

import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate







class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  CONFIG_NAME
 @register_to_config
 def __init__(							self				,							A_ = True				,							A_ = 8				,							A_ = "lanczos"				,							A_ = True				,							A_ = False				,							) ->Union[str, Any]:



       '''simple docstring'''





       super().__init__()
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->PIL.Image.Image:



       '''simple docstring'''





       if images.ndim == 3:
             __lowerCAmelCase :						str = images[None, ...]
       __lowerCAmelCase :						Optional[int] = (images * 255).round().astype('''uint8''' )
       if images.shape[-1] == 1:
             # special case for grayscale (single channel) images
             __lowerCAmelCase :						Tuple = [Image.fromarray(image.squeeze()				,							mode='''L''' ) for image in images]
       else:
             __lowerCAmelCase :						Optional[int] = [Image.fromarray(A_ ) for image in images]

       return pil_images
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->np.ndarray:



       '''simple docstring'''





       if not isinstance(A_				,							A_ ):
             __lowerCAmelCase :						Optional[Any] = [images]
       __lowerCAmelCase :						Union[str, Any] = [np.array(A_ ).astype(np.floataa ) / 255.0 for image in images]
       __lowerCAmelCase :						Optional[int] = np.stack(A_				,							axis=0 )

       return images
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->torch.FloatTensor:



       '''simple docstring'''





       if images.ndim == 3:
             __lowerCAmelCase :						Dict = images[..., None]

       __lowerCAmelCase :						Optional[int] = torch.from_numpy(images.transpose(0				,							3				,							1				,							2 ) )
       return images
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->np.ndarray:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = images.cpu().permute(0				,							2				,							3				,							1 ).float().numpy()
       return images
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->Optional[int]:



       '''simple docstring'''





       return 2.0 * images - 1.0
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->Optional[Any]:



       '''simple docstring'''





       return (images / 2 + 0.5).clamp(0				,							1 )
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->PIL.Image.Image:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = image.convert('''RGB''' )
       return image
 def      UpperCamelCase__  (							self				,							A_				,							A_ = None				,							A_ = None				,							) ->PIL.Image.Image:



       '''simple docstring'''





       if height is None:
             __lowerCAmelCase :						Any = image.height
       if width is None:
             __lowerCAmelCase :						List[str] = image.width

       __lowerCAmelCase,							__lowerCAmelCase :						Dict = (
           x - x % self.config.vae_scale_factor for x in (width, height)
       )  # resize to integer multiple of vae_scale_factor
       __lowerCAmelCase :						Optional[int] = image.resize((width, height)				,							resample=PIL_INTERPOLATION[self.config.resample] )
       return image
 def      UpperCamelCase__  (							self				,							A_				,							A_ = None				,							A_ = None				,							) ->torch.Tensor:



       '''simple docstring'''





       __lowerCAmelCase :						int = (PIL.Image.Image, np.ndarray, torch.Tensor)
       if isinstance(A_				,							A_ ):
             __lowerCAmelCase :						List[Any] = [image]
       elif not (isinstance(A_				,							A_ ) and all(isinstance(A_				,							A_ ) for i in image )):
             raise ValueError(
                 f"""Input is in incorrect format: {[type(A_ ) for i in image]}. Currently, we only support {", ".join(A_ )}""" )

       if isinstance(image[0]				,							PIL.Image.Image ):
             if self.config.do_convert_rgb:
                   __lowerCAmelCase :						Tuple = [self.convert_to_rgb(A_ ) for i in image]
             if self.config.do_resize:
                   __lowerCAmelCase :						Any = [self.resize(A_				,							A_				,							A_ ) for i in image]
             __lowerCAmelCase :						Dict = self.pil_to_numpy(A_ )  # to np
             __lowerCAmelCase :						Optional[Any] = self.numpy_to_pt(A_ )  # to pt

