



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     lowerCAmelCase_		(  a__	,   a__	,   unittest.TestCase							):
       UpperCAmelCase__    : List[Any]       					=  IFPipeline
       UpperCAmelCase__    : List[Any]       					=  TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
       UpperCAmelCase__    : Optional[Any]       					=  TEXT_TO_IMAGE_BATCH_PARAMS
       UpperCAmelCase__    : Union[str, Any]       					=  PipelineTesterMixin.required_optional_params - {"latents"}
       def  snake_case_	(      self	)					->		str:
          return self._get_dummy_components()
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=0	)					->		Any:
          if str(SCREAMING_SNAKE_CASE_	).startswith('mps'	):
             UpperCamelCase   :  str						     =     torch.manual_seed(SCREAMING_SNAKE_CASE_	)
          else:
             UpperCamelCase   :  Union[str, Any]						     =     torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[Any]						     =     {
              'prompt': 'A painting of a squirrel eating a burger',
              'generator': generator,
              'num_inference_steps': 2,
              'output_type': 'numpy',
          }

          return inputs
       def  snake_case_	(      self	)					->		Dict:
          self._test_save_load_optional_components()
       @unittest.skipIf(torch_device != 'cuda',      reason='float16 requires CUDA'	)
       def  snake_case_	(      self	)					->		List[str]:
          # 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  snake_case_	(      self	)					->		str:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
       def  snake_case_	(      self	)					->		Optional[int]:
          self._test_save_load_local()
       def  snake_case_	(      self	)					->		Any:
          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  snake_case_	(      self	)					->		Dict:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)






@slow
@require_torch_gpu
class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self	)					->		str:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
       def  snake_case_	(      self	)					->		int:
          # if

          UpperCamelCase   :  Optional[Any]						     =     IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0',      variant='fp16',      torch_dtype=torch.floataa	)

          UpperCamelCase   :  List[Any]						     =     IFSuperResolutionPipeline.from_pretrained(
              'DeepFloyd/IF-II-L-v1.0',      variant='fp16',      torch_dtype=torch.floataa,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_	)

          # pre compute text embeddings and remove T5 to save memory

          pipe_a.text_encoder.to('cuda'	)

          UpperCamelCase     ,					UpperCamelCase   :  List[Any]						     =     pipe_a.encode_prompt('anime turtle',      device='cuda'	)

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

          UpperCamelCase   :  Union[str, Any]						     =     None
          UpperCamelCase   :  List[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(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # img2img

          UpperCamelCase   :  Optional[int]						     =     IFImgaImgPipeline(**pipe_a.components	)
          UpperCamelCase   :  str						     =     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(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # inpainting

          UpperCamelCase   :  str						     =     IFInpaintingPipeline(**pipe_a.components	)
          UpperCamelCase   :  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(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          # pipeline 1

          _start_torch_memory_measurement()

          UpperCamelCase   :  Union[str, Any]						     =     torch.Generator(device='cpu'	).manual_seed(0	)
          UpperCamelCase   :  Tuple						     =     pipe_a(
              prompt_embeds=SCREAMING_SNAKE_CASE_,      negative_prompt_embeds=SCREAMING_SNAKE_CASE_,      num_inference_steps=2,      generator=SCREAMING_SNAKE_CASE_,      output_type='np',      )

          UpperCamelCase   :  str						     =     output.images[0]

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

          UpperCamelCase   :  Dict						     =     torch.cuda.max_memory_allocated()
          assert mem_bytes < 13 * 10**9

          UpperCamelCase   :  List[str]						     =     load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'	)
          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          # pipeline 2

          _start_torch_memory_measurement()

          UpperCamelCase   :  Optional[Any]						     =     torch.Generator(device='cpu'	).manual_seed(0	)

          UpperCamelCase   :  List[Any]						     =     floats_tensor((1, 3, 64, 64),      rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     pipe_a(
              prompt_embeds=SCREAMING_SNAKE_CASE_,      negative_prompt_embeds=SCREAMING_SNAKE_CASE_,      image=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      num_inference_steps=2,      output_type='np',      )

