





'''simple docstring'''


from dataclasses import dataclass

lowerCAmelCase      :  Union[str, Any]      							='''0.1'''
lowerCAmelCase      :  Union[str, Any]      							='''Lucia Harcekova'''
@dataclass
class    a_     :
     __A  				=    42
     __A  				=    42
     __A  				=    42





     def __repr__(				self	:      Union[str, Any]		):





        """simple docstring"""




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







class    a_     :





     def __init__(				self	:      str			,   lowercase	:      int = 13			,   lowercase	:      int = 6		):





        """simple docstring"""




        lowercase_    :List[Any]           =				window_size
        lowercase_    :Any           =				lookahead_buffer_size
        lowercase_    :Tuple           =				self.window_size - self.lookahead_buffer_size





     def 							lowercase__   (				self	:      Dict			,   lowercase	:      str		):





        """simple docstring"""




        lowercase_    :List[str]           =				[]
        lowercase_    :Optional[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)
           lowercase_    :Optional[int]           =				self._find_encoding_token(lowerCAmelCase_			,   lowerCAmelCase_		)

           # 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(lowerCAmelCase_		) > self.search_buffer_size:
              lowercase_    :int           =				search_buffer[-self.search_buffer_size :]

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

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

        return output





     def 							lowercase__   (				self	:      Optional[int]			,   lowercase	:      list[Token]		):





        """simple docstring"""




        lowercase_    :Any           =				""

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

        return output





     def 							lowercase__   (				self	:      Tuple			,   lowercase	:      str			,   lowercase	:      str		):





        """simple docstring"""




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

        # Initialise result parameters to default values
        lowercase_    ,							lowercase_    :Tuple           =				0, 0

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

        for i, character in enumerate(lowerCAmelCase_		):
           lowercase_    :Dict           =				len(lowerCAmelCase_		) - i
           if character == text[0]:
              lowercase_    :Optional[int]           =				self._match_length_from_index(lowerCAmelCase_			,   lowerCAmelCase_			,   0			,   lowerCAmelCase_		)
              # 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:
                 lowercase_    ,							lowercase_    :int           =				found_offset, found_length

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







     def 							lowercase__   (				self	:      Any			,   lowercase	:      str			,   lowercase	:      str			,   lowercase	:      int			,   lowercase	:      int		):





        """simple docstring"""




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


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

 testmod()
 # Initialize compressor class
 lowerCAmelCase      :  Tuple      							=LZaaCompressor(window_size=13, lookahead_buffer_size=6)

 # Example
 lowerCAmelCase      :  Any      							='''cabracadabrarrarrad'''
 lowerCAmelCase      :  str      							=lzaa_compressor.compress(TEXT)
 print(lzaa_compressor.compress('''ababcbababaa'''))
 lowerCAmelCase      :  Optional[Any]      							=lzaa_compressor.decompress(compressed_text)
 assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."









'''simple docstring'''


import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Optional[int]			,__lowerCamelCase      :      Union[str, Any]			,__lowerCamelCase      :      List[Any]			,__lowerCamelCase      :      str=None				):
   lowercase_    :List[Any]					    =				None
   # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
   if str(weight.dtype				) == "torch.bfloat16":
      # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
      lowercase_    :int					    =				weight.view(torch.intaa				)
      lowercase_    :List[Any]					    =				"bfloat16"
   lowercase_    :Optional[Any]					    =				weight.cpu().numpy()
   lowercase_    :Optional[int]					    =				os.path.join(__lowerCamelCase			,F'{weight_name}.dat'				)
   if index is not None:
      if dtype is None:
         lowercase_    :str					    =				str(array.dtype				)
      lowercase_    :int					    =				{"dtype": dtype, "shape": list(array.shape				)}
   if array.ndim == 0:
      lowercase_    :int					    =				array[None]
   lowercase_    :List[Any]					    =				np.memmap(__lowerCamelCase			,dtype=array.dtype			,mode="w+"			,shape=array.shape				)
   lowercase_    :List[Any]					    =				array[:]
   file_array.flush()
   return index
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int			,__lowerCamelCase      :      Dict				):
   lowercase_    :int					    =				tuple(weight_info["shape"]				)
   if shape == ():
      # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
      lowercase_    :List[str]					    =				(1,)

   lowercase_    :List[str]					    =				weight_info["dtype"]
   if dtype == "bfloat16":
      # NumPy does not support bfloat16 so this was saved as a int16
      lowercase_    :Optional[Any]					    =				"int16"

   lowercase_    :str					    =				np.memmap(__lowerCamelCase			,dtype=__lowerCamelCase			,shape=__lowerCamelCase			,mode="r"				)

   if len(weight_info["shape"]				) == 0:
      lowercase_    :Union[str, Any]					    =				weight[0]
   lowercase_    :str					    =				torch.tensor(__lowerCamelCase				)
   if weight_info["dtype"] == "bfloat16":
      lowercase_    :Any					    =				weight.view(torch.bfloataa				)