       elif isinstance(image[0]				,							np.ndarray ):
             __lowerCAmelCase :						Union[str, Any] = np.concatenate(A_				,							axis=0 ) if image[0].ndim == 4 else np.stack(A_				,							axis=0 )
             __lowerCAmelCase :						Any = self.numpy_to_pt(A_ )
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Optional[int] = image.shape
             if self.config.do_resize and (
                 height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
             ):
                   raise ValueError(
                       f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                       f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""" )

       elif isinstance(image[0]				,							torch.Tensor ):
             __lowerCAmelCase :						Union[str, Any] = torch.cat(A_				,							axis=0 ) if image[0].ndim == 4 else torch.stack(A_				,							axis=0 )
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase :						Any = image.shape

             # don't need any preprocess if the image is latents
             if channel == 4:
                   return image

             if self.config.do_resize and (
                 height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
             ):
                   raise ValueError(
                       f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                       f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor""" )

        # expected range [0,1], normalize to [-1,1]
       __lowerCAmelCase :						Dict = self.config.do_normalize
       if image.min() < 0:
             warnings.warn(
                 '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
                 f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""				,							A_				,							)
             __lowerCAmelCase :						List[str] = False

       if do_normalize:
             __lowerCAmelCase :						Any = self.normalize(A_ )

       return image





 def      UpperCamelCase__  (							self				,							A_				,							A_ = "pil"				,							A_ = None				,							) ->List[Any]:



       '''simple docstring'''





       if not isinstance(A_				,							torch.Tensor ):
             raise ValueError(
                 f"""Input for postprocessing is in incorrect format: {type(A_ )}. We only support pytorch tensor""" )
       if output_type not in ["latent", "pt", "np", "pil"]:
             __lowerCAmelCase :						Any = (
                 f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                 '''`pil`, `np`, `pt`, `latent`'''
             )
             deprecate('''Unsupported output_type'''				,							'''1.0.0'''				,							A_				,							standard_warn=A_ )
             __lowerCAmelCase :						str = '''np'''

       if output_type == "latent":
             return image

       if do_denormalize is None:
             __lowerCAmelCase :						Optional[Any] = [self.config.do_normalize] * image.shape[0]

       __lowerCAmelCase :						Union[str, Any] = torch.stack(
           [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

       if output_type == "pt":
             return image

       __lowerCAmelCase :						Tuple = self.pt_to_numpy(A_ )

       if output_type == "np":
             return image

       if output_type == "pil":
             return self.numpy_to_pil(A_ )







class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  CONFIG_NAME
 @register_to_config
 def __init__(							self				,							A_ = True				,							A_ = 8				,							A_ = "lanczos"				,							A_ = True				,							) ->str:



       '''simple docstring'''





       super().__init__()
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->Tuple:



       '''simple docstring'''





       if images.ndim == 3:
             __lowerCAmelCase :						Union[str, Any] = images[None, ...]
       __lowerCAmelCase :						str = (images * 255).round().astype('''uint8''' )
       if images.shape[-1] == 1:
             # special case for grayscale (single channel) images
             __lowerCAmelCase :						Any = [Image.fromarray(image.squeeze()				,							mode='''L''' ) for image in images]
       else:
             __lowerCAmelCase :						List[str] = [Image.fromarray(image[:, :, :3] ) for image in images]

       return pil_images
 @staticmethod
 def      UpperCamelCase__  (							A_ ) ->str:



       '''simple docstring'''





       return image[:, :, 1] * 2**8 + image[:, :, 2]
 def      UpperCamelCase__  (							self				,							A_ ) ->List[str]:



