


'''simple docstring'''

import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
							from tokenizers import Tokenizer
							from tokenizers.models import WordPiece







class  UpperCAmelCase (       unittest.TestCase):

	def 		lowercase_						( self :	Tuple,					a_ :	Union[str, Any]					):





				"""simple docstring"""



				UpperCamelCase__ 						=						list(tokenizer_class.max_model_input_sizes.keys()					)
				for model_name in sa_models[:1]:
							UpperCamelCase__ 						=						tokenizer_class.from_pretrained(a_					)
							self.assertIsNotNone(a_					)
							self.assertIsInstance(a_,					a_					)
							self.assertIsInstance(a_,					a_					)

							for special_tok in tokenizer.all_special_tokens:
										self.assertIsInstance(a_,					a_					)
										UpperCamelCase__ 						=						tokenizer.convert_tokens_to_ids(a_					)
										self.assertIsInstance(a_,					a_					)

	def 		lowercase_						( self :	Union[str, Any],					a_ :	BatchEncoding,					a_ :	Optional[Callable] = None					):





				"""simple docstring"""



				UpperCamelCase__ 						=						pickle.dumps(a_					)
				self.assertIsNotNone(a_					)

				UpperCamelCase__ 						=						pickle.loads(a_					)

				# Ensure is_fast is correctly restored
				self.assertEqual(be_restored.is_fast,					be_original.is_fast					)

				# Ensure encodings are potentially correctly restored
				if be_original.is_fast:
							self.assertIsNotNone(be_restored.encodings					)
				else:
							self.assertIsNone(be_restored.encodings					)

				# Ensure the keys are the same
				for original_v, restored_v in zip(be_original.values(),					be_restored.values()					):
							if equal_op:
										self.assertTrue(equal_op(a_,					a_					)					)
							else:
										self.assertEqual(a_,					a_					)

	@slow
	def 		lowercase_						( self :	Optional[int]					):





				"""simple docstring"""



				self.check_tokenizer_from_pretrained(a_					)

	def 		lowercase_						( self :	List[str]					):





				"""simple docstring"""



				self.assertEqual(TensorType("tf"					),					TensorType.TENSORFLOW					)
				self.assertEqual(TensorType("pt"					),					TensorType.PYTORCH					)
				self.assertEqual(TensorType("np"					),					TensorType.NUMPY					)

	@require_tokenizers
	def 		lowercase_						( self :	List[Any]					):





				"""simple docstring"""



				import numpy as np

				UpperCamelCase__ 						=						BertTokenizer.from_pretrained("bert-base-cased"					)
				UpperCamelCase__ 						=						BertTokenizerFast.from_pretrained("bert-base-cased"					)

				# Python no tensor
				with self.subTest("BatchEncoding (Python, return_tensors=None)"					):
							self.assert_dump_and_restore(tokenizer_p("Small example to encode"					)					)

				with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"					):
							self.assert_dump_and_restore(
							    tokenizer_p("Small example to encode",					return_tensors=TensorType.NUMPY					),					np.array_equal					)

				with self.subTest("BatchEncoding (Rust, return_tensors=None)"					):
							self.assert_dump_and_restore(tokenizer_r("Small example to encode"					)					)

				with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"					):
							self.assert_dump_and_restore(
							    tokenizer_r("Small example to encode",					return_tensors=TensorType.NUMPY					),					np.array_equal					)

	@require_tf
	@require_tokenizers
	def 		lowercase_						( self :	str					):





				"""simple docstring"""



				import tensorflow as tf

				def tf_array_equals(a_ :	Tuple,					a_ :	str					):
							return tf.reduce_all(tf.equal(a_,					a_					)					)

				UpperCamelCase__ 						=						BertTokenizer.from_pretrained("bert-base-cased"					)
				UpperCamelCase__ 						=						BertTokenizerFast.from_pretrained("bert-base-cased"					)

				with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"					):
							self.assert_dump_and_restore(
							    tokenizer_p("Small example to encode",					return_tensors=TensorType.TENSORFLOW					),					a_					)

				with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"					):
							self.assert_dump_and_restore(
							    tokenizer_r("Small example to encode",					return_tensors=TensorType.TENSORFLOW					),					a_					)

	@require_torch
	@require_tokenizers
	def 		lowercase_						( self :	List[str]					):





				"""simple docstring"""



				import torch

				UpperCamelCase__ 						=						BertTokenizer.from_pretrained("bert-base-cased"					)
				UpperCamelCase__ 						=						BertTokenizerFast.from_pretrained("bert-base-cased"					)

				with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"					):
							self.assert_dump_and_restore(
							    tokenizer_p("Small example to encode",					return_tensors=TensorType.PYTORCH					),					torch.equal					)

				with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"					):
							self.assert_dump_and_restore(
							    tokenizer_r("Small example to encode",					return_tensors=TensorType.PYTORCH					),					torch.equal					)

