


"""simple docstring"""
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__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					StableDiffusionPanoramaPipeline
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_BATCH_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_IMAGE_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_IMAGE_PARAMS





   def 		UpperCamelCase_  (							self)  ->  str:
      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	:					Optional[Any]   =		DDIMScheduler()
      torch.manual_seed(0)
      _lowerCamelCase	:					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)
      _lowerCamelCase	:					Dict   =		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      ,   )
      _lowerCamelCase	:					Any   =		CLIPTextModel(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

      _lowerCamelCase	:					List[str]   =		{
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=0)  ->  Tuple:
      _lowerCamelCase	:					Optional[int]   =		torch.manual_seed(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		{
          """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 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					str   =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      _lowerCamelCase	:					Optional[int]   =		self.get_dummy_components()
      _lowerCamelCase	:					Dict   =		StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		sd_pipe.to(SCREAMING_SNAKE_CASE)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		sd_pipe(**SCREAMING_SNAKE_CASE).images
      _lowerCamelCase	:					Optional[int]   =		image[0, -3:, -3:, -1]
      assert image.shape == (1, 64, 64, 3)

      _lowerCamelCase	:					Any   =		np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57])

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





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      super().test_inference_batch_consistent(batch_sizes=[1, 2])





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





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					List[Any]   =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      _lowerCamelCase	:					int   =		self.get_dummy_components()
      _lowerCamelCase	:					Union[str, Any]   =		StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		sd_pipe.to(SCREAMING_SNAKE_CASE)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

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

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

      _lowerCamelCase	:					Optional[int]   =		np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57])

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





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					int   =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      _lowerCamelCase	:					Any   =		self.get_dummy_components()
      _lowerCamelCase	:					Tuple   =		StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		sd_pipe.to(SCREAMING_SNAKE_CASE)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

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

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

      _lowerCamelCase	:					List[str]   =		np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57])

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





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					str   =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      _lowerCamelCase	:					str   =		self.get_dummy_components()
      _lowerCamelCase	:					Optional[Any]   =		EulerAncestralDiscreteScheduler(
          beta_start=0.0_00_85      ,   beta_end=0.0_12      ,   beta_schedule="""scaled_linear""")
      _lowerCamelCase	:					str   =		StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		sd_pipe.to(SCREAMING_SNAKE_CASE)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

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

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

      _lowerCamelCase	:					Dict   =		np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52])

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





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					int   =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      _lowerCamelCase	:					Tuple   =		self.get_dummy_components()
      _lowerCamelCase	:					Any   =		PNDMScheduler(
          beta_start=0.0_00_85      ,   beta_end=0.0_12      ,   beta_schedule="""scaled_linear"""      ,   skip_prk_steps=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		StableDiffusionPanoramaPipeline(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		sd_pipe.to(SCREAMING_SNAKE_CASE)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		sd_pipe(**SCREAMING_SNAKE_CASE).images
      _lowerCamelCase	:					Optional[int]   =		image[0, -3:, -3:, -1]

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

      _lowerCamelCase	:					Union[str, Any]   =		np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39])

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







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





   def 		UpperCamelCase_  (							self)  ->  Dict:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE=0)  ->  str:
      _lowerCamelCase	:					List[str]   =		torch.manual_seed(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		{
          """prompt""": """a photo of the dolomites""",
          """generator""": generator,
          """num_inference_steps""": 3,
          """guidance_scale""": 7.5,
          """output_type""": """numpy""",
      }
      return inputs





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Tuple   =		"""stabilityai/stable-diffusion-2-base"""
      _lowerCamelCase	:					Optional[Any]   =		DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE      ,   subfolder="""scheduler""")
      _lowerCamelCase	:					Union[str, Any]   =		StableDiffusionPanoramaPipeline.from_pretrained(SCREAMING_SNAKE_CASE      ,   scheduler=SCREAMING_SNAKE_CASE      ,   safety_checker=SCREAMING_SNAKE_CASE)
      pipe.to(SCREAMING_SNAKE_CASE)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
      pipe.enable_attention_slicing()

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

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

      _lowerCamelCase	:					Union[str, Any]   =		np.array(
          [
              0.36_96_83_92,
              0.27_02_53_72,
              0.32_44_67_66,
              0.28_37_93_87,
              0.36_36_32_74,
              0.30_73_33_47,
              0.27_10_00_27,
              0.27_05_41_25,
              0.25_53_60_96,
          ])

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





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					int   =		StableDiffusionPanoramaPipeline.from_pretrained(
          """stabilityai/stable-diffusion-2-base"""      ,   safety_checker=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		LMSDiscreteScheduler.from_config(pipe.scheduler.config)
      pipe.to(SCREAMING_SNAKE_CASE)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
      pipe.enable_attention_slicing()

      _lowerCamelCase	:					Optional[int]   =		self.get_inputs()
      _lowerCamelCase	:					str   =		pipe(**SCREAMING_SNAKE_CASE).images
      _lowerCamelCase	:					int   =		image[0, -3:, -3:, -1].flatten()

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

      _lowerCamelCase	:					int   =		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 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					List[str]   =		0

      def callback_fn(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE) -> None:
         _lowerCamelCase	:					str   =		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	:					Dict   =		latents[0, -3:, -3:, -1]

            _lowerCamelCase	:					Tuple   =		np.array(
                [
                    0.18_68_18_69,
                    0.33_90_78_16,
                    0.5_36_12_76,
                    0.14_43_28_65,
                    -0.02_85_66_11,
                    -0.73_94_11_23,
                    0.23_39_79_87,
                    0.47_32_26_82,
                    -0.37_82_31_64,
                ])
            assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
         elif step == 2:
            _lowerCamelCase	:					int   =		latents.detach().cpu().numpy()
            assert latents.shape == (1, 4, 64, 256)
            _lowerCamelCase	:					int   =		latents[0, -3:, -3:, -1]

            _lowerCamelCase	:					Tuple   =		np.array(
                [
                    0.18_53_96_45,
                    0.33_98_72_48,
                    0.5_37_85_59,
                    0.14_43_71_42,
                    -0.02_45_52_61,
                    -0.7_33_83_17,
                    0.23_99_07_55,
                    0.47_35_62_72,
                    -0.3_78_65_05,
                ])

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

      _lowerCamelCase	:					Any   =		False

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

      _lowerCamelCase	:					Dict   =		self.get_inputs()
      pipe(**SCREAMING_SNAKE_CASE      ,   callback=SCREAMING_SNAKE_CASE      ,   callback_steps=1)
      assert callback_fn.has_been_called
      assert number_of_steps == 3





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

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

      _lowerCamelCase	:					int   =		self.get_inputs()
      _lowerCamelCase	:					int   =		pipe(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		torch.cuda.max_memory_allocated()
      # make sure that less than 5.2 GB is allocated
      assert mem_bytes < 5.5 * 10**9





"""simple docstring"""
def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		len(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		len(__snake_case   )
   _lowerCamelCase	:					int   =		[[False for _ in range(m + 1   )] for _ in range(n + 1   )]
   _lowerCamelCase	:					Union[str, Any]   =		True
   for i in range(__snake_case   ):
      for j in range(m + 1   ):
         if dp[i][j]:
            if j < m and a[i].upper() == b[j]:
               _lowerCamelCase	:					Tuple   =		True
            if a[i].islower():
               _lowerCamelCase	:					Tuple   =		True
   return dp[n][m]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

