



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 			_snake_case     (   lowercase__      ,		lowercase__      ,		unittest.TestCase):
    UpperCamelCase__			:   Optional[Any]       						=StableDiffusionPanoramaPipeline
    UpperCamelCase__			:   int       						=TEXT_TO_IMAGE_PARAMS
    UpperCamelCase__			:   List[str]       						=TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCamelCase__			:   Optional[Any]       						=TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCamelCase__			:   List[Any]       						=TEXT_TO_IMAGE_IMAGE_PARAMS




    def 							A__ (  self   :  Dict  ):
       torch.manual_seed(0  )
       lowercase__   					=			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__   					=			DDIMScheduler()
       torch.manual_seed(0  )
       lowercase__   					=			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__   					=			CLIPTextConfig(
           bos_token_id=0,   eos_token_id=2,   hidden_size=32,   intermediate_size=37,   layer_norm_eps=1e-0_5,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=1000,   )
       lowercase__   					=			CLIPTextModel(lowerCamelCase_  )
       lowercase__   					=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

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




    def 							A__ (  self   :  Tuple,   __lowercase   :  int,   __lowercase   :  Any=0  ):
       lowercase__   					=			torch.manual_seed(lowerCamelCase_  )
       lowercase__   					=			{
           "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   :  Any  ):
       lowercase__   					=			"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase__   					=			self.get_dummy_components()
       lowercase__   					=			StableDiffusionPanoramaPipeline(**lowerCamelCase_  )
       lowercase__   					=			sd_pipe.to(lowerCamelCase_  )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

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

       lowercase__   					=			np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]  )

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




    def 							A__ (  self   :  Optional[int]  ):
       super().test_inference_batch_consistent(batch_sizes=[1, 2]  )




    def 							A__ (  self   :  int  ):
       super().test_inference_batch_single_identical(batch_size=2,   expected_max_diff=3.2_5e-3  )




    def 							A__ (  self   :  List[str]  ):
       lowercase__   					=			"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase__   					=			self.get_dummy_components()
       lowercase__   					=			StableDiffusionPanoramaPipeline(**lowerCamelCase_  )
       lowercase__   					=			sd_pipe.to(lowerCamelCase_  )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

       lowercase__   					=			self.get_dummy_inputs(lowerCamelCase_  )
       lowercase__   					=			"french fries"
       lowercase__   					=			sd_pipe(**lowerCamelCase_,   negative_prompt=lowerCamelCase_  )
       lowercase__   					=			output.images
       lowercase__   					=			image[0, -3:, -3:, -1]

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

       lowercase__   					=			np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]  )

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




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

       lowercase__   					=			self.get_dummy_inputs(lowerCamelCase_  )
       lowercase__   					=			sd_pipe(**lowerCamelCase_,   view_batch_size=2  )
       lowercase__   					=			output.images
       lowercase__   					=			image[0, -3:, -3:, -1]

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

       lowercase__   					=			np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]  )

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




    def 							A__ (  self   :  List[str]  ):
       lowercase__   					=			"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase__   					=			self.get_dummy_components()
       lowercase__   					=			EulerAncestralDiscreteScheduler(
           beta_start=0.00085,   beta_end=0.012,   beta_schedule="scaled_linear"  )
       lowercase__   					=			StableDiffusionPanoramaPipeline(**lowerCamelCase_  )
       lowercase__   					=			sd_pipe.to(lowerCamelCase_  )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

       lowercase__   					=			self.get_dummy_inputs(lowerCamelCase_  )
       lowercase__   					=			sd_pipe(**lowerCamelCase_  ).images
       lowercase__   					=			image[0, -3:, -3:, -1]

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

       lowercase__   					=			np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]  )

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




    def 							A__ (  self   :  int  ):
       lowercase__   					=			"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase__   					=			self.get_dummy_components()
       lowercase__   					=			PNDMScheduler(
           beta_start=0.00085,   beta_end=0.012,   beta_schedule="scaled_linear",   skip_prk_steps=lowerCamelCase_  )
       lowercase__   					=			StableDiffusionPanoramaPipeline(**lowerCamelCase_  )
       lowercase__   					=			sd_pipe.to(lowerCamelCase_  )
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

       lowercase__   					=			self.get_dummy_inputs(lowerCamelCase_  )
       lowercase__   					=			sd_pipe(**lowerCamelCase_  ).images
       lowercase__   					=			image[0, -3:, -3:, -1]

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

       lowercase__   					=			np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
@slow
@require_torch_gpu
class 			_snake_case     (   unittest.TestCase):