	@require_tokenizers
	def 		lowercase_						( self :	Optional[int]					):





				"""simple docstring"""



				UpperCamelCase__ 						=						BertTokenizer.from_pretrained("bert-base-cased"					)
				UpperCamelCase__ 						=						BertTokenizerFast.from_pretrained("bert-base-cased"					)

				with self.subTest("Python Tokenizer"					):
							self.assertFalse(tokenizer_p("Small example to_encode"					).is_fast					)

				with self.subTest("Rust Tokenizer"					):
							self.assertTrue(tokenizer_r("Small example to_encode"					).is_fast					)

	@require_tokenizers
	def 		lowercase_						( self :	str					):





				"""simple docstring"""



				UpperCamelCase__ 						=						BertTokenizerFast.from_pretrained("bert-base-cased"					)
				UpperCamelCase__ 						=						tokenizer_r(["Test", "\xad", "test"],					is_split_into_words=a_					)

				self.assertEqual(encoded.word_to_tokens(0					),					TokenSpan(start=1,					end=2					)					)
				self.assertEqual(encoded.word_to_tokens(1					),					a_					)
				self.assertEqual(encoded.word_to_tokens(2					),					TokenSpan(start=2,					end=3					)					)

	def 		lowercase_						( self :	Optional[int]					):





				"""simple docstring"""



				UpperCamelCase__ 						=						BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
				UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="np"					)
				self.assertEqual(tensor_batch["inputs"].shape,					(2, 3)					)
				self.assertEqual(tensor_batch["labels"].shape,					(2,)					)
				# test converting the converted
				with CaptureStderr() as cs:
							UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="np"					)
				self.assertFalse(len(cs.err					),					msg=f'should have no warning, but got {cs.err}'					)

				UpperCamelCase__ 						=						BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
				UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="np",					prepend_batch_axis=a_					)
				self.assertEqual(tensor_batch["inputs"].shape,					(1, 3)					)
				self.assertEqual(tensor_batch["labels"].shape,					(1,)					)

	@require_torch
	def 		lowercase_						( self :	List[str]					):





				"""simple docstring"""



				UpperCamelCase__ 						=						BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
				UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="pt"					)
				self.assertEqual(tensor_batch["inputs"].shape,					(2, 3)					)
				self.assertEqual(tensor_batch["labels"].shape,					(2,)					)
				# test converting the converted
				with CaptureStderr() as cs:
							UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="pt"					)
				self.assertFalse(len(cs.err					),					msg=f'should have no warning, but got {cs.err}'					)

				UpperCamelCase__ 						=						BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
				UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="pt",					prepend_batch_axis=a_					)
				self.assertEqual(tensor_batch["inputs"].shape,					(1, 3)					)
				self.assertEqual(tensor_batch["labels"].shape,					(1,)					)

	@require_tf
	def 		lowercase_						( self :	Optional[Any]					):





				"""simple docstring"""



				UpperCamelCase__ 						=						BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
				UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="tf"					)
				self.assertEqual(tensor_batch["inputs"].shape,					(2, 3)					)
				self.assertEqual(tensor_batch["labels"].shape,					(2,)					)
				# test converting the converted
				with CaptureStderr() as cs:
							UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="tf"					)
				self.assertFalse(len(cs.err					),					msg=f'should have no warning, but got {cs.err}'					)

				UpperCamelCase__ 						=						BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
				UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="tf",					prepend_batch_axis=a_					)
				self.assertEqual(tensor_batch["inputs"].shape,					(1, 3)					)
				self.assertEqual(tensor_batch["labels"].shape,					(1,)					)

	@require_flax
	def 		lowercase_						( self :	List[str]					):





				"""simple docstring"""



				UpperCamelCase__ 						=						BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
				UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="jax"					)
				self.assertEqual(tensor_batch["inputs"].shape,					(2, 3)					)
				self.assertEqual(tensor_batch["labels"].shape,					(2,)					)
				# test converting the converted
				with CaptureStderr() as cs:
							UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="jax"					)
				self.assertFalse(len(cs.err					),					msg=f'should have no warning, but got {cs.err}'					)

				UpperCamelCase__ 						=						BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
				UpperCamelCase__ 						=						batch.convert_to_tensors(tensor_type="jax",					prepend_batch_axis=a_					)
				self.assertEqual(tensor_batch["inputs"].shape,					(1, 3)					)
				self.assertEqual(tensor_batch["labels"].shape,					(1,)					)

	def 		lowercase_						( self :	List[Any]					):





				"""simple docstring"""



