




'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class   _snake_case( UpperCAmelCase					,   unittest.TestCase			):
 __snake_case:       List[str]									=				RePaintPipeline
 __snake_case:       Tuple									=				IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
 __snake_case:       Optional[Any]									=				PipelineTesterMixin.required_optional_params - {
     '''latents''',
     '''num_images_per_prompt''',
     '''callback''',
     '''callback_steps''',
 }
 __snake_case:       List[str]									=				IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case:       int									=				False






 def 	_UpperCamelCase   (self					:     List[str]						)					->      List[Any]:






    """simple docstring"""


    torch.manual_seed(0						)
    torch.manual_seed(0						)
    A__         =    UNetaDModel(
        block_out_channels=(32, 64)						,		layers_per_block=2						,		sample_size=32						,		in_channels=3						,		out_channels=3						,		down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,		up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,		)
    A__         =    RePaintScheduler()
    A__         =    {'unet': unet, 'scheduler': scheduler}
    return components






 def 	_UpperCamelCase   (self					:     List[str]						,		a					:     Tuple						,		a					:     str=0						)					->      Optional[Any]:






    """simple docstring"""


    if str(a						).startswith('mps'						):
       A__         =    torch.manual_seed(a						)
    else:
       A__         =    torch.Generator(device=a						).manual_seed(a						)
    A__         =    np.random.RandomState(a						).standard_normal((1, 3, 32, 32)						)
    A__         =    torch.from_numpy(a						).to(device=a						,		dtype=torch.floataa						)
    A__         =    (image > 0).to(device=a						,		dtype=torch.floataa						)
    A__         =    {
        'image': image,
        'mask_image': mask,
        'generator': generator,
        'num_inference_steps': 5,
        'eta': 0.0,
        'jump_length': 2,
        'jump_n_sample': 2,
        'output_type': 'numpy',
    }
    return inputs






 def 	_UpperCamelCase   (self					:     List[str]						)					->      Any:






    """simple docstring"""


    A__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
    A__         =    self.get_dummy_components()
    A__         =    RePaintPipeline(**a						)
    A__         =    sd_pipe.to(a						)
    sd_pipe.set_progress_bar_config(disable=a						)

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

    assert image.shape == (1, 32, 32, 3)
    A__         =    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]						)

    assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3






 @skip_mps
 def 	_UpperCamelCase   (self					:     str						)					->      str:






    """simple docstring"""


    return super().test_save_load_local()






 @unittest.skip('non-deterministic pipeline'						)
 def 	_UpperCamelCase   (self					:     List[str]						)					->      List[Any]:






    """simple docstring"""


    return super().test_inference_batch_single_identical()






 @skip_mps
 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      List[str]:






    """simple docstring"""


    return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      List[Any]:






    """simple docstring"""


    return super().test_save_load_optional_components()






 @skip_mps
 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Any:






    """simple docstring"""


    return super().test_attention_slicing_forward_pass()




@nightly
@require_torch_gpu
class   _snake_case( unittest.TestCase			):






 def 	_UpperCamelCase   (self					:     List[str]						)					->      Optional[int]:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     Any						)					->      Any:






    """simple docstring"""


    A__         =    load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
        'repaint/celeba_hq_256.png'						)
    A__         =    load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'						)
    A__         =    load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
        'repaint/celeba_hq_256_result.npy'						)

    A__         =    'google/ddpm-ema-celebahq-256'
    A__         =    UNetaDModel.from_pretrained(a						)
    A__         =    RePaintScheduler.from_pretrained(a						)

    A__         =    RePaintPipeline(unet=a						,		scheduler=a						).to(a						)
    repaint.set_progress_bar_config(disable=a						)
    repaint.enable_attention_slicing()

    A__         =    torch.manual_seed(0						)
    A__         =    repaint(
        a						,		a						,		num_inference_steps=2_50						,		eta=0.0						,		jump_length=10						,		jump_n_sample=10						,		generator=a						,		output_type='np'						,		)
    A__         =    output.images[0]

    assert image.shape == (2_56, 2_56, 3)
    assert np.abs(expected_image - image						).mean() < 1e-2








'''simple docstring'''







from dataclasses import dataclass

lowerCAmelCase_    	=							'''0.1'''
lowerCAmelCase_    	=							'''Lucia Harcekova'''

@dataclass
class   _snake_case:
 __snake_case:       int
 __snake_case:       int
 __snake_case:       str






 def __repr__(self					:     Optional[int]						)					->      str:






    """simple docstring"""


    return f"""({self.offset}, {self.length}, {self.indicator})"""




class   _snake_case:






 def __init__(self					:     Optional[Any]						,		a					:     int = 13						,		a					:     int = 6						)					->      None:






    """simple docstring"""


    A__         =    window_size
    A__         =    lookahead_buffer_size
    A__         =    self.window_size - self.lookahead_buffer_size






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     str						)					->      list[Token]:






    """simple docstring"""


    A__         =    []
    A__         =    ''

    # while there are still characters in text to compress
    while text:
       # find the next encoding phrase
       # - triplet with offset, length, indicator (the next encoding character)
       A__         =    self._find_encoding_token(a						,		a						)

       # update the search buffer:
       # - add new characters from text into it
       # - check if size exceed the max search buffer size, if so, drop the
       #   oldest elements
       search_buffer += text[: token.length + 1]
       if len(a						) > self.search_buffer_size:
          A__         =    search_buffer[-self.search_buffer_size :]

       # update the text
       A__         =    text[token.length + 1 :]

       # append the token to output
       output.append(a						)

    return output






 def 	_UpperCamelCase   (self					:     Optional[int]						,		a					:     list[Token]						)					->      str:






    """simple docstring"""


    A__         =    ''

    for token in tokens:
       for _ in range(token.length						):
          output += output[-token.offset]
       output += token.indicator

    return output






 def 	_UpperCamelCase   (self					:     Any						,		a					:     str						,		a					:     str						)					->      Token:






    """simple docstring"""


    if not text:
       raise ValueError('We need some text to work with.'						)

    # Initialise result parameters to default values
    A__     , A__         =    0, 0

    if not search_buffer:
       return Token(a						,		a						,		text[length]						)

    for i, character in enumerate(a						):
       A__         =    len(a						) - i
       if character == text[0]:
          A__         =    self._match_length_from_index(a						,		a						,		0						,		a						)
          # if the found length is bigger than the current or if it's equal,
          # which means it's offset is smaller: update offset and length
          if found_length >= length:
             A__     , A__         =    found_offset, found_length

    return Token(a						,		a						,		text[length]						)






 def 	_UpperCamelCase   (self					:     int						,		a					:     str						,		a					:     str						,		a					:     int						,		a					:     int						)					->      int:






    """simple docstring"""


    if not text or text[text_index] != window[window_index]:
       return 0
    return 1 + self._match_length_from_index(
        a						,		window + text[text_index]						,		text_index + 1						,		window_index + 1						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    # Initialize compressor class
    lowerCAmelCase_    	=							LZaaCompressor(window_size=1_3, lookahead_buffer_size=6)

    # Example
    lowerCAmelCase_    	=							'''cabracadabrarrarrad'''
    lowerCAmelCase_    	=							lzaa_compressor.compress(TEXT)
    print(lzaa_compressor.compress('''ababcbababaa'''))
    lowerCAmelCase_    	=							lzaa_compressor.decompress(compressed_text)
    assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."