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




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




    def 							A__ (  self   :  Any  ):
       lowercase__   					=			"stabilityai/stable-diffusion-2-base"
       lowercase__   					=			DDIMScheduler.from_pretrained(lowerCamelCase_,   subfolder="scheduler"  )
       lowercase__   					=			StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase_,   scheduler=lowerCamelCase_,   safety_checker=lowerCamelCase_  )
       pipe.to(lowerCamelCase_  )
       pipe.set_progress_bar_config(disable=lowerCamelCase_  )
       pipe.enable_attention_slicing()

       lowercase__   					=			self.get_inputs()
       lowercase__   					=			pipe(**lowerCamelCase_  ).images
       lowercase__   					=			image[0, -3:, -3:, -1].flatten()

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

       lowercase__   					=			np.array(
           [
               0.36968392,
               0.27025372,
               0.32446766,
               0.28379387,
               0.36363274,
               0.30733347,
               0.27100027,
               0.27054125,
               0.25536096,
           ]  )

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




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

       lowercase__   					=			self.get_inputs()
       lowercase__   					=			pipe(**lowerCamelCase_  ).images
       lowercase__   					=			image[0, -3:, -3:, -1].flatten()

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

       lowercase__   					=			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   :  List[Any]  ):
       lowercase__   					=			0

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

             lowercase__   					=			np.array(
                 [
                     0.18681869,
                     0.33907816,
                     0.5361276,
                     0.14432865,
                     -0.02856611,
                     -0.73941123,
                     0.23397987,
                     0.47322682,
                     -0.37823164,
                 ]  )
             assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5e-2
          elif step == 2:
             lowercase__   					=			latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 256)
             lowercase__   					=			latents[0, -3:, -3:, -1]

             lowercase__   					=			np.array(
                 [
                     0.18539645,
                     0.33987248,
                     0.5378559,
                     0.14437142,
                     -0.02455261,
                     -0.7338317,
                     0.23990755,
                     0.47356272,
                     -0.3786505,
                 ]  )

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

       lowercase__   					=			False

       lowercase__   					=			"stabilityai/stable-diffusion-2-base"
       lowercase__   					=			DDIMScheduler.from_pretrained(lowerCamelCase_,   subfolder="scheduler"  )
       lowercase__   					=			StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase_,   scheduler=lowerCamelCase_,   safety_checker=lowerCamelCase_  )
       lowercase__   					=			pipe.to(lowerCamelCase_  )
       pipe.set_progress_bar_config(disable=lowerCamelCase_  )
       pipe.enable_attention_slicing()

       lowercase__   					=			self.get_inputs()
       pipe(**lowerCamelCase_,   callback=lowerCamelCase_,   callback_steps=1  )
       assert callback_fn.has_been_called
       assert number_of_steps == 3




    def 							A__ (  self   :  Optional[int]  ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       lowercase__   					=			"stabilityai/stable-diffusion-2-base"
       lowercase__   					=			DDIMScheduler.from_pretrained(lowerCamelCase_,   subfolder="scheduler"  )
       lowercase__   					=			StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase_,   scheduler=lowerCamelCase_,   safety_checker=lowerCamelCase_  )
       lowercase__   					=			pipe.to(lowerCamelCase_  )
       pipe.set_progress_bar_config(disable=lowerCamelCase_  )
       pipe.enable_attention_slicing(1  )
       pipe.enable_sequential_cpu_offload()

       lowercase__   					=			self.get_inputs()
       lowercase__   					=			pipe(**lowerCamelCase_  )

       lowercase__   					=			torch.cuda.max_memory_allocated()
       # make sure that less than 5.2 GB is allocated
       assert mem_bytes < 5.5 * 10**9









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
			if decimal in (0, 1):  # Exit cases for the recursion
						return str(SCREAMING_SNAKE_CASE_					)
			lowercase__					,    lowercase__   					=			divmod(SCREAMING_SNAKE_CASE_  ,					2					)
			return binary_recursive(SCREAMING_SNAKE_CASE_					) + str(SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			str(SCREAMING_SNAKE_CASE_					).strip()
			if not number:
						raise ValueError("No input value was provided"					)
			lowercase__   					=			"-" if number.startswith("-"					) else ""
			lowercase__   					=			number.lstrip("-"					)
			if not number.isnumeric():
						raise ValueError("Input value is not an integer"					)
			return f'''{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_					)					)}'''


if __name__ == "__main__":
							from doctest import testmod

							testmod()




