


import math
import os
import sys






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						""""""
		try:
				with open(__magic_name__       ,       """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 					UpperCamelCase   (      __magic_name__	:				dict[str, str]       ,       __magic_name__	:				str       ,       __magic_name__	:				int       ,       __magic_name__	:				str	)  ->    None:
		"""simple docstring"""




		lexicon.pop(__magic_name__	)
		lowercase__	     =						last_match_id

		if math.loga(__magic_name__	).is_integer():
				for curr_key in lexicon:
						lowercase__	     =						"""0""" + lexicon[curr_key]

		lowercase__	     =						bin(__magic_name__	)[2:]






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						{"""0""": """0""", """1""": """1"""}
		lowercase__     ,      lowercase__	     =						"""""", """"""
		lowercase__	     =						len(__magic_name__	)

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

				lowercase__	     =						lexicon[curr_string]
				result += last_match_id
				add_key_to_lexicon(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)
				index += 1
				lowercase__	     =						""""""

		while curr_string != "" and curr_string not in lexicon:
				curr_string += "0"

		if curr_string != "":
				lowercase__	     =						lexicon[curr_string]
				result += last_match_id

		return result






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						os.path.getsize(__magic_name__	)
		lowercase__	     =						bin(__magic_name__	)[2:]
		lowercase__	     =						len(__magic_name__	)

		return "0" * (length_length - 1) + file_length_binary + compressed






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str	)  ->    None:
		"""simple docstring"""




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

						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:
								opened_file.write(int(__magic_name__       ,       2	).to_bytes(1       ,       byteorder="""big"""	)	)
		except OSError:
				print("""File not accessible"""	)
				sys.exit()






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str	)  ->    None:
		"""simple docstring"""




		lowercase__	     =						read_file_binary(__magic_name__	)
		lowercase__	     =						compress_data(__magic_name__	)
		lowercase__	     =						add_file_length(__magic_name__       ,       __magic_name__	)
		write_file_binary(__magic_name__       ,       __magic_name__	)


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





from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
	import datasets



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@slow
					@require_torch
					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,   """prajjwal1/bert-tiny"""						)
							lowercase__	     =						BertTokenizer.from_pretrained("""bert-base-uncased"""						)

							lowercase__	     =						bertabert.config.encoder.vocab_size
							lowercase__	     =						tokenizer.sep_token_id
							lowercase__	     =						tokenizer.cls_token_id
							lowercase__	     =						128

							lowercase__	     =						datasets.load_dataset("""cnn_dailymail""" ,   """3.0.0""" ,   split="""train[:1%]"""						)
							lowercase__	     =						datasets.load_dataset("""cnn_dailymail""" ,   """3.0.0""" ,   split="""validation[:1%]"""						)

							lowercase__	     =						train_dataset.select(range(32						)						)
							lowercase__	     =						val_dataset.select(range(16						)						)

							lowercase__	     =						4

							def _map_to_encoder_decoder_inputs(_UpperCAmelCase       :		Any						):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									lowercase__	     =						tokenizer(batch["""article"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   max_length=512						)
									lowercase__	     =						tokenizer(batch["""highlights"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   max_length=128						)
									lowercase__	     =						inputs.input_ids
									lowercase__	     =						inputs.attention_mask

									lowercase__	     =						outputs.input_ids
									lowercase__	     =						outputs.input_ids.copy()
									lowercase__	     =						[
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
									]
									lowercase__	     =						outputs.attention_mask

									assert all(len(_UpperCAmelCase						) == 512 for x in inputs.input_ids						)
									assert all(len(_UpperCAmelCase						) == 128 for x in outputs.input_ids						)

									return batch

							def _compute_metrics(_UpperCAmelCase       :		List[Any]						):
									lowercase__	     =						pred.label_ids
									lowercase__	     =						pred.predictions

									# all unnecessary tokens are removed
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)

									lowercase__	     =						sum([int(pred_str[i] == label_str[i]						) for i in range(len(_UpperCAmelCase						)						)]						) / len(_UpperCAmelCase						)

									return {"accuracy": accuracy}

							# map train dataset
							lowercase__	     =						train_dataset.map(
							    _map_to_encoder_decoder_inputs ,   batched=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   remove_columns=["""article""", """highlights"""] ,   )
							train_dataset.set_format(
							    type="""torch""" ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,   )

							# same for validation dataset
							lowercase__	     =						val_dataset.map(
							    _map_to_encoder_decoder_inputs ,   batched=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   remove_columns=["""article""", """highlights"""] ,   )
							val_dataset.set_format(
							    type="""torch""" ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,   )

							lowercase__	     =						self.get_auto_remove_tmp_dir()

							lowercase__	     =						SeqaSeqTrainingArguments(
							    output_dir=_UpperCAmelCase ,   per_device_train_batch_size=_UpperCAmelCase ,   per_device_eval_batch_size=_UpperCAmelCase ,   predict_with_generate=_UpperCAmelCase ,   evaluation_strategy="""steps""" ,   do_train=_UpperCAmelCase ,   do_eval=_UpperCAmelCase ,   warmup_steps=0 ,   eval_steps=2 ,   logging_steps=2 ,   )

							# instantiate trainer
							lowercase__	     =						SeqaSeqTrainer(
							    model=_UpperCAmelCase ,   args=_UpperCAmelCase ,   compute_metrics=_compute_metrics ,   train_dataset=_UpperCAmelCase ,   eval_dataset=_UpperCAmelCase ,   tokenizer=_UpperCAmelCase ,   )

							# start training
							trainer.train()