				UpperCamelCase__ 						=						[{"input_ids": np.array([0, 1, 2]					)}, {"input_ids": np.array([0, 1, 2, 3]					)}]
				UpperCamelCase__ 						=						BertTokenizer.from_pretrained("bert-base-cased"					)

				UpperCamelCase__ 						=						tokenizer.pad(a_,					padding=a_					)
				self.assertTrue(isinstance(batch["input_ids"],					np.ndarray					)					)
				self.assertEqual(batch["input_ids"].tolist(),					[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
				UpperCamelCase__ 						=						tokenizer.pad(a_,					padding=a_,					return_tensors="np"					)
				self.assertTrue(isinstance(batch["input_ids"],					np.ndarray					)					)
				self.assertEqual(batch["input_ids"].tolist(),					[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)

	@require_torch
	def 		lowercase_						( self :	List[Any]					):





				"""simple docstring"""



				import torch

				UpperCamelCase__ 						=						[{"input_ids": torch.tensor([0, 1, 2]					)}, {"input_ids": torch.tensor([0, 1, 2, 3]					)}]
				UpperCamelCase__ 						=						BertTokenizer.from_pretrained("bert-base-cased"					)

				UpperCamelCase__ 						=						tokenizer.pad(a_,					padding=a_					)
				self.assertTrue(isinstance(batch["input_ids"],					torch.Tensor					)					)
				self.assertEqual(batch["input_ids"].tolist(),					[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
				UpperCamelCase__ 						=						tokenizer.pad(a_,					padding=a_,					return_tensors="pt"					)
				self.assertTrue(isinstance(batch["input_ids"],					torch.Tensor					)					)
				self.assertEqual(batch["input_ids"].tolist(),					[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)

	@require_tf
	def 		lowercase_						( self :	int					):





				"""simple docstring"""



				import tensorflow as tf

				UpperCamelCase__ 						=						[{"input_ids": tf.constant([0, 1, 2]					)}, {"input_ids": tf.constant([0, 1, 2, 3]					)}]
				UpperCamelCase__ 						=						BertTokenizer.from_pretrained("bert-base-cased"					)

				UpperCamelCase__ 						=						tokenizer.pad(a_,					padding=a_					)
				self.assertTrue(isinstance(batch["input_ids"],					tf.Tensor					)					)
				self.assertEqual(batch["input_ids"].numpy().tolist(),					[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)
				UpperCamelCase__ 						=						tokenizer.pad(a_,					padding=a_,					return_tensors="tf"					)
				self.assertTrue(isinstance(batch["input_ids"],					tf.Tensor					)					)
				self.assertEqual(batch["input_ids"].numpy().tolist(),					[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]					)

	@require_tokenizers
	def 		lowercase_						( self :	List[str]					):





				"""simple docstring"""



				UpperCamelCase__ 						=						Tokenizer(WordPiece(unk_token="[UNK]"					)					)
				PreTrainedTokenizerFast(tokenizer_object=a_					)

	@require_tokenizers
	def 		lowercase_						( self :	Optional[int]					):





				"""simple docstring"""



				UpperCamelCase__ 						=						Tokenizer(WordPiece(unk_token="[UNK]"					)					)
				with tempfile.TemporaryDirectory() as tmpdirname:
							bert_tokenizer.save(os.path.join(a_,					"tokenizer.json"					)					)
							PreTrainedTokenizerFast(tokenizer_file=os.path.join(a_,					"tokenizer.json"					)					)



'''simple docstring'''

from dataclasses import dataclass

__lowercase:		List[str]							=     "0.1"
__lowercase:		Union[str, Any]							=     "Lucia Harcekova"







@dataclass
class  UpperCAmelCase :
 _lowerCamelCase :					int
 _lowerCamelCase :					int
 _lowerCamelCase :					str

 def __repr__( self :	List[str]					):





    """simple docstring"""



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







class  UpperCAmelCase :

 def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):





    """simple docstring"""



    UpperCamelCase__ 						=						window_size
    UpperCamelCase__ 						=						lookahead_buffer_size
    UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size

 def 		lowercase_						( self :	Optional[int],					a_ :	str					):





    """simple docstring"""



    UpperCamelCase__ 						=						[]
    UpperCamelCase__ 						=						""

    # 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__ 						=						self._find_encoding_token(a_,					a_					)

       # 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(a_					) > self.search_buffer_size:
          UpperCamelCase__ 						=						search_buffer[-self.search_buffer_size :]

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

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

    return output

 def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):





    """simple docstring"""



    UpperCamelCase__ 						=						""

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

    return output

 def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):





    """simple docstring"""



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

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

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

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

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

 def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					a_ :	int					):





    """simple docstring"""



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


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

       testmod()
       # Initialize compressor class
       __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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