


from dataclasses import dataclass

SCREAMING_SNAKE_CASE__       			=				"""0.1"""
SCREAMING_SNAKE_CASE__       			=				"""Lucia Harcekova"""







@dataclass
class     __lowerCamelCase     :


							"""simple docstring"""

							lowerCAmelCase__          =				42
							lowerCAmelCase__          =				42
							lowerCAmelCase__          =				42




							def __repr__(  self  )     ->       str:






								'''simple docstring'''






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







class     __lowerCamelCase     :


							"""simple docstring"""

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






								'''simple docstring'''






								lowercase_			 = window_size
								lowercase_			 = lookahead_buffer_size
								lowercase_			 = self.window_size - self.lookahead_buffer_size




							def 				A__      (  self						,       UpperCAmelCase  )     ->       list[Token]:






								'''simple docstring'''






								lowercase_			 = []
								lowercase_			 = ""

								# 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_			 = self._find_encoding_token(UpperCAmelCase						,       UpperCAmelCase  )

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

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

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

								return output




							def 				A__      (  self						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






								lowercase_			 = ""

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

								return output




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Token:






								'''simple docstring'''






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

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

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

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

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




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       int:






								'''simple docstring'''






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


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

							testmod()
							# Initialize compressor class
							SCREAMING_SNAKE_CASE__       			=				LZaaCompressor(window_size=1_3, lookahead_buffer_size=6)

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





import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				argparse.ArgumentParser()

							# Hyperparameters sent by the client are passed as command-line arguments to the script.
							parser.add_argument("""--epochs""", type=int, default=1)
							parser.add_argument("""--per_device_train_batch_size""", type=int, default=1_6)
							parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
							parser.add_argument("""--model_name_or_path""", type=str)
							parser.add_argument("""--learning_rate""", type=str, default=5E-5)
							parser.add_argument("""--do_train""", type=bool, default=True)
							parser.add_argument("""--do_eval""", type=bool, default=True)
							parser.add_argument("""--output_dir""", type=str)

							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							SCREAMING_SNAKE_CASE__       			=				1_6
							SCREAMING_SNAKE_CASE__       			=				1_6

							# Set up logging
							SCREAMING_SNAKE_CASE__       			=				logging.getLogger(__name__)

							logging.basicConfig(
							    level=logging.getLevelName("""INFO"""),
							    handlers=[logging.StreamHandler(sys.stdout)],
							    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
							)

							# Load model and tokenizer
							SCREAMING_SNAKE_CASE__       			=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							SCREAMING_SNAKE_CASE__       			=				AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				load_dataset("""imdb""", split=["""train""", """test"""])
							SCREAMING_SNAKE_CASE__       			=				train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
							SCREAMING_SNAKE_CASE__       			=				test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							SCREAMING_SNAKE_CASE__       			=				train_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							SCREAMING_SNAKE_CASE__       			=				{
							    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							SCREAMING_SNAKE_CASE__       			=				tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
							    args.per_device_train_batch_size
							)

							# Preprocess test dataset
							SCREAMING_SNAKE_CASE__       			=				test_dataset.map(
							    lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
							)
							test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

							SCREAMING_SNAKE_CASE__       			=				{
							    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
							    for x in ["""input_ids""", """attention_mask"""]
							}
							SCREAMING_SNAKE_CASE__       			=				tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
							    args.per_device_eval_batch_size
							)

							# fine optimizer and loss
							SCREAMING_SNAKE_CASE__       			=				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
							SCREAMING_SNAKE_CASE__       			=				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
							SCREAMING_SNAKE_CASE__       			=				[tf.keras.metrics.SparseCategoricalAccuracy()]
							model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

							SCREAMING_SNAKE_CASE__       			=				time.time()
							SCREAMING_SNAKE_CASE__       			=				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							SCREAMING_SNAKE_CASE__       			=				time.time() - start_train_time

							logger.info("""*** Train ***""")
							logger.info(f"""train_runtime = {end_train_time}""")
							for key, value in train_results.history.items():
														logger.info(f"""  {key} = {value}""")

