




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     torch.Tensor:







				'''simple docstring'''


				if not inplace:
								__lowerCAmelCase					=  ma + ma
				else:
								ma += ma

				return ma





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     torch.Tensor:







				'''simple docstring'''


				__lowerCAmelCase					=  -1 * len(lowerCamelCase__   )
				__lowerCAmelCase					=  list(range(len(tensor.shape[:zero_index]   )   )   )
				return tensor.permute(first_inds + [zero_index + i for i in inds]   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     torch.Tensor:







				'''simple docstring'''


				return t.reshape(t.shape[:-no_dims] + (-1,)   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = 1e-4   )     ->     torch.Tensor:







				'''simple docstring'''


				__lowerCAmelCase					=  mask.expand(*value.shape   )
				return torch.sum(mask * value						,			dim=lowerCamelCase__   ) / (eps + torch.sum(lowerCamelCase__						,			dim=lowerCamelCase__   ))





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__ = 2.3125						,			UpperCamelCase__ = 21.6875						,			UpperCamelCase__ = 64   )     ->     torch.Tensor:







				'''simple docstring'''


				__lowerCAmelCase					=  torch.linspace(lowerCamelCase__						,			lowerCamelCase__						,			no_bins - 1						,			device=pts.device   )
				__lowerCAmelCase					=  torch.sqrt(torch.sum((pts.unsqueeze(-2   ) - pts.unsqueeze(-3   )) ** 2						,			dim=-1   )   )
				return torch.bucketize(lowerCamelCase__						,			lowerCamelCase__   )





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     dict:







				'''simple docstring'''


				__lowerCAmelCase					=  dicts[0]
				__lowerCAmelCase					=  {}
				for k, v in first.items():
								__lowerCAmelCase					=  [d[k] for d in dicts]
								if isinstance(lowerCamelCase__						,			lowerCamelCase__   ):
												__lowerCAmelCase					=  dict_multimap(lowerCamelCase__						,			lowerCamelCase__   )
								else:
												__lowerCAmelCase					=  fn(lowerCamelCase__   )

				return new_dict





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     torch.Tensor:







				'''simple docstring'''


				__lowerCAmelCase					=  v_bins.view(((1,) * len(x.shape   )) + (len(lowerCamelCase__   ),)   )
				__lowerCAmelCase					=  x[..., None] - reshaped_bins
				__lowerCAmelCase					=  torch.argmin(torch.abs(lowerCamelCase__   )						,			dim=-1   )
				return nn.functional.one_hot(lowerCamelCase__						,			num_classes=len(lowerCamelCase__   )   ).float()





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = 0						,			UpperCamelCase__ = 0   )     ->     torch.Tensor:







				'''simple docstring'''


				__lowerCAmelCase					=  []
				for i, s in enumerate(data.shape[:no_batch_dims]   ):
								__lowerCAmelCase					=  torch.arange(lowerCamelCase__   )
								__lowerCAmelCase					=  r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape   ) - i - 1)))   )
								ranges.append(lowerCamelCase__   )

				__lowerCAmelCase					=  [slice(lowerCamelCase__   ) for _ in range(len(data.shape   ) - no_batch_dims   )]
				__lowerCAmelCase					=  inds
				ranges.extend(lowerCamelCase__   )
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(lowerCamelCase__   )]


__A						:		Dict          =						TypeVar("T")





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Dict[Any, Union[dict, list, tuple, Any]]:







				'''simple docstring'''


				__lowerCAmelCase					=  {}
				for k, v in dic.items():
								if isinstance(lowerCamelCase__						,			lowerCamelCase__   ):
												__lowerCAmelCase					=  dict_map(lowerCamelCase__						,			lowerCamelCase__						,			lowerCamelCase__   )
								else:
												__lowerCAmelCase					=  tree_map(lowerCamelCase__						,			lowerCamelCase__						,			lowerCamelCase__   )

				return new_dict





@overload
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Any:







				'''simple docstring'''


				...





@overload
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     dict:







				'''simple docstring'''


				...





@overload
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     list:







				'''simple docstring'''


				...





@overload
def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     tuple:







				'''simple docstring'''


				...





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Any:







				'''simple docstring'''


				if isinstance(lowerCamelCase__						,			lowerCamelCase__   ):
								return dict_map(lowerCamelCase__						,			lowerCamelCase__						,			lowerCamelCase__   )
				elif isinstance(lowerCamelCase__						,			lowerCamelCase__   ):
								return [tree_map(lowerCamelCase__						,			lowerCamelCase__						,			lowerCamelCase__   ) for x in tree]
				elif isinstance(lowerCamelCase__						,			lowerCamelCase__   ):
								return tuple(tree_map(lowerCamelCase__						,			lowerCamelCase__						,			lowerCamelCase__   ) for x in tree   )
				elif isinstance(lowerCamelCase__						,			lowerCamelCase__   ):
								return fn(lowerCamelCase__   )
				else:
								print(type(lowerCamelCase__   )   )
								raise ValueError("""Not supported"""   )
__A						:		List[str]          =						partial(tree_map, leaf_type=torch.Tensor)













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."
