


'''simple docstring'''

import math
import sys

def _SCREAMING_SNAKE_CASE (A			)			->			str:






       """simple docstring"""







       lowercase__      =					''''''
       try:
              with open(A    ,					'''rb'''			) as binary_file:
                     lowercase__      =					binary_file.read()
              for dat in data:
                     lowercase__      =					f"{dat:08b}"
                     result += curr_byte
              return result
       except OSError:
              print('''File not accessible'''			)
              sys.exit()

def _SCREAMING_SNAKE_CASE (A			)			->			str:






       """simple docstring"""







       lowercase__      =					{'''0''': '''0''', '''1''': '''1'''}
       lowercase__     ,lowercase__      =					'''''', ''''''
       lowercase__      =					len(A			)

       for i in range(len(A			)			):
              curr_string += data_bits[i]
              if curr_string not in lexicon:
                     continue

              lowercase__      =					lexicon[curr_string]
              result += last_match_id
              lowercase__      =					last_match_id + '''0'''

              if math.loga(A			).is_integer():
                     lowercase__      =					{}
                     for curr_key in list(A			):
                            lowercase__      =					lexicon.pop(A			)
                     lowercase__      =					new_lex

              lowercase__      =					last_match_id + '''1'''
              index += 1
              lowercase__      =					''''''
       return result

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			None:






       """simple docstring"""







       lowercase__      =					8
       try:
              with open(A    ,					'''wb'''			) as opened_file:
                     lowercase__      =					[
                         to_write[i : i + byte_length]
                         for i in range(0    ,					len(A			)    ,					A			)
                     ]

                     if len(result_byte_array[-1]			) % byte_length == 0:
                            result_byte_array.append('''10000000'''			)
                     else:
                            result_byte_array[-1] += "1" + "0" * (
                                byte_length - len(result_byte_array[-1]			) - 1
                            )

                     for elem in result_byte_array[:-1]:
                            opened_file.write(int(A    ,					2			).to_bytes(1    ,					byteorder='''big'''			)			)
       except OSError:
              print('''File not accessible'''			)
              sys.exit()

def _SCREAMING_SNAKE_CASE (A			)			->			str:






       """simple docstring"""







       lowercase__      =					0
       for letter in data_bits:
              if letter == "1":
                     break
              counter += 1

       lowercase__      =					data_bits[counter:]
       lowercase__      =					data_bits[counter + 1 :]
       return data_bits

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			None:






       """simple docstring"""







       lowercase__      =					read_file_binary(A			)
       lowercase__      =					remove_prefix(A			)
       lowercase__      =					decompress_data(A			)
       write_file_binary(A    ,					A			)


if __name__ == "__main__":
 compress(sys.argv[1], sys.argv[2])








'''simple docstring'''

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import torch
class     __lowerCAmelCase		(TensorFormatter[Mapping, """torch.Tensor""", Mapping]	):





      '''simple docstring'''






      def __init__(self       :       Optional[Any]  ,		UpperCamelCase       :       Tuple=None  ,		**UpperCamelCase       :       List[str]					):

             '''simple docstring'''







             super().__init__(features=UpperCamelCase					)
             lowercase__      =					torch_tensor_kwargs
             import torch  # noqa import torch at initialization



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       Union[str, Any]					):

             '''simple docstring'''







             import torch

             if isinstance(UpperCamelCase  ,		UpperCamelCase					) and column:
                    if all(
                        isinstance(UpperCamelCase  ,		torch.Tensor					) and x.shape == column[0].shape and x.dtype == column[0].dtype
                        for x in column					):
                           return torch.stack(UpperCamelCase					)
             return column



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             import torch

             if isinstance(UpperCamelCase  ,		(str, bytes, type(UpperCamelCase					))					):
                    return value
             elif isinstance(UpperCamelCase  ,		(np.character, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.character					):
                    return value.tolist()

             lowercase__      =					{}

             if isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.integer					):
                    lowercase__      =					{'''dtype''': torch.intaa}
             elif isinstance(UpperCamelCase  ,		(np.number, np.ndarray)					) and np.issubdtype(value.dtype  ,		np.floating					):
                    lowercase__      =					{'''dtype''': torch.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(UpperCamelCase  ,		PIL.Image.Image					):
                           lowercase__      =					np.asarray(UpperCamelCase					)
             return torch.tensor(UpperCamelCase  ,		**{**default_dtype, **self.torch_tensor_kwargs}					)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             import torch

             # support for torch, tf, jax etc.
             if hasattr(UpperCamelCase  ,		'''__array__'''					) and not isinstance(UpperCamelCase  ,		torch.Tensor					):
                    lowercase__      =					data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(UpperCamelCase  ,		np.ndarray					):
                    if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                           return self._consolidate([self.recursive_tensorize(UpperCamelCase					) for substruct in data_struct]					)
             elif isinstance(UpperCamelCase  ,		(list, tuple)					):
                    return self._consolidate([self.recursive_tensorize(UpperCamelCase					) for substruct in data_struct]					)
             return self._tensorize(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       dict					):

             '''simple docstring'''







             return map_nested(self._recursive_tensorize  ,		UpperCamelCase  ,		map_list=UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_row(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_row(UpperCamelCase					)
             return self.recursive_tensorize(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_column(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_column(UpperCamelCase  ,		pa_table.column_names[0]					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             lowercase__      =					self._consolidate(UpperCamelCase					)
             return column






      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       pa.Table					):

             '''simple docstring'''







             lowercase__      =					self.numpy_arrow_extractor().extract_batch(UpperCamelCase					)
             lowercase__      =					self.python_features_decoder.decode_batch(UpperCamelCase					)
             lowercase__      =					self.recursive_tensorize(UpperCamelCase					)
             for column_name in batch:
                    lowercase__      =					self._consolidate(batch[column_name]					)
             return batch




