



"""simple docstring"""




import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , *SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , **SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



											"""simple docstring"""




											super().__init__(*SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: str     =     eval_examples
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     post_process_function



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = "eval"     , **SCREAMING_SNAKE_CASE__     , )      ->	Dict[str, float]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     gen_kwargs.copy()
											SCREAMING_SNAKE_CASE__		: List[Any]     =     (
											    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""					) is not None else self.args.generation_max_length
											)
											SCREAMING_SNAKE_CASE__		: int     =     (
											    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
											)
											SCREAMING_SNAKE_CASE__		: int     =     gen_kwargs

											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.eval_dataset if eval_dataset is None else eval_dataset
											SCREAMING_SNAKE_CASE__		: Dict     =     self.get_eval_dataloader(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: str     =     self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											SCREAMING_SNAKE_CASE__		: int     =     self.compute_metrics
											SCREAMING_SNAKE_CASE__		: str     =     None
											SCREAMING_SNAKE_CASE__		: Tuple     =     time.time()
											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																		SCREAMING_SNAKE_CASE__		: Tuple     =     eval_loop(
																		    SCREAMING_SNAKE_CASE__     , description="""Evaluation"""     , prediction_loss_only=True if compute_metrics is None else None     , ignore_keys=SCREAMING_SNAKE_CASE__     , metric_key_prefix=SCREAMING_SNAKE_CASE__     , )
											finally:
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     compute_metrics
											SCREAMING_SNAKE_CASE__		: List[str]     =     self.args.eval_batch_size * self.args.world_size
											if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																		start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
											output.metrics.update(
											    speed_metrics(
											        SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , num_samples=output.num_samples     , num_steps=math.ceil(output.num_samples / total_batch_size					)     , )					)

											if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
																		# Only the main node write the results by default
																		SCREAMING_SNAKE_CASE__		: str     =     self.post_process_function(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
																		SCREAMING_SNAKE_CASE__		: int     =     self.compute_metrics(SCREAMING_SNAKE_CASE__					)

																		# Prefix all keys with metric_key_prefix + '_'
																		for key in list(metrics.keys()					):
																									if not key.startswith(F'''{metric_key_prefix}_'''					):
																																SCREAMING_SNAKE_CASE__		: Optional[int]     =     metrics.pop(SCREAMING_SNAKE_CASE__					)

																		metrics.update(output.metrics					)
											else:
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     output.metrics

											if self.args.should_log:
																		# Only the main node log the results by default
																		self.log(SCREAMING_SNAKE_CASE__					)

											if self.args.tpu_metrics_debug or self.args.debug:
																		# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																		xm.master_print(met.metrics_report()					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.callback_handler.on_evaluate(self.args     , self.state     , self.control     , SCREAMING_SNAKE_CASE__					)
											return metrics



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__ = "test"     , **SCREAMING_SNAKE_CASE__					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     gen_kwargs.copy()

											SCREAMING_SNAKE_CASE__		: str     =     self.get_test_dataloader(SCREAMING_SNAKE_CASE__					)

											# Temporarily disable metric computation, we will do it in the loop here.
											SCREAMING_SNAKE_CASE__		: List[str]     =     self.compute_metrics
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     None
											SCREAMING_SNAKE_CASE__		: str     =     time.time()
											SCREAMING_SNAKE_CASE__		: Any     =     self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     eval_loop(
																		    SCREAMING_SNAKE_CASE__     , description="""Prediction"""     , prediction_loss_only=True if compute_metrics is None else None     , ignore_keys=SCREAMING_SNAKE_CASE__     , metric_key_prefix=SCREAMING_SNAKE_CASE__     , )
											finally:
																		SCREAMING_SNAKE_CASE__		: List[str]     =     compute_metrics

											SCREAMING_SNAKE_CASE__		: Tuple     =     self.args.eval_batch_size * self.args.world_size
											if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
																		start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
											output.metrics.update(
											    speed_metrics(
											        SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , num_samples=output.num_samples     , num_steps=math.ceil(output.num_samples / total_batch_size					)     , )					)
											if self.post_process_function is None or self.compute_metrics is None:
																		return output

											SCREAMING_SNAKE_CASE__		: Any     =     self.post_process_function(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , """predict"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     self.compute_metrics(SCREAMING_SNAKE_CASE__					)

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()					):
																		if not key.startswith(F'''{metric_key_prefix}_'''					):
																									SCREAMING_SNAKE_CASE__		: Dict     =     metrics.pop(SCREAMING_SNAKE_CASE__					)
											metrics.update(output.metrics					)
											return PredictionOutput(predictions=predictions.predictions     , label_ids=predictions.label_ids     , metrics=SCREAMING_SNAKE_CASE__					)






"""simple docstring"""




import math
import os
import sys

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     """"""
							try:
														with open(_snake_case     ,"""rb"""		) as binary_file:
																					SCREAMING_SNAKE_CASE__		: str     =     binary_file.read()
														for dat in data:
																					SCREAMING_SNAKE_CASE__		: int     =     f'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							lexicon.pop(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     last_match_id

							if math.loga(_snake_case		).is_integer():
														for curr_key in lexicon:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     """0""" + lexicon[curr_key]

							SCREAMING_SNAKE_CASE__		: Tuple     =     bin(_snake_case		)[2:]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     {"""0""": """0""", """1""": """1"""}
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: str     =     """""", """"""
							SCREAMING_SNAKE_CASE__		: int     =     len(_snake_case		)

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

														SCREAMING_SNAKE_CASE__		: Dict     =     lexicon[curr_string]
														result += last_match_id
														add_key_to_lexicon(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)
														index += 1
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """"""

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

							if curr_string != "":
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     lexicon[curr_string]
														result += last_match_id

							return result

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     os.path.getsize(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     bin(_snake_case		)[2:]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     len(_snake_case		)

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

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     8
							try:
														with open(_snake_case     ,"""wb"""		) as opened_file:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     [
																					    to_write[i : i + byte_length]
																					    for i in range(0     ,len(_snake_case		)     ,_snake_case		)
																					]

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

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     read_file_binary(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     compress_data(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     add_file_length(_snake_case     ,_snake_case		)
							write_file_binary(_snake_case     ,_snake_case		)


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