       '''simple docstring'''





       if images.ndim == 3:
             __lowerCAmelCase :						List[Any] = images[None, ...]
       __lowerCAmelCase :						Union[str, Any] = images[:, :, :, 3:]
       if images.shape[-1] == 6:
             __lowerCAmelCase :						Union[str, Any] = (images_depth * 255).round().astype('''uint8''' )
             __lowerCAmelCase :						str = [
                 Image.fromarray(self.rgblike_to_depthmap(A_ )				,							mode='''I;16''' ) for image_depth in images_depth
             ]
       elif images.shape[-1] == 4:
             __lowerCAmelCase :						List[str] = (images_depth * 65_535.0).astype(np.uintaa )
             __lowerCAmelCase :						Union[str, Any] = [Image.fromarray(A_				,							mode='''I;16''' ) for image_depth in images_depth]
       else:
             raise Exception('''Not supported''' )

       return pil_images





 def      UpperCamelCase__  (							self				,							A_				,							A_ = "pil"				,							A_ = None				,							) ->int:



       '''simple docstring'''





       if not isinstance(A_				,							torch.Tensor ):
             raise ValueError(
                 f"""Input for postprocessing is in incorrect format: {type(A_ )}. We only support pytorch tensor""" )
       if output_type not in ["latent", "pt", "np", "pil"]:
             __lowerCAmelCase :						List[Any] = (
                 f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                 '''`pil`, `np`, `pt`, `latent`'''
             )
             deprecate('''Unsupported output_type'''				,							'''1.0.0'''				,							A_				,							standard_warn=A_ )
             __lowerCAmelCase :						int = '''np'''

       if do_denormalize is None:
             __lowerCAmelCase :						int = [self.config.do_normalize] * image.shape[0]

       __lowerCAmelCase :						Tuple = torch.stack(
           [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

       __lowerCAmelCase :						List[Any] = self.pt_to_numpy(A_ )

       if output_type == "np":
             if image.shape[-1] == 6:
                   __lowerCAmelCase :						int = np.stack([self.rgblike_to_depthmap(im[:, :, 3:] ) for im in image]				,							axis=0 )
             else:
                   __lowerCAmelCase :						Dict = image[:, :, :, 3:]
             return image[:, :, :, :3], image_depth

       if output_type == "pil":
             return self.numpy_to_pil(A_ ), self.numpy_to_depth(A_ )
       else:
             raise Exception(f"""This type {output_type} is not supported""" )





from dataclasses import dataclass

_UpperCamelCase			      =		"0.1"
_UpperCamelCase			      =		"Lucia Harcekova"







@dataclass
class   __lowercase				:
 _UpperCamelCase        =  42
 _UpperCamelCase        =  42
 _UpperCamelCase        =  42
 def __repr__(							self ) ->str:



       '''simple docstring'''





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







class   __lowercase				:
 def __init__(							self				,							A_ = 13				,							A_ = 6 ) ->None:



       '''simple docstring'''





       __lowerCAmelCase :						List[str] = window_size
       __lowerCAmelCase :						str = lookahead_buffer_size
       __lowerCAmelCase :						Dict = self.window_size - self.lookahead_buffer_size
 def      UpperCamelCase__  (							self				,							A_ ) ->list[Token]:



       '''simple docstring'''





       __lowerCAmelCase :						int = []
       __lowerCAmelCase :						Any = ''''''

       # 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 :						Optional[int] = 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:
                   __lowerCAmelCase :						Dict = search_buffer[-self.search_buffer_size :]

             # update the text
             __lowerCAmelCase :						List[Any] = text[token.length + 1 :]

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

       return output
 def      UpperCamelCase__  (							self				,							A_ ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[int] = ''''''

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

       return output
 def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->Token:



       '''simple docstring'''





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

       # Initialise result parameters to default values
       __lowerCAmelCase,							__lowerCAmelCase :						Union[str, Any] = 0, 0

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

       for i, character in enumerate(A_ ):
             __lowerCAmelCase :						Dict = len(A_ ) - i
             if character == text[0]:
                   __lowerCAmelCase :						Dict = 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:
                         __lowerCAmelCase,							__lowerCAmelCase :						int = found_offset, found_length

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





 def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_				,							A_ ) ->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
 _UpperCamelCase			      =		LZaaCompressor(window_size=13, 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."


