


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				):
 __lowercase			:							Tuple       					=			StableDiffusionPanoramaPipeline
 __lowercase			:							Any       					=			TEXT_TO_IMAGE_PARAMS
 __lowercase			:							List[Any]       					=			TEXT_TO_IMAGE_BATCH_PARAMS
 __lowercase			:							Dict       					=			TEXT_TO_IMAGE_IMAGE_PARAMS
 __lowercase			:							int       					=			TEXT_TO_IMAGE_IMAGE_PARAMS
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        torch.manual_seed(0			)
        lowercase__:       Optional[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    ,			)
        lowercase__:       Union[str, Any]        =       DDIMScheduler()
        torch.manual_seed(0			)
        lowercase__:       Optional[int]        =       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			)
        lowercase__:       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=1_000    ,			)
        lowercase__:       List[Any]        =       CLIPTextModel(lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

        lowercase__:       Tuple        =       {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=0			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       torch.manual_seed(lowerCAmelCase__			)
        lowercase__:       Any        =       {
            '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 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Tuple        =       'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowercase__:       Optional[Any]        =       self.get_dummy_components()
        lowercase__:       Tuple        =       StableDiffusionPanoramaPipeline(**lowerCAmelCase__			)
        lowercase__:       Tuple        =       sd_pipe.to(lowerCAmelCase__			)
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       Any        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       Tuple        =       sd_pipe(**lowerCAmelCase__			).images
        lowercase__:       List[Any]        =       image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        lowercase__:       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        super().test_inference_batch_consistent(batch_sizes=[1, 2]			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        super().test_inference_batch_single_identical(batch_size=2    ,			expected_max_diff=3.25E-3			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowercase__:       List[Any]        =       self.get_dummy_components()
        lowercase__:       int        =       StableDiffusionPanoramaPipeline(**lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       sd_pipe.to(lowerCAmelCase__			)
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       Any        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       'french fries'
        lowercase__:       Tuple        =       sd_pipe(**lowerCAmelCase__    ,			negative_prompt=lowerCAmelCase__			)
        lowercase__:       Any        =       output.images
        lowercase__:       Any        =       image[0, -3:, -3:, -1]

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

        lowercase__:       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Tuple        =       'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowercase__:       Any        =       self.get_dummy_components()
        lowercase__:       int        =       StableDiffusionPanoramaPipeline(**lowerCAmelCase__			)
        lowercase__:       Tuple        =       sd_pipe.to(lowerCAmelCase__			)
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

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

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

        lowercase__:       List[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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[Any]        =       'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowercase__:       Union[str, Any]        =       self.get_dummy_components()
        lowercase__:       Tuple        =       EulerAncestralDiscreteScheduler(
            beta_start=0.0_0_0_8_5    ,			beta_end=0.0_1_2    ,			beta_schedule='scaled_linear'			)
        lowercase__:       List[str]        =       StableDiffusionPanoramaPipeline(**lowerCAmelCase__			)
        lowercase__:       int        =       sd_pipe.to(lowerCAmelCase__			)
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

        lowercase__:       List[str]        =       self.get_dummy_inputs(lowerCAmelCase__			)
        lowercase__:       Dict        =       sd_pipe(**lowerCAmelCase__			).images
        lowercase__:       Tuple        =       image[0, -3:, -3:, -1]

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

        lowercase__:       List[str]        =       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowercase__:       List[str]        =       self.get_dummy_components()
        lowercase__:       List[str]        =       PNDMScheduler(
            beta_start=0.0_0_0_8_5    ,			beta_end=0.0_1_2    ,			beta_schedule='scaled_linear'    ,			skip_prk_steps=lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       StableDiffusionPanoramaPipeline(**lowerCAmelCase__			)
        lowercase__:       Optional[Any]        =       sd_pipe.to(lowerCAmelCase__			)
        sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

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

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

        lowercase__:       Optional[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				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__=0			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       torch.manual_seed(lowerCAmelCase__			)
        lowercase__:       Dict        =       {
            'prompt': 'a photo of the dolomites',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       List[Any]        =       'stabilityai/stable-diffusion-2-base'
        lowercase__:       Any        =       DDIMScheduler.from_pretrained(lowerCAmelCase__    ,			subfolder='scheduler'			)
        lowercase__:       Optional[int]        =       StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__    ,			scheduler=lowerCAmelCase__    ,			safety_checker=lowerCAmelCase__			)
        pipe.to(lowerCAmelCase__			)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
        pipe.enable_attention_slicing()

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

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

        lowercase__:       int        =       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       List[str]        =       StableDiffusionPanoramaPipeline.from_pretrained(
            'stabilityai/stable-diffusion-2-base'    ,			safety_checker=lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
        pipe.to(lowerCAmelCase__			)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
        pipe.enable_attention_slicing()

        lowercase__:       Dict        =       self.get_inputs()
        lowercase__:       Tuple        =       pipe(**lowerCAmelCase__			).images
        lowercase__:       List[str]        =       image[0, -3:, -3:, -1].flatten()

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

        lowercase__:       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       0

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

                      lowercase__:       Tuple        =       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:
                      lowercase__:       Tuple        =       latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 256)
                      lowercase__:       str        =       latents[0, -3:, -3:, -1]

                      lowercase__:       int        =       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

        lowercase__:       int        =       False

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

        lowercase__:       Any        =       self.get_inputs()
        pipe(**lowerCAmelCase__    ,			callback=lowerCAmelCase__    ,			callback_steps=1			)
        assert callback_fn.has_been_called
        assert number_of_steps == 3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

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

        lowercase__:       List[str]        =       self.get_inputs()
        lowercase__:       Dict        =       pipe(**lowerCAmelCase__			)

        lowercase__:       Optional[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        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							)  ->    tuple[int, int]:
       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

       lowercase__:       int        =       int(pow(snake_case      ,							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
              lowercase__      ,						lowercase__:       Any        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							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.
              lowercase__      ,						lowercase__:       Optional[int]        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							snake_case					)
       return current_sum, solutions_count




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
              raise ValueError(
                  'Invalid input\n'
                  'needed_sum must be between 1 and 1000, power between 2 and 10.'					)

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()


