




def 			a   (_lowerCAmelCase     ):
							try:
														SCREAMING_SNAKE_CASE_   	=    float(_lowerCAmelCase     )
							except ValueