




'''simple docstring'''







from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


lowerCAmelCase_    	=							logging.get_logger(__name__)  # pylint: disable=invalid-name

class   _snake_case( UpperCAmelCase			):
 __snake_case:       str									=				['''safety_checker''', '''feature_extractor''']






 def __init__(self					:     Tuple						,		a					:     AutoencoderKL						,		a					:     CLIPTextModel						,		a					:     CLIPTokenizer						,		a					:     UNetaDConditionModel						,		a					:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,		a					:     StableDiffusionSafetyChecker						,		a					:     CLIPImageProcessor						,		a					:     bool = True						,		)					->      Union[str, Any]:






    """simple docstring"""


    super().__init__()
    if hasattr(scheduler.config						,		'steps_offset'						) and scheduler.config.steps_offset != 1:
       A__         =    (
           f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
           f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
           'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
           ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
           ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
           ' file'
       )
       deprecate('steps_offset!=1'						,		'1.0.0'						,		a						,		standard_warn=a						)
       A__         =    dict(scheduler.config						)
       A__         =    1
       A__         =    FrozenDict(a						)

    self.register_modules(
        vae=a						,		text_encoder=a						,		tokenizer=a						,		unet=a						,		scheduler=a						,		safety_checker=a						,		feature_extractor=a						,		)
    self.register_to_config(requires_safety_checker=a						)






 @property
 def 	_UpperCamelCase   (self					:     str						)					->      Dict[str, Any]:






    """simple docstring"""


    return {k: getattr(self						,		a						) for k in self.config.keys() if not k.startswith('_'						)}






 def 	_UpperCamelCase   (self					:     Tuple						,		a					:     Optional[Union[str, int]] = "auto"						)					->      Union[str, Any]:






    """simple docstring"""


    if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       A__         =    self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(a						)






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






    """simple docstring"""


    self.enable_attention_slicing(a						)






 @torch.no_grad()
 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     Union[str, List[str]]						,		a					:     Union[torch.FloatTensor, PIL.Image.Image]						,		a					:     Union[torch.FloatTensor, PIL.Image.Image]						,		a					:     float = 0.8						,		a					:     Optional[int] = 50						,		a					:     Optional[float] = 7.5						,		a					:     Optional[Union[str, List[str]]] = None						,		a					:     Optional[int] = 1						,		a					:     Optional[float] = 0.0						,		a					:     Optional[torch.Generator] = None						,		a					:     Optional[str] = "pil"						,		a					:     bool = True						,		a					:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,		a					:     int = 1						,		)					->      Union[str, Any]:






    """simple docstring"""


    return StableDiffusionInpaintPipelineLegacy(**self.components						)(
        prompt=a						,		image=a						,		mask_image=a						,		strength=a						,		num_inference_steps=a						,		guidance_scale=a						,		negative_prompt=a						,		num_images_per_prompt=a						,		eta=a						,		generator=a						,		output_type=a						,		return_dict=a						,		callback=a						,		)






 @torch.no_grad()
 def 	_UpperCamelCase   (self					:     int						,		a					:     Union[str, List[str]]						,		a					:     Union[torch.FloatTensor, PIL.Image.Image]						,		a					:     float = 0.8						,		a					:     Optional[int] = 50						,		a					:     Optional[float] = 7.5						,		a					:     Optional[Union[str, List[str]]] = None						,		a					:     Optional[int] = 1						,		a					:     Optional[float] = 0.0						,		a					:     Optional[torch.Generator] = None						,		a					:     Optional[str] = "pil"						,		a					:     bool = True						,		a					:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,		a					:     int = 1						,		**a					:     int						,		)					->      Optional[int]:






    """simple docstring"""


    return StableDiffusionImgaImgPipeline(**self.components						)(
        prompt=a						,		image=a						,		strength=a						,		num_inference_steps=a						,		guidance_scale=a						,		negative_prompt=a						,		num_images_per_prompt=a						,		eta=a						,		generator=a						,		output_type=a						,		return_dict=a						,		callback=a						,		callback_steps=a						,		)






 @torch.no_grad()
 def 	_UpperCamelCase   (self					:     Tuple						,		a					:     Union[str, List[str]]						,		a					:     int = 5_12						,		a					:     int = 5_12						,		a					:     int = 50						,		a					:     float = 7.5						,		a					:     Optional[Union[str, List[str]]] = None						,		a					:     Optional[int] = 1						,		a					:     float = 0.0						,		a					:     Optional[torch.Generator] = None						,		a					:     Optional[torch.FloatTensor] = None						,		a					:     Optional[str] = "pil"						,		a					:     bool = True						,		a					:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,		a					:     int = 1						,		)					->      Any:






    """simple docstring"""


    return StableDiffusionPipeline(**self.components						)(
        prompt=a						,		height=a						,		width=a						,		num_inference_steps=a						,		guidance_scale=a						,		negative_prompt=a						,		num_images_per_prompt=a						,		eta=a						,		generator=a						,		latents=a						,		output_type=a						,		return_dict=a						,		callback=a						,		callback_steps=a						,		)








'''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."


