:							Union[str, Any]		 =						DDIMScheduler()
       torch.manual_seed(0    )
       __lowerCamelCase		:							Union[str, Any]		 =						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		:							Union[str, 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						,				)
       __lowerCamelCase		:							Optional[int]		 =						CLIPTextModel(a    )
       __lowerCamelCase		:							int		 =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

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




      def 					_snake_case     (    self:	Any						,				a:	List[str]						,				a:	Dict=0    ):
       __lowerCamelCase		:							List[Any]		 =						torch.manual_seed(a    )
       __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 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							Union[str, Any]		 =						'cpu'  # ensure determinism for the device-dependent torch.Generator
       __lowerCamelCase		:							List[Any]		 =						self.get_dummy_components()
       __lowerCamelCase		:							Optional[int]		 =						StableDiffusionPanoramaPipeline(**a    )
       __lowerCamelCase		:							Dict		 =						sd_pipe.to(a    )
       sd_pipe.set_progress_bar_config(disable=a    )

       __lowerCamelCase		:							int		 =						self.get_dummy_inputs(a    )
       __lowerCamelCase		:							Dict		 =						sd_pipe(**a    ).images
       __lowerCamelCase		:							str		 =						image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       __lowerCamelCase		:							List[str]		 =						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 					_snake_case     (    self:	List[Any]    ):
       super().test_inference_batch_consistent(batch_sizes=[1, 2]    )




      def 					_snake_case     (    self:	List[Any]    ):
       super().test_inference_batch_single_identical(batch_size=2						,				expected_max_diff=3.25e-3    )




      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							Optional[Any]		 =						'cpu'  # ensure determinism for the device-dependent torch.Generator
       __lowerCamelCase		:							Optional[int]		 =						self.get_dummy_components()
       __lowerCamelCase		:							Optional[Any]		 =						StableDiffusionPanoramaPipeline(**a    )
       __lowerCamelCase		:							List[str]		 =						sd_pipe.to(a    )
       sd_pipe.set_progress_bar_config(disable=a    )

       __lowerCamelCase		:							Dict		 =						self.get_dummy_inputs(a    )
       __lowerCamelCase		:							Dict		 =						'french fries'
       __lowerCamelCase		:							Dict		 =						sd_pipe(**a						,				negative_prompt=a    )
       __lowerCamelCase		:							Tuple		 =						output.images
       __lowerCamelCase		:							List[Any]		 =						image[0, -3:, -3:, -1]

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

       __lowerCamelCase		:							int		 =						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 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							Dict		 =						'cpu'  # ensure determinism for the device-dependent torch.Generator
       __lowerCamelCase		:							Union[str, Any]		 =						self.get_dummy_components()
       __lowerCamelCase		:							int		 =						StableDiffusionPanoramaPipeline(**a    )
       __lowerCamelCase		:							Optional[Any]		 =						sd_pipe.to(a    )
       sd_pipe.set_progress_bar_config(disable=a    )

       __lowerCamelCase		:							str		 =						self.get_dummy_inputs(a    )
       __lowerCamelCase		:							Optional[Any]		 =						sd_pipe(**a						,				view_batch_size=2    )
       __lowerCamelCase		:							int		 =						output.images
       __lowerCamelCase		:							Optional[Any]		 =						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 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Tuple		 =						'cpu'  # ensure determinism for the device-dependent torch.Generator
       __lowerCamelCase		:							Any		 =						self.get_dummy_components()
       __lowerCamelCase		:							Optional[Any]		 =						EulerAncestralDiscreteScheduler(
           beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				beta_schedule='scaled_linear'    )
       __lowerCamelCase		:							Union[str, Any]		 =						StableDiffusionPanoramaPipeline(**a    )
       __lowerCamelCase		:							Union[str, Any]		 =						sd_pipe.to(a    )
       sd_pipe.set_progress_bar_config(disable=a    )

       __lowerCamelCase		:							Union[str, Any]		 =						self.get_dummy_inputs(a    )
       __lowerCamelCase		:							Dict		 =						sd_pipe(**a    ).images
       __lowerCamelCase		:							Any		 =						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 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							Dict		 =						'cpu'  # ensure determinism for the device-dependent torch.Generator
       __lowerCamelCase		:							Optional[Any]		 =						self.get_dummy_components()
       __lowerCamelCase		:							Any		 =						PNDMScheduler(
           beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				beta_schedule='scaled_linear'						,				skip_prk_steps=a    )
       __lowerCamelCase		:							Any		 =						StableDiffusionPanoramaPipeline(**a    )
       __lowerCamelCase		:							Union[str, Any]		 =						sd_pipe.to(a    )
       sd_pipe.set_progress_bar_config(disable=a    )

