






import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()




class 	lowerCamelCase__				(							unittest.TestCase ):






      @property
      def      UpperCAmelCase_     (self					:    Any		)       ->   int:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     1
       lowerCamelCase_							:    List[str]							=     3
       lowerCamelCase_							:    str							=     (32, 32)

       lowerCamelCase_							:    Union[str, Any]							=     floats_tensor((batch_size, num_channels) + sizes		,		rng=random.Random(0		)		).to(_snake_case		)
       return image






      @property
      def      UpperCAmelCase_     (self					:    Dict		)       ->   Optional[Any]:


       """simple docstring"""






       torch.manual_seed(0		)
       lowerCamelCase_							:    str							=     UNetaDModel(
           block_out_channels=(32, 64)		,		layers_per_block=2		,		sample_size=32		,		in_channels=6		,		out_channels=3		,		down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,		up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,		)
       return model






      @property
      def      UpperCAmelCase_     (self					:    List[Any]		)       ->   List[str]:


       """simple docstring"""






       torch.manual_seed(0		)
       lowerCamelCase_							:    int							=     VQModel(
           block_out_channels=[32, 64]		,		in_channels=3		,		out_channels=3		,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,		latent_channels=3		,		)
       return model






      def      UpperCAmelCase_     (self					:    List[Any]		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     'cpu'
       lowerCamelCase_							:    Any							=     self.dummy_uncond_unet
       lowerCamelCase_							:    Optional[Any]							=     DDIMScheduler()
       lowerCamelCase_							:    List[str]							=     self.dummy_vq_model

       lowerCamelCase_							:    int							=     LDMSuperResolutionPipeline(unet=_snake_case		,		vqvae=_snake_case		,		scheduler=_snake_case		)
       ldm.to(_snake_case		)
       ldm.set_progress_bar_config(disable=_snake_case		)

       lowerCamelCase_							:    List[str]							=     self.dummy_image.to(_snake_case		)

       lowerCamelCase_							:    Optional[Any]							=     torch.Generator(device=_snake_case		).manual_seed(0		)
       lowerCamelCase_							:    List[str]							=     ldm(image=_snake_case		,		generator=_snake_case		,		num_inference_steps=2		,		output_type='numpy'		).images

       lowerCamelCase_							:    List[str]							=     image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       lowerCamelCase_							:    str							=     np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]		)

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






      @unittest.skipIf(torch_device != 'cuda'		,		'This test requires a GPU'		)
      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     self.dummy_uncond_unet
       lowerCamelCase_							:    Union[str, Any]							=     DDIMScheduler()
       lowerCamelCase_							:    List[Any]							=     self.dummy_vq_model

       # put models in fp16
       lowerCamelCase_							:    List[str]							=     unet.half()
       lowerCamelCase_							:    List[Any]							=     vqvae.half()

       lowerCamelCase_							:    Tuple							=     LDMSuperResolutionPipeline(unet=_snake_case		,		vqvae=_snake_case		,		scheduler=_snake_case		)
       ldm.to(_snake_case		)
       ldm.set_progress_bar_config(disable=_snake_case		)

       lowerCamelCase_							:    List[str]							=     self.dummy_image.to(_snake_case		)

       lowerCamelCase_							:    Any							=     ldm(_snake_case		,		num_inference_steps=2		,		output_type='numpy'		).images

       assert image.shape == (1, 64, 64, 3)




@slow
@require_torch
class 	lowerCamelCase__				(							unittest.TestCase ):






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


       """simple docstring"""






       lowerCamelCase_							:    int							=     load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/vq_diffusion/teddy_bear_pool.png'		)
       lowerCamelCase_							:    Any							=     init_image.resize((64, 64)		,		resample=PIL_INTERPOLATION['lanczos']		)

       lowerCamelCase_							:    Tuple							=     LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'		,		device_map='auto'		)
       ldm.set_progress_bar_config(disable=_snake_case		)

       lowerCamelCase_							:    Optional[int]							=     torch.manual_seed(0		)
       lowerCamelCase_							:    int							=     ldm(image=_snake_case		,		generator=_snake_case		,		num_inference_steps=20		,		output_type='numpy'		).images

       lowerCamelCase_							:    int							=     image[0, -3:, -3:, -1]

       assert image.shape == (1, 256, 256, 3)
       lowerCamelCase_							:    Any							=     np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]		)

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








from dataclasses import dataclass

UpperCamelCase											=      '''0.1'''
UpperCamelCase											=      '''Lucia Harcekova'''




@dataclass
class 	lowerCamelCase__				:
      lowerCamelCase_							:   int
      lowerCamelCase_							:   int
      lowerCamelCase_							:   str






      def __repr__(self					:    Tuple		)       ->   str:


       """simple docstring"""






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




class 	lowerCamelCase__				:






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


       """simple docstring"""






       lowerCamelCase_							:    Any							=     window_size
       lowerCamelCase_							:    List[str]							=     lookahead_buffer_size
       lowerCamelCase_							:    Optional[int]							=     self.window_size - self.lookahead_buffer_size






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    str		)       ->   list[Token]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     []
       lowerCamelCase_							:    List[str]							=     ''

       # 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)
        lowerCamelCase_							:    List[Any]							=     self._find_encoding_token(_snake_case		,		_snake_case		)

        # 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(_snake_case		) > self.search_buffer_size:
         lowerCamelCase_							:    int							=     search_buffer[-self.search_buffer_size :]

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

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

       return output






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    list[Token]		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     ''

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

       return output






      def      UpperCAmelCase_     (self					:    Dict		,		_snake_case					:    str		,		_snake_case					:    str		)       ->   Token:


       """simple docstring"""






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

       # Initialise result parameters to default values
       lowerCamelCase_		,      lowerCamelCase_							:    List[str]							=     0, 0

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

       for i, character in enumerate(_snake_case		):
        lowerCamelCase_							:    Union[str, Any]							=     len(_snake_case		) - i
        if character == text[0]:
         lowerCamelCase_							:    str							=     self._match_length_from_index(_snake_case		,		_snake_case		,		0		,		_snake_case		)
         # 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:
          lowerCamelCase_		,      lowerCamelCase_							:    str							=     found_offset, found_length

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






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    str		,		_snake_case					:    str		,		_snake_case					:    int		,		_snake_case					:    int		)       ->   int:


       """simple docstring"""






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


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

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

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