





def      A__       (    SCREAMING_SNAKE_CASE_   :      list						)  ->  int:






   """simple docstring"""







   if not grid or not grid[0]:
      raise TypeError('''The grid does not contain the appropriate information'''						)

   for cell_n in range(1				,	len(grid[0]						)						):
      grid[0][cell_n] += grid[0][cell_n - 1]
   _UpperCAmelCase           =   grid[0]

   for row_n in range(1				,	len(SCREAMING_SNAKE_CASE_						)						):
      _UpperCAmelCase           =   grid[row_n]
      _UpperCAmelCase           =   fill_row(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   grid[row_n]

   return grid[-1][-1]
def      A__       (    SCREAMING_SNAKE_CASE_   :      list				,	SCREAMING_SNAKE_CASE_   :      list						)  ->  list:






   """simple docstring"""







   current_row[0] += row_above[0]
   for cell_n in range(1				,	len(SCREAMING_SNAKE_CASE_						)						):
      current_row[cell_n] += min(current_row[cell_n - 1]				,	row_above[cell_n]						)

   return current_row


if __name__ == "__main__":
   import doctest

   doctest.testmod()

"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin

@skip_mps
class 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							IFPipeline
   __lowerCAmelCase				=							TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
   __lowerCAmelCase				=							TEXT_TO_IMAGE_BATCH_PARAMS
   __lowerCAmelCase				=							PipelineTesterMixin.required_optional_params - {"""latents"""}
   def      __magic_name__     (     self							):
          return self._get_dummy_components()
   def      __magic_name__     (     self      ,       _a      ,       _a=0							):
          if str(_a							).startswith("mps"							):
                 lowercase       :							List[str]									=				torch.manual_seed(_a							)
          else:
                 lowercase       :							Dict									=				torch.Generator(device=_a							).manual_seed(_a							)

          lowercase       :							str									=				{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs
   def      __magic_name__     (     self							):
          self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != "cuda"      ,       reason="float16 requires CUDA"							)
   def      __magic_name__     (     self							):
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1E-1							)
   def      __magic_name__     (     self							):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)
   def      __magic_name__     (     self							):
          self._test_save_load_local()
   def      __magic_name__     (     self							):
          self._test_inference_batch_single_identical(
              expected_max_diff=1E-2      ,       )

   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()      ,       reason="XFormers attention is only available with CUDA and `xformers` installed"      ,       )
   def      __magic_name__     (     self							):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)


@slow
@require_torch_gpu
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self							):
          # if

          lowercase       :							Tuple									=				IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"      ,       variant="fp16"      ,       torch_dtype=torch.floataa							)

          lowercase       :							List[str]									=				IFSuperResolutionPipeline.from_pretrained(
              "DeepFloyd/IF-II-L-v1.0"      ,       variant="fp16"      ,       torch_dtype=torch.floataa      ,       text_encoder=_a      ,       tokenizer=_a							)

          # pre compute text embeddings and remove T5 to save memory

          pipe_a.text_encoder.to("cuda"							)

          lowercase						,       lowercase       :							int									=				pipe_a.encode_prompt("anime turtle"      ,       device="cuda"							)

          del pipe_a.tokenizer
          del pipe_a.text_encoder
          gc.collect()

          lowercase       :							List[str]									=				None
          lowercase       :							Union[str, Any]									=				None

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

          self._test_if(_a      ,       _a      ,       _a      ,       _a							)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # img2img

          lowercase       :							Optional[int]									=				IFImgaImgPipeline(**pipe_a.components							)
          lowercase       :							Dict									=				IFImgaImgSuperResolutionPipeline(**pipe_a.components							)

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

          self._test_if_imgaimg(_a      ,       _a      ,       _a      ,       _a							)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # inpainting

          lowercase       :							List[str]									=				IFInpaintingPipeline(**pipe_a.components							)
          lowercase       :							Optional[Any]									=				IFInpaintingSuperResolutionPipeline(**pipe_a.components							)

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

          self._test_if_inpainting(_a      ,       _a      ,       _a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Optional[Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)
          lowercase       :							Optional[Any]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

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

          lowercase       :							Optional[Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 13 * 10**9

          lowercase       :							List[Any]									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							List[str]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Union[str, Any]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

          lowercase       :							Optional[int]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

          assert image.shape == (256, 256, 3)

          lowercase       :							Union[str, Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							Tuple									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Optional[int]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Union[str, Any]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Optional[Any]									=				output.images[0]

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

          lowercase       :							Optional[Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          lowercase       :							Union[str, Any]									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							Dict									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Dict									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							str									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       original_image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							int									=				output.images[0]

          assert image.shape == (256, 256, 3)

          lowercase       :							List[str]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							int									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Dict									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							Dict									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(1							)							).to(_a							)

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)
          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       mask_image=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Dict									=				output.images[0]

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

          lowercase       :							int									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          lowercase       :							str									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Optional[Any]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							Union[str, Any]									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							str									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(1							)							).to(_a							)

          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       mask_image=_a      ,       original_image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

          assert image.shape == (256, 256, 3)

          lowercase       :							Tuple									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							Tuple									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)


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


