

import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCamelCase									= logging.get_logger(__name__)







@dataclass
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     str
   lowerCamelCase__			:     str
   lowerCamelCase__			:     Optional[str]    =       None
   lowerCamelCase__			:     Optional[str]    =       None



   def 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






     return json.dumps(dataclasses.asdict(self	),   indent=2	) + "\n"







@dataclass(frozen=lowerCamelCase__							)
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     List[int]
   lowerCamelCase__			:     Optional[List[int]]    =       None
   lowerCamelCase__			:     Optional[List[int]]    =       None
   lowerCamelCase__			:     Optional[Union[int, float]]    =       None



   def 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






     return json.dumps(dataclasses.asdict(self	)	) + "\n"







class 						__magic_name__      :

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     raise NotImplementedError()



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






     raise NotImplementedError()



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   List[Any]:






     """simple docstring"""






     raise NotImplementedError()



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Any:






     """simple docstring"""






     raise NotImplementedError()



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     raise NotImplementedError()



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Dict:






     """simple docstring"""






     if len(self.get_labels()	) > 1:
       a__  =self.get_labels()[int(example.label	)]
     return example



   @classmethod
   def 			_UpperCAmelCase			(    cls,   lowercase_,   lowercase_=None	)							->   Union[str, Any]:






     """simple docstring"""






     with open(lowercase_,   '''r''',   encoding='''utf-8-sig'''	) as f:
       return list(csv.reader(lowercase_,   delimiter='''\t''',   quotechar=lowercase_	)	)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(    self,   lowercase_=None,   lowercase_=None,   lowercase_="classification",   lowercase_=False	)							->   Tuple:






     """simple docstring"""






     a__  =[] if labels is None else labels
     a__  =[] if examples is None else examples
     a__  =mode
     a__  =verbose



   def __len__(    self	)							->   List[str]:






     """simple docstring"""






     return len(self.examples	)



   def __getitem__(    self,   lowercase_	)							->   Tuple:






     """simple docstring"""






     if isinstance(lowercase_,   lowercase_	):
       return SingleSentenceClassificationProcessor(labels=self.labels,   examples=self.examples[idx]	)
     return self.examples[idx]



   @classmethod
   def 			_UpperCAmelCase			(    cls,   lowercase_,   lowercase_="",   lowercase_=0,   lowercase_=1,   lowercase_=None,   lowercase_=False,   **lowercase_	)							->   List[Any]:






     """simple docstring"""






     a__  =cls(**lowercase_	)
     processor.add_examples_from_csv(
         lowercase_,   split_name=lowercase_,   column_label=lowercase_,   column_text=lowercase_,   column_id=lowercase_,   skip_first_row=lowercase_,   overwrite_labels=lowercase_,   overwrite_examples=lowercase_,   )
     return processor



   @classmethod
   def 			_UpperCAmelCase			(    cls,   lowercase_,   lowercase_=None,   **lowercase_	)							->   int:






     """simple docstring"""






     a__  =cls(**lowercase_	)
     processor.add_examples(lowercase_,   labels=lowercase_	)
     return processor



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_="",   lowercase_=0,   lowercase_=1,   lowercase_=None,   lowercase_=False,   lowercase_=False,   lowercase_=False,   )							->   str:






     """simple docstring"""






     a__  =self._read_tsv(lowercase_	)
     if skip_first_row:
       a__  =lines[1:]
     a__  =[]
     a__  =[]
     a__  =[]
     for i, line in enumerate(lowercase_	):
       texts.append(line[column_text]	)
       labels.append(line[column_label]	)
       if column_id is not None:
         ids.append(line[column_id]	)
       else:
         a__  =F"""{split_name}-{i}""" if split_name else str(lowercase_	)
         ids.append(lowercase_	)

     return self.add_examples(
         lowercase_,   lowercase_,   lowercase_,   overwrite_labels=lowercase_,   overwrite_examples=lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=None,   lowercase_=None,   lowercase_=False,   lowercase_=False	)							->   Dict:






     """simple docstring"""






     if labels is not None and len(lowercase_	) != len(lowercase_	):
       raise ValueError(
           F"""Text and labels have mismatched lengths {len(lowercase_	)} and {len(lowercase_	)}"""	)
     if ids is not None and len(lowercase_	) != len(lowercase_	):
       raise ValueError(F"""Text and ids have mismatched lengths {len(lowercase_	)} and {len(lowercase_	)}"""	)
     if ids is None:
       a__  =[None] * len(lowercase_	)
     if labels is None:
       a__  =[None] * len(lowercase_	)
     a__  =[]
     a__  =set()
     for text_or_text_and_label, label, guid in zip(lowercase_,   lowercase_,   lowercase_	):
       if isinstance(lowercase_,   (tuple, list)	) and label is None:
         a__,				a__  =text_or_text_and_label
       else:
         a__  =text_or_text_and_label
       added_labels.add(lowercase_	)
       examples.append(InputExample(guid=lowercase_,   text_a=lowercase_,   text_b=lowercase_,   label=lowercase_	)	)

