import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  __magic_name__  (		__a      ,	__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   StableDiffusionInpaintPipeline
   lowerCAmelCase      :    Optional[Any]  			=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCAmelCase      :    str  			=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCAmelCase      :    str  			=   frozenset(
       []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   lowerCAmelCase      :    Tuple  			=   frozenset([]    )



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= UNetaDConditionModel(
        block_out_channels=(32, 64)		,  layers_per_block=2		,  sample_size=32		,  in_channels=9		,  out_channels=4		,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,  cross_attention_dim=32		,  attention_head_dim=(2, 4)		,  use_linear_projection=_lowercase		,  )
    _UpperCamelCase:     List[str]							= PNDMScheduler(skip_prk_steps=_lowercase							)
    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= 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		,  sample_size=128		,  )
    torch.manual_seed(0							)
    _UpperCamelCase:     Optional[int]							= CLIPTextConfig(
        bos_token_id=0		,  eos_token_id=2		,  hidden_size=32		,  intermediate_size=37		,  layer_norm_eps=1E-05		,  num_attention_heads=4		,  num_hidden_layers=5		,  pad_token_id=1		,  vocab_size=1_000		,  hidden_act='''gelu'''		,  projection_dim=512		,  )
    _UpperCamelCase:     Optional[int]							= CLIPTextModel(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

    _UpperCamelCase:     Optional[Any]							= {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Any		,  _lowercase					:  int=0							):



    """simple docstring"""



    _UpperCamelCase:     Any							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= image.cpu().permute(0		,  2		,  3		,  1							)[0]
    _UpperCamelCase:     Optional[int]							= Image.fromarray(np.uinta(_lowercase							)							).convert('''RGB'''							).resize((64, 64)							)
    _UpperCamelCase:     Optional[Any]							= Image.fromarray(np.uinta(image + 4							)							).convert('''RGB'''							).resize((64, 64)							)
    if str(_lowercase							).startswith('''mps'''							):
     _UpperCamelCase:     Dict							= torch.manual_seed(_lowercase							)
    else:
     _UpperCamelCase:     Union[str, Any]							= torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
    _UpperCamelCase:     List[str]							= {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''image''': init_image,
        '''mask_image''': mask_image,
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    _UpperCamelCase:     str							= self.get_dummy_components()
    _UpperCamelCase:     Any							= StableDiffusionInpaintPipeline(**_lowercase							)
    _UpperCamelCase:     Any							= sd_pipe.to(_lowercase							)
    sd_pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Tuple							= self.get_dummy_inputs(_lowercase							)
    _UpperCamelCase:     List[Any]							= sd_pipe(**_lowercase							).images
    _UpperCamelCase:     Optional[Any]							= image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    _UpperCamelCase:     List[str]							= np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

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



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    super().test_inference_batch_single_identical(expected_max_diff=3E-3							)




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





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Tuple							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     Dict							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Optional[Any]							= StableDiffusionInpaintPipeline.from_pretrained(_lowercase		,  safety_checker=_lowercase							)
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     List[str]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     List[Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     str							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 9E-3



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     str							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     int							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  torch_dtype=torch.floataa		,  safety_checker=_lowercase		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     Optional[Any]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Dict							= torch.manual_seed(0							)
    _UpperCamelCase:     int							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 5E-1



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)

    _UpperCamelCase:     Optional[int]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Union[str, Any]							= PNDMScheduler.from_pretrained(_lowercase		,  subfolder='''scheduler'''							)
    _UpperCamelCase:     Optional[int]							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  safety_checker=_lowercase		,  scheduler=_lowercase		,  torch_dtype=torch.floataa		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing(1							)
    pipe.enable_sequential_cpu_offload()

    _UpperCamelCase:     Optional[int]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Union[str, Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     Union[str, Any]							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  num_inference_steps=2		,  output_type='''np'''		,  )

    _UpperCamelCase:     Dict							= torch.cuda.max_memory_allocated()
    # make sure that less than 2.65 GB is allocated
    assert mem_bytes < 2.65 * 10**9
from __future__ import annotations




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  Dict=None							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= data
    _UpperCamelCase:     int							= None



   def __repr__(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     int							= []
    _UpperCamelCase:     str							= self
    while temp:
     string_rep.append(f"""{temp.data}"""							)
     _UpperCamelCase:     Optional[Any]							= temp.next
    return "->".join(_lowercase							)




def       lowerCAmelCase_     (		lowercase:							list		)    ->				Optional[int]:







 '''simple docstring'''
 if not elements_list:
  raise Exception('''The Elements List is empty'''		)

 _UpperCamelCase:     Optional[Any]							= Node(elements_list[0]		)
 for i in range(1  ,					len(lowercase		)		):
  _UpperCamelCase:     Union[str, Any]							= Node(elements_list[i]		)
  _UpperCamelCase:     Optional[int]							= current.next
 return head




def       lowerCAmelCase_     (		lowercase:							Node		)    ->				None:







 '''simple docstring'''
 if head_node is not None and isinstance(lowercase  ,					lowercase		):
  print_reverse(head_node.next		)
  print(head_node.data		)




def       lowerCAmelCase_     (		)    ->				Tuple:







 '''simple docstring'''
 from doctest import testmod

 testmod()

 _UpperCamelCase:     Any							= make_linked_list([14, 52, 14, 12, 43]		)
 print('''Linked List:'''		)
 print(lowercase		)
 print('''Elements in Reverse:'''		)
 print_reverse(lowercase		)


if __name__ == "__main__":
  main()