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

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

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

          UpperCamelCase   :  Optional[Any]						     =     load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'	)
          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          # pipeline 1

          _start_torch_memory_measurement()

          UpperCamelCase   :  Optional[Any]						     =     floats_tensor((1, 3, 64, 64),      rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     torch.Generator(device='cpu'	).manual_seed(0	)

          UpperCamelCase   :  List[str]						     =     pipe_a(
              prompt_embeds=SCREAMING_SNAKE_CASE_,      negative_prompt_embeds=SCREAMING_SNAKE_CASE_,      image=SCREAMING_SNAKE_CASE_,      num_inference_steps=2,      generator=SCREAMING_SNAKE_CASE_,      output_type='np',      )

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

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

          UpperCamelCase   :  str						     =     torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          UpperCamelCase   :  str						     =     load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'	)
          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          # pipeline 2

          _start_torch_memory_measurement()

          UpperCamelCase   :  int						     =     torch.Generator(device='cpu'	).manual_seed(0	)

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

          UpperCamelCase   :  List[str]						     =     pipe_a(
              prompt_embeds=SCREAMING_SNAKE_CASE_,      negative_prompt_embeds=SCREAMING_SNAKE_CASE_,      image=SCREAMING_SNAKE_CASE_,      original_image=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      num_inference_steps=2,      output_type='np',      )

          UpperCamelCase   :  Any						     =     output.images[0]

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

          UpperCamelCase   :  Optional[Any]						     =     torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          UpperCamelCase   :  Optional[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(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          # pipeline 1

          _start_torch_memory_measurement()

          UpperCamelCase   :  List[str]						     =     floats_tensor((1, 3, 64, 64),      rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     floats_tensor((1, 3, 64, 64),      rng=random.Random(1	)	).to(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     torch.Generator(device='cpu'	).manual_seed(0	)
          UpperCamelCase   :  Dict						     =     pipe_a(
              prompt_embeds=SCREAMING_SNAKE_CASE_,      negative_prompt_embeds=SCREAMING_SNAKE_CASE_,      image=SCREAMING_SNAKE_CASE_,      mask_image=SCREAMING_SNAKE_CASE_,      num_inference_steps=2,      generator=SCREAMING_SNAKE_CASE_,      output_type='np',      )

          UpperCamelCase   :  Any						     =     output.images[0]

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

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

          UpperCamelCase   :  Optional[Any]						     =     load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'	)
          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          # pipeline 2

          _start_torch_memory_measurement()

          UpperCamelCase   :  int						     =     torch.Generator(device='cpu'	).manual_seed(0	)

          UpperCamelCase   :  int						     =     floats_tensor((1, 3, 64, 64),      rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[str]						     =     floats_tensor((1, 3, 256, 256),      rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     floats_tensor((1, 3, 256, 256),      rng=random.Random(1	)	).to(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Tuple						     =     pipe_a(
              prompt_embeds=SCREAMING_SNAKE_CASE_,      negative_prompt_embeds=SCREAMING_SNAKE_CASE_,      image=SCREAMING_SNAKE_CASE_,      mask_image=SCREAMING_SNAKE_CASE_,      original_image=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      num_inference_steps=2,      output_type='np',      )

          UpperCamelCase   :  Tuple						     =     output.images[0]

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

          UpperCamelCase   :  Optional[Any]						     =     torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          UpperCamelCase   :  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(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)






def 		UpperCamelCase     (       )     ->					Union[str, Any]:
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()








from graphs.minimum_spanning_tree_kruskal import kruskal
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  List[str]						     =     9
   UpperCamelCase   :  Optional[Any]						     =     [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   UpperCamelCase   :  int						     =     kruskal(snake_case__	,					snake_case__     )

   UpperCamelCase   :  List[str]						     =     [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(snake_case__     ) == sorted(snake_case__     )