     # Update examples
     if overwrite_examples:
       a__  =examples
     else:
       self.examples.extend(lowercase_	)

     # Update labels
     if overwrite_labels:
       a__  =list(lowercase_	)
     else:
       a__  =list(set(self.labels	).union(lowercase_	)	)

     return self.examples



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=None,   lowercase_=False,   lowercase_=0,   lowercase_=True,   lowercase_=None,   )							->   Optional[int]:






     """simple docstring"""






     if max_length is None:
       a__  =tokenizer.max_len

     a__  ={label: i for i, label in enumerate(self.labels	)}

     a__  =[]
     for ex_index, example in enumerate(self.examples	):
       if ex_index % 10000 == 0:
         logger.info(F"""Tokenizing example {ex_index}"""	)

       a__  =tokenizer.encode(
           example.text_a,   add_special_tokens=lowercase_,   max_length=min(lowercase_,   tokenizer.max_len	),   )
       all_input_ids.append(lowercase_	)

     a__  =max(len(lowercase_	) for input_ids in all_input_ids	)

     a__  =[]
     for ex_index, (input_ids, example) in enumerate(zip(lowercase_,   self.examples	)	):
       if ex_index % 10000 == 0:
         logger.info(F"""Writing example {ex_index}/{len(self.examples	)}"""	)
       # The mask has 1 for real tokens and 0 for padding tokens. Only real
       # tokens are attended to.
       a__  =[1 if mask_padding_with_zero else 0] * len(lowercase_	)

       # Zero-pad up to the sequence length.
       a__  =batch_length - len(lowercase_	)
       if pad_on_left:
         a__  =([pad_token] * padding_length) + input_ids
         a__  =([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
       else:
         a__  =input_ids + ([pad_token] * padding_length)
         a__  =attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

       if len(lowercase_	) != batch_length:
         raise ValueError(F"""Error with input length {len(lowercase_	)} vs {batch_length}"""	)
       if len(lowercase_	) != batch_length:
         raise ValueError(F"""Error with input length {len(lowercase_	)} vs {batch_length}"""	)

       if self.mode == "classification":
         a__  =label_map[example.label]
       elif self.mode == "regression":
         a__  =float(example.label	)
       else:
         raise ValueError(self.mode	)

       if ex_index < 5 and self.verbose:
         logger.info('''*** Example ***'''	)
         logger.info(F"""guid: {example.guid}"""	)
         logger.info(F"""input_ids: {" ".join([str(lowercase_	) for x in input_ids]	)}"""	)
         logger.info(F"""attention_mask: {" ".join([str(lowercase_	) for x in attention_mask]	)}"""	)
         logger.info(F"""label: {example.label} (id = {label})"""	)

       features.append(InputFeatures(input_ids=lowercase_,   attention_mask=lowercase_,   label=lowercase_	)	)

     if return_tensors is None:
       return features
     elif return_tensors == "tf":
       if not is_tf_available():
         raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''	)
       import tensorflow as tf

       def gen():
         for ex in features:
           yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

       a__  =tf.data.Dataset.from_generator(
           lowercase_,   ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa),   ({'''input_ids''': tf.TensorShape([None]	), '''attention_mask''': tf.TensorShape([None]	)}, tf.TensorShape([]	)),   )
       return dataset
     elif return_tensors == "pt":
       if not is_torch_available():
         raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''	)
       import torch
       from torch.utils.data import TensorDataset

       a__  =torch.tensor([f.input_ids for f in features],   dtype=torch.long	)
       a__  =torch.tensor([f.attention_mask for f in features],   dtype=torch.long	)
       if self.mode == "classification":
         a__  =torch.tensor([f.label for f in features],   dtype=torch.long	)
       elif self.mode == "regression":
         a__  =torch.tensor([f.label for f in features],   dtype=torch.float	)

       a__  =TensorDataset(lowercase_,   lowercase_,   lowercase_	)
       return dataset
     else:
       raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''	)



from dataclasses import dataclass

lowerCamelCase									= '''0.1'''
lowerCamelCase									= '''Lucia Harcekova'''







@dataclass
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     int
   lowerCamelCase__			:     int
   lowerCamelCase__			:     str



   def __repr__(    self	)							->   str:






     """simple docstring"""






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







class 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_ = 13,   lowercase_ = 6	)							->   None:






     """simple docstring"""






     a__  =window_size
     a__  =lookahead_buffer_size
     a__  =self.window_size - self.lookahead_buffer_size



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   list[Token]:






     """simple docstring"""






     a__  =[]
     a__  =''''''

     # 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)
       a__  =self._find_encoding_token(lowercase_,   lowercase_	)

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

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

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

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =''''''

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

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Token:






     """simple docstring"""






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

     # Initialise result parameters to default values
     a__,				a__  =0, 0

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

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

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   int:






     """simple docstring"""






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


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

       testmod()
       # Initialize compressor class
       lowerCamelCase									= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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