
'''simple docstring'''







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







@skip_mps
class 		snake_case_       (						A__		,	A__		,	A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							int								=StableDiffusionModelEditingPipeline
   __lowerCAmelCase	:							List[Any]								=TEXT_TO_IMAGE_PARAMS
   __lowerCAmelCase	:							Optional[Any]								=TEXT_TO_IMAGE_BATCH_PARAMS
   __lowerCAmelCase	:							Dict								=TEXT_TO_IMAGE_IMAGE_PARAMS
   __lowerCAmelCase	:							Dict								=TEXT_TO_IMAGE_IMAGE_PARAMS







   def     __UpperCAmelCase      ( self):
          torch.manual_seed(0)
          lowerCamelCase__				   =      UNetaDConditionModel(
              block_out_channels=(32, 64)			,   layers_per_block=2			,   sample_size=32			,   in_channels=4			,   out_channels=4			,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,   cross_attention_dim=32			,   )
          lowerCamelCase__				   =      DDIMScheduler()
          torch.manual_seed(0)
          lowerCamelCase__				   =      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__				   =      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=10_00			,   )
          lowerCamelCase__				   =      CLIPTextModel(UpperCamelCase)
          lowerCamelCase__				   =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

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







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase=0):
          lowerCamelCase__				   =      torch.manual_seed(UpperCamelCase)
          lowerCamelCase__				   =      {
              "prompt": "A field of roses",
              "generator": generator,
              # Setting height and width to None to prevent OOMs on CPU.
              "height": None,
              "width": None,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }
          return inputs







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      "cpu"  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__				   =      self.get_dummy_components()
          lowerCamelCase__				   =      StableDiffusionModelEditingPipeline(**UpperCamelCase)
          lowerCamelCase__				   =      sd_pipe.to(UpperCamelCase)
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase)

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

          lowerCamelCase__				   =      np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9])

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      "cpu"  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__				   =      self.get_dummy_components()
          lowerCamelCase__				   =      StableDiffusionModelEditingPipeline(**UpperCamelCase)
          lowerCamelCase__				   =      sd_pipe.to(UpperCamelCase)
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase)

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

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

          lowerCamelCase__				   =      np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3])

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      "cpu"  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__				   =      self.get_dummy_components()
          lowerCamelCase__				   =      EulerAncestralDiscreteScheduler(
              beta_start=0.0_0_0_8_5			,   beta_end=0.0_1_2			,   beta_schedule="scaled_linear")
          lowerCamelCase__				   =      StableDiffusionModelEditingPipeline(**UpperCamelCase)
          lowerCamelCase__				   =      sd_pipe.to(UpperCamelCase)
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase)

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

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

          lowerCamelCase__				   =      np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7])

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      "cpu"  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__				   =      self.get_dummy_components()
          lowerCamelCase__				   =      PNDMScheduler()
          lowerCamelCase__				   =      StableDiffusionModelEditingPipeline(**UpperCamelCase)
          lowerCamelCase__				   =      sd_pipe.to(UpperCamelCase)
          sd_pipe.set_progress_bar_config(disable=UpperCamelCase)

          lowerCamelCase__				   =      self.get_dummy_inputs(UpperCamelCase)
          # the pipeline does not expect pndm so test if it raises error.
          with self.assertRaises(UpperCamelCase):
                 lowerCamelCase__				   =      sd_pipe(**UpperCamelCase).images







   def     __UpperCAmelCase      ( self):
          super().test_inference_batch_single_identical(expected_max_diff=5E-3)







   def     __UpperCAmelCase      ( self):
          super().test_attention_slicing_forward_pass(expected_max_diff=5E-3)







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



   """simple docstring"""





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







   def     __UpperCAmelCase      ( self			,   UpperCamelCase=0):
          lowerCamelCase__				   =      torch.manual_seed(UpperCamelCase)
          lowerCamelCase__				   =      {
              "prompt": "A field of roses",
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      "CompVis/stable-diffusion-v1-4"
          lowerCamelCase__				   =      StableDiffusionModelEditingPipeline.from_pretrained(UpperCamelCase			,   safety_checker=UpperCamelCase)
          pipe.to(UpperCamelCase)
          pipe.set_progress_bar_config(disable=UpperCamelCase)
          pipe.enable_attention_slicing()

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

          assert image.shape == (1, 5_12, 5_12, 3)

          lowerCamelCase__				   =      np.array(
              [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8])

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

          # make sure image changes after editing
          pipe.edit_model("A pack of roses"			,   "A pack of blue roses")

          lowerCamelCase__				   =      pipe(**UpperCamelCase).images
          lowerCamelCase__				   =      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 5_12, 5_12, 3)

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







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

          lowerCamelCase__				   =      "CompVis/stable-diffusion-v1-4"
          lowerCamelCase__				   =      DDIMScheduler.from_pretrained(UpperCamelCase			,   subfolder="scheduler")
          lowerCamelCase__				   =      StableDiffusionModelEditingPipeline.from_pretrained(
              UpperCamelCase			,   scheduler=UpperCamelCase			,   safety_checker=UpperCamelCase)
          lowerCamelCase__				   =      pipe.to(UpperCamelCase)
          pipe.set_progress_bar_config(disable=UpperCamelCase)
          pipe.enable_attention_slicing(1)
          pipe.enable_sequential_cpu_offload()

          lowerCamelCase__				   =      self.get_inputs()
          lowerCamelCase__				   =      pipe(**UpperCamelCase)

          lowerCamelCase__				   =      torch.cuda.max_memory_allocated()
          # make sure that less than 4.4 GB is allocated
          assert mem_bytes < 4.4 * 10**9



'''simple docstring'''







def   lowerCAmelCase(  a__    :  float      , a__    :  float      , a__    :  int      ):







       '''simple docstring'''






       lowerCamelCase__				   =      [1 / first_term]
       lowerCamelCase__				   =      1 / first_term
       for _ in range(number_of_terms - 1      ):
              first_term += common_difference
              arithmetic_progression.append(a__      )
       lowerCamelCase__				   =      [1 / step for step in arithmetic_progression]
       return sum(a__      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(sum_of_harmonic_progression(1 / 2, 2, 2))

