



"""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    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     int		=		IFPipeline
   __lowerCAmelCase						:     Optional[Any]		=		TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
   __lowerCAmelCase						:     str		=		TEXT_TO_IMAGE_BATCH_PARAMS
   __lowerCAmelCase						:     str		=		PipelineTesterMixin.required_optional_params - {"latents"}

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    return self._get_dummy_components()

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=0		)						->   Any:





    '''simple docstring'''




    if str(lowerCamelCase__		).startswith("""mps"""		):
     UpperCAmelCase			:      Tuple			=			torch.manual_seed(lowerCamelCase__		)
    else:
     UpperCAmelCase			:      int			=			torch.Generator(device=lowerCamelCase__		).manual_seed(lowerCamelCase__		)

    UpperCAmelCase			:      Union[str, Any]			=			{
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "output_type": "numpy",
    }

    return inputs

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    self._test_save_load_optional_components()

   @unittest.skipIf(torch_device != """cuda"""   ,  reason="""float16 requires CUDA"""		)
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    super().test_save_load_floataa(expected_max_diff=1E-1		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    self._test_save_load_local()

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    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 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)



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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




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

    UpperCAmelCase			:      Tuple			=			IFSuperResolutionPipeline.from_pretrained(
        """DeepFloyd/IF-II-L-v1.0"""   ,  variant="""fp16"""   ,  torch_dtype=torch.floataa   ,  text_encoder=lowerCamelCase__   ,  tokenizer=lowerCamelCase__		)

    # pre compute text embeddings and remove T5 to save memory

    pipe_a.text_encoder.to("""cuda"""		)

    UpperCAmelCase			:      Tuple			=			pipe_a.encode_prompt("""anime turtle"""   ,  device="""cuda"""		)

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

    UpperCAmelCase			:      Any			=			None
    UpperCAmelCase			:      Optional[int]			=			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(lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__		)

    pipe_a.remove_all_hooks()
    pipe_a.remove_all_hooks()

    # img2img

    UpperCAmelCase			:      Union[str, Any]			=			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(lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__		)

    pipe_a.remove_all_hooks()
    pipe_a.remove_all_hooks()

    # inpainting

    UpperCAmelCase			:      Any			=			IFInpaintingPipeline(**pipe_a.components		)
    UpperCAmelCase			:      List[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(lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    _start_torch_memory_measurement()

    UpperCAmelCase			:      str			=			torch.Generator(device="""cpu"""		).manual_seed(0		)
    UpperCAmelCase			:      Union[str, Any]			=			pipe_a(
        prompt_embeds=lowerCamelCase__   ,  negative_prompt_embeds=lowerCamelCase__   ,  num_inference_steps=2   ,  generator=lowerCamelCase__   ,  output_type="""np"""   ,  )

    UpperCAmelCase			:      Dict			=			output.images[0]

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

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

    UpperCAmelCase			:      Dict			=			load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""		)
    assert_mean_pixel_difference(lowerCamelCase__   ,  lowerCamelCase__		)

    # pipeline 2

    _start_torch_memory_measurement()

    UpperCAmelCase			:      str			=			torch.Generator(device="""cpu"""		).manual_seed(0		)

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

    UpperCAmelCase			:      Any			=			pipe_a(
        prompt_embeds=lowerCamelCase__   ,  negative_prompt_embeds=lowerCamelCase__   ,  image=lowerCamelCase__   ,  generator=lowerCamelCase__   ,  num_inference_steps=2   ,  output_type="""np"""   ,  )

    UpperCAmelCase			:      Any			=			output.images[0]

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

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

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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    _start_torch_memory_measurement()

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

    UpperCAmelCase			:      List[Any]			=			torch.Generator(device="""cpu"""		).manual_seed(0		)

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

    UpperCAmelCase			:      Tuple			=			output.images[0]

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

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

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

    # pipeline 2

    _start_torch_memory_measurement()