       __lowerCamelCase		:							int		 =						self.get_dummy_inputs(a    )
       __lowerCamelCase		:							Optional[int]		 =						sd_pipe(**a    ).images
       __lowerCamelCase		:							List[str]		 =						image[0, -3:, -3:, -1]

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

       __lowerCamelCase		:							List[Any]		 =						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 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	Optional[int]    ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	int=0    ):
       __lowerCamelCase		:							int		 =						torch.manual_seed(a    )
       __lowerCamelCase		:							int		 =						{
           'prompt': 'a photo of the dolomites',
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs




      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							Tuple		 =						'stabilityai/stable-diffusion-2-base'
       __lowerCamelCase		:							Tuple		 =						DDIMScheduler.from_pretrained(a						,				subfolder='scheduler'    )
       __lowerCamelCase		:							Any		 =						StableDiffusionPanoramaPipeline.from_pretrained(a						,				scheduler=a						,				safety_checker=a    )
       pipe.to(a    )
       pipe.set_progress_bar_config(disable=a    )
       pipe.enable_attention_slicing()

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

       assert image.shape == (1, 512, 2048, 3)

       __lowerCamelCase		:							str		 =						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 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							int		 =						StableDiffusionPanoramaPipeline.from_pretrained(
           'stabilityai/stable-diffusion-2-base'						,				safety_checker=a    )
       __lowerCamelCase		:							int		 =						LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
       pipe.to(a    )
       pipe.set_progress_bar_config(disable=a    )
       pipe.enable_attention_slicing()

       __lowerCamelCase		:							Any		 =						self.get_inputs()
       __lowerCamelCase		:							Optional[int]		 =						pipe(**a    ).images
       __lowerCamelCase		:							Any		 =						image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 2048, 3)

       __lowerCamelCase		:							List[Any]		 =						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 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							List[Any]		 =						0

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

         __lowerCamelCase		:							List[str]		 =						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[str]		 =						latents.detach().cpu().numpy()
         assert latents.shape == (1, 4, 64, 256)
         __lowerCamelCase		:							Optional[Any]		 =						latents[0, -3:, -3:, -1]

         __lowerCamelCase		:							str		 =						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		:							Optional[int]		 =						False

       __lowerCamelCase		:							List[str]		 =						'stabilityai/stable-diffusion-2-base'
       __lowerCamelCase		:							str		 =						DDIMScheduler.from_pretrained(a						,				subfolder='scheduler'    )
       __lowerCamelCase		:							List[str]		 =						StableDiffusionPanoramaPipeline.from_pretrained(a						,				scheduler=a						,				safety_checker=a    )
       __lowerCamelCase		:							Any		 =						pipe.to(a    )
       pipe.set_progress_bar_config(disable=a    )
       pipe.enable_attention_slicing()

       __lowerCamelCase		:							List[str]		 =						self.get_inputs()
       pipe(**a						,				callback=a						,				callback_steps=1    )
       assert callback_fn.has_been_called
       assert number_of_steps == 3




      def 					_snake_case     (    self:	List[Any]    ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       __lowerCamelCase		:							str		 =						self.get_inputs()
       __lowerCamelCase		:							Any		 =						pipe(**a    )

       __lowerCamelCase		:							int		 =						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 pow
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	):
 if current_sum == needed_sum:
  # If the sum of the powers is equal to needed_sum, then we have a solution.
  solutions_count += 1
  return current_sum, solutions_count

 __lowerCamelCase		:							Optional[Any]		 =						int(pow(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)						)
 if current_sum + i_to_n <= needed_sum:
  # If the sum of the powers is less than needed_sum, then continue adding powers.
  current_sum += i_to_n
  __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						backtrack(
      SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	current_number + 1      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
  current_sum -= i_to_n
 if i_to_n < needed_sum:
  # If the power of i is less than needed_sum, then try with the next power.
  __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						backtrack(
      SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	current_number + 1      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 return current_sum, solutions_count
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 if not (1 <= needed_sum <= 1_000 and 2 <= power <= 10):
  raise ValueError(
      'Invalid input\n'
      'needed_sum must be between 1 and 1000, power between 2 and 10.'						)

 return backtrack(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	1      ,	0      ,	0						)[1]  # Return the solutions_count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