   return weight
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Dict			,__lowerCamelCase      :      List[str]				):
   if index is None or len(__lowerCamelCase				) == 0:
      # Nothing to save
      return

   lowercase_    :List[Any]					    =				os.path.join(__lowerCamelCase			,"index.json"				)
   if os.path.isfile(__lowerCamelCase				):
      with open(__lowerCamelCase			,"r"			,encoding="utf-8"				) as f:
         lowercase_    :Any					    =				json.load(__lowerCamelCase				)
   else:
      lowercase_    :Union[str, Any]					    =				{}
   current_index.update(__lowerCamelCase				)

   with open(__lowerCamelCase			,"w"			,encoding="utf-8"				) as f:
      json.dump(__lowerCamelCase			,__lowerCamelCase			,indent=2				)
def 							UpperCAmelCase_						(		__lowerCamelCase      :      Union[str, os.PathLike]			,__lowerCamelCase      :      Dict[str, torch.Tensor]				):
   os.makedirs(__lowerCamelCase			,exist_ok=__lowerCamelCase				)
   lowercase_    :List[Any]					    =				{}
   for name, parameter in state_dict.items():
      lowercase_    :Tuple					    =				offload_weight(__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,index=__lowerCamelCase				)

   # Update index
   save_offload_index(__lowerCamelCase			,__lowerCamelCase				)
class    a_     (       _lowerCAmelCase							):





     def __init__(				self	:      int			,   lowercase	:      Mapping			,   lowercase	:      str		):





        """simple docstring"""




        lowercase_    :Union[str, Any]					    =				dataset
        lowercase_    :List[str]					    =				prefix





     def __getitem__(				self	:      Any			,   lowercase	:      List[str]		):





        """simple docstring"""




        return self.dataset[F'{self.prefix}{key}']





     def __iter__(				self	:      Optional[int]		):





        """simple docstring"""




        return iter([key for key in self.dataset if key.startswith(self.prefix		)]		)







     def __len__(				self	:      Any		):





        """simple docstring"""




        return len(self.dataset		)







class    a_     (       _lowerCAmelCase							):





     def __init__(				self	:      Tuple			,   lowercase	:      Dict[str, torch.Tensor] = None			,   lowercase	:      Optional[Union[str, os.PathLike]] = None			,   lowercase	:      Mapping = None			,   lowercase	:      Optional[Any]=None			,   ):





        """simple docstring"""




        if state_dict is None and save_folder is None:
           raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights."		)

        lowercase_    :Optional[int]					    =				{} if state_dict is None else state_dict
        lowercase_    :str					    =				save_folder
        if index is None and save_folder is not None:
           with open(os.path.join(lowercase			,   "index.json"		)		) as f:
              lowercase_    :Any					    =				json.load(lowercase		)
        lowercase_    :str					    =				{} if index is None else index
        lowercase_    :List[str]					    =				list(self.state_dict.keys()		)
        self.all_keys.extend([key for key in self.index if key not in self.all_keys]		)
        lowercase_    :List[Any]					    =				device





     def __getitem__(				self	:      Any			,   lowercase	:      str		):





        """simple docstring"""




        if key in self.state_dict:
           return self.state_dict[key]
        lowercase_    :int					    =				self.index[key]
        if weight_info.get("safetensors_file"		) is not None:
           if not is_safetensors_available():
              raise ImportError("These offloaded weights require the use of safetensors: `pip install safetensors`."		)

           from safetensors import safe_open

           lowercase_    :Dict					    =				"cpu" if self.device is None else self.device
           with safe_open(weight_info["safetensors_file"]			,   framework="pt"			,   device=lowercase		) as f:
              lowercase_    :Tuple					    =				f.get_tensor(weight_info.get("weight_name"			,   lowercase		)		)

           if "dtype" in weight_info:
              return tensor.to(getattr(lowercase			,   weight_info["dtype"]		)		)
           else:
              return tensor

        lowercase_    :str					    =				os.path.join(self.save_folder			,   F'{key}.dat'		)
        return load_offloaded_weight(lowercase			,   lowercase		)





     def __iter__(				self	:      Optional[int]		):





        """simple docstring"""




        return iter(self.all_keys		)







     def __len__(				self	:      Tuple		):





        """simple docstring"""




        return len(self.all_keys		)







def 							UpperCAmelCase_						(		__lowerCamelCase      :      Dict[str, torch.Tensor]			,__lowerCamelCase      :      List[str]				):
   lowercase_    :List[str]					    =				{}
   for module_name in submodule_names:
      # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
      # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
      result.update(
          {
              key: param
              for key, param in state_dict.items()
              if key == module_name or key.startswith(module_name + "."				)
          }				)
   return result