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

    UpperCAmelCase			:      Tuple			=			floats_tensor((1, 3, 256, 256)   ,  rng=random.Random(0		)		).to(lowerCamelCase__		)
    UpperCAmelCase			:      Optional[Any]			=			floats_tensor((1, 3, 64, 64)   ,  rng=random.Random(0		)		).to(lowerCamelCase__		)

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

    UpperCAmelCase			:      List[Any]			=			output.images[0]

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

    UpperCAmelCase			:      Dict			=			torch.cuda.max_memory_allocated()
    assert mem_bytes < 4 * 10**9

    UpperCAmelCase			:      List[str]			=			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(lowerCamelCase__   ,  lowerCamelCase__		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[int]:





    '''simple docstring'''




    _start_torch_memory_measurement()

    UpperCAmelCase			:      Tuple			=			floats_tensor((1, 3, 64, 64)   ,  rng=random.Random(0		)		).to(lowerCamelCase__		)
    UpperCAmelCase			:      Dict			=			floats_tensor((1, 3, 64, 64)   ,  rng=random.Random(1		)		).to(lowerCamelCase__		)

    UpperCAmelCase			:      int			=			torch.Generator(device="""cpu"""		).manual_seed(0		)
    UpperCAmelCase			:      Any			=			pipe_a(
        prompt_embeds=lowerCamelCase__   ,  negative_prompt_embeds=lowerCamelCase__   ,  image=lowerCamelCase__   ,  mask_image=lowerCamelCase__   ,  num_inference_steps=2   ,  generator=lowerCamelCase__   ,  output_type="""np"""   ,  )

    UpperCAmelCase			:      Optional[int]			=			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_inpainting.npy"""		)
    assert_mean_pixel_difference(lowerCamelCase__   ,  lowerCamelCase__		)

    # pipeline 2

    _start_torch_memory_measurement()

    UpperCAmelCase			:      List[Any]			=			torch.Generator(device="""cpu"""		).manual_seed(0		)

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

    UpperCAmelCase			:      Tuple			=			pipe_a(
        prompt_embeds=lowerCamelCase__   ,  negative_prompt_embeds=lowerCamelCase__   ,  image=lowerCamelCase__   ,  mask_image=lowerCamelCase__   ,  original_image=lowerCamelCase__   ,  generator=lowerCamelCase__   ,  num_inference_steps=2   ,  output_type="""np"""   ,  )

    UpperCAmelCase			:      Any			=			output.images[0]

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

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

    UpperCAmelCase			:      List[str]			=			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(lowerCamelCase__   ,  lowerCamelCase__		)

def    _snake_case		(		):
 torch.cuda.empty_cache()
 torch.cuda.reset_max_memory_allocated()
 torch.cuda.reset_peak_memory_stats()





"""simple docstring"""
from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

A:     List[Any]										=	(3, 9, -1_1, 0, 7, 5, 1, -1)
A:     Union[str, Any]										=	(4, 6, 2, 0, 8, 1_0, 3, -2)



@dataclass
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     int
   __lowerCAmelCase						:     Node | None



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    UpperCAmelCase			:      Node | None			=			None
    for i in sorted(_SCREAMING_SNAKE_CASE   ,  reverse=_SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      Union[str, Any]			=			Node(_SCREAMING_SNAKE_CASE   ,  self.head		)

   def __iter__(     self		)						->   Iterator[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.head
    while node:
     yield node.data
     UpperCAmelCase			:      List[str]			=			node.next_node

   def __len__(     self		)						->   int:





    '''simple docstring'''




    return sum(1 for _ in self		)




   def __str__(     self		)						->   str:





    '''simple docstring'''




    return " -> ".join([str(_SCREAMING_SNAKE_CASE		) for node in self]		)

def    _snake_case		(		UpperCamelCase		:	SortedLinkedList    ,	UpperCamelCase		:	SortedLinkedList		):
 return SortedLinkedList(list(UpperCamelCase		) + list(UpperCamelCase		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      A:     Dict										=	SortedLinkedList
      print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
