'''simple docstring'''




from dataclasses import dataclass

__SCREAMING_SNAKE_CASE	:					Tuple					=  """0.1"""
__SCREAMING_SNAKE_CASE	:					Optional[Any]					=  """Lucia Harcekova"""

@dataclass
class      lowerCamelCase_     :
      '''simple docstring'''

      __UpperCamelCase:  int
      __UpperCamelCase:  int
      __UpperCamelCase:  str




      def __repr__(	self						:    Tuple	):
             return F"""({self.offset}, {self.length}, {self.indicator})"""

class      lowerCamelCase_     :
      '''simple docstring'''

      def __init__(	self						:    int						, A						:    int = 13						, A						:    List[str] = 6	):
             _UpperCAmelCase					:  Tuple											=   window_size
             _UpperCAmelCase					:  Tuple											=   lookahead_buffer_size
             _UpperCAmelCase					:  Any											=   self.window_size - self.lookahead_buffer_size




      def 					_A     (	self						:    List[str]						, A						:    List[Any]	):
             _UpperCAmelCase					:  List[str]											=   []
             _UpperCAmelCase					:  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)
                    _UpperCAmelCase					:  List[str]											=   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:
                           _UpperCAmelCase					:  List[Any]											=   search_buffer[-self.search_buffer_size :]

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

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

             return output




      def 					_A     (	self						:    Tuple						, A						:    List[str]	):
             _UpperCAmelCase					:  List[str]											=   ""

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

             return output




      def 					_A     (	self						:    Tuple						, A						:    Any						, A						:    Dict	):






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

             # Initialise result parameters to default values
             _UpperCAmelCase				,  _UpperCAmelCase					:  List[str]											=   0, 0

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

             for i, character in enumerate(lowercase_	):
                    _UpperCAmelCase					:  Tuple											=   len(lowercase_	) - i
                    if character == text[0]:
                           _UpperCAmelCase					:  Any											=   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:
                                  _UpperCAmelCase				,  _UpperCAmelCase					:  Any											=   found_offset, found_length

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





      def 					_A     (	self						:    List[Any]						, A						:    Any						, A						:    Optional[int]						, A						:    List[Any]						, A						:    Optional[int]	):






             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
  __SCREAMING_SNAKE_CASE	:					int					=  LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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


'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class      lowerCamelCase_     (unittest.TestCase   ):
      '''simple docstring'''

      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   tempfile.mkdtemp()

             _UpperCAmelCase					:  Any											=   CLIPImageProcessor()
             _UpperCAmelCase					:  Optional[int]											=   BertTokenizer.from_pretrained(
                 "hf-internal-testing/tiny-random-BertModel"						, model_input_names=["input_ids", "attention_mask"]	)

             _UpperCAmelCase					:  Dict											=   GitProcessor(A						, A	)

             processor.save_pretrained(self.tmpdirname	)




      def 					_A     (	self						:    Dict						, **A						:    Tuple	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).tokenizer




      def 					_A     (	self						:    List[str]						, **A						:    str	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).image_processor




      def 					_A     (	self						:    Optional[int]	):
             shutil.rmtree(self.tmpdirname	)




      def 					_A     (	self						:    Tuple	):
             _UpperCAmelCase					:  Dict											=   [np.random.randint(255						, size=(3, 30, 400)						, dtype=np.uinta	)]

             _UpperCAmelCase					:  Optional[Any]											=   [Image.fromarray(np.moveaxis(A						, 0						, -1	)	) for x in image_inputs]

             return image_inputs




      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()	)
             processor.save_pretrained(self.tmpdirname	)

             _UpperCAmelCase					:  Dict											=   self.get_tokenizer(bos_token="(BOS)"						, eos_token="(EOS)"	)
             _UpperCAmelCase					:  Optional[Any]											=   self.get_image_processor(do_normalize=A						, padding_value=1.0	)

             _UpperCAmelCase					:  Union[str, Any]											=   GitProcessor.from_pretrained(
                 self.tmpdirname						, bos_token="(BOS)"						, eos_token="(EOS)"						, do_normalize=A						, padding_value=1.0	)

             self.assertEqual(processor.tokenizer.get_vocab()						, tokenizer_add_kwargs.get_vocab()	)
             self.assertIsInstance(processor.tokenizer						, A	)

             self.assertEqual(processor.image_processor.to_json_string()						, image_processor_add_kwargs.to_json_string()	)
             self.assertIsInstance(processor.image_processor						, A	)




      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  int											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[Any]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Optional[Any]											=   image_processor(A						, return_tensors="np"	)
             _UpperCAmelCase					:  int											=   processor(images=A						, return_tensors="np"	)

             for key in input_feat_extract.keys():
                    self.assertAlmostEqual(input_feat_extract[key].sum()						, input_processor[key].sum()						, delta=1E-2	)




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  str											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[Any]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   "lower newer"

             _UpperCAmelCase					:  Optional[int]											=   processor(text=A	)

             _UpperCAmelCase					:  Tuple											=   tokenizer(A						, return_token_type_ids=A	)

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]						, encoded_processor[key]	)




      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  Any											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Optional[Any]											=   "lower newer"
             _UpperCAmelCase					:  Optional[int]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Any											=   processor(text=A						, images=A	)

             self.assertListEqual(list(inputs.keys()	)						, ["input_ids", "attention_mask", "pixel_values"]	)

             # test if it raises when no input is passed
             with pytest.raises(A	):
                    processor()




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  List[str]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[str]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _UpperCAmelCase					:  Dict											=   processor.batch_decode(A	)
             _UpperCAmelCase					:  Dict											=   tokenizer.batch_decode(A	)

             self.assertListEqual(A						, A	)





      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Dict											=   "lower newer"
             _UpperCAmelCase					:  Tuple											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Dict											=   processor(text=A						, images=A	)

             # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
             self.assertListEqual(list(inputs.keys()	)						, ["input_ids", "attention_mask", "pixel_values"]	)

