






import math
import sys
def 	snake_case__       (					__lowercase       )		->		str:







						"""simple docstring"""

						A__	:							List[str]						    = ""
						try:
												with open(__lowercase							,     "rb"       ) as binary_file:
																		A__	:							int						    = binary_file.read()
												for dat in data:
																		A__	:							Dict						    = F'{dat:08b}'
																		result += curr_byte
												return result
						except OSError:
												print("File not accessible"       )
												sys.exit()
def 	snake_case__       (					__lowercase       )		->		str:







						"""simple docstring"""

						A__	:							Optional[Any]						    = {"0": "0", "1": "1"}
						A__    ,       A__	:							Dict						    = "", ""
						A__	:							Any						    = len(__lowercase       )

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

												A__	:							List[str]						    = lexicon[curr_string]
												result += last_match_id
												A__	:							Tuple						    = last_match_id + "0"

												if math.loga(__lowercase       ).is_integer():
																		A__	:							Dict						    = {}
																		for curr_key in list(__lowercase       ):
																								A__	:							Tuple						    = lexicon.pop(__lowercase       )
																		A__	:							Tuple						    = new_lex

												A__	:							List[Any]						    = last_match_id + "1"
												index += 1
												A__	:							Dict						    = ""
						return result
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		None:







						"""simple docstring"""

						A__	:							List[Any]						    = 8
						try:
												with open(__lowercase							,     "wb"       ) as opened_file:
																		A__	:							Tuple						    = [
																		    to_write[i : i + byte_length]
																		    for i in range(0							,     len(__lowercase       )							,     __lowercase       )
																		]

																		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[:-1]:
																								opened_file.write(int(__lowercase							,     2       ).to_bytes(1							,     byteorder="big"       )       )
						except OSError:
												print("File not accessible"       )
												sys.exit()
def 	snake_case__       (					__lowercase       )		->		str:







						"""simple docstring"""

						A__	:							List[str]						    = 0
						for letter in data_bits:
												if letter == "1":
																		break
												counter += 1

						A__	:							Dict						    = data_bits[counter:]
						A__	:							Dict						    = data_bits[counter + 1 :]
						return data_bits
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		None:







						"""simple docstring"""

						A__	:							str						    = read_file_binary(__lowercase       )
						A__	:							int						    = remove_prefix(__lowercase       )
						A__	:							List[str]						    = decompress_data(__lowercase       )
						write_file_binary(__lowercase							,     __lowercase       )


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




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 							lowerCAmelCase__  (	unittest.TestCase						):







					@slow
					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"			,		return_dict=_A).to(_A)
											A__	:							Any						    = AutoTokenizer.from_pretrained("google/mt5-small")

											A__	:							int						    = tokenizer("Hello there"			,		return_tensors="pt").input_ids
											A__	:							List[str]						    = tokenizer("Hi I am"			,		return_tensors="pt").input_ids

											A__	:							int						    = model(input_ids.to(_A)			,		labels=labels.to(_A)).loss
											A__	:							Optional[int]						    = -(labels.shape[-1] * loss.item())

											A__	:							List[str]						    = -84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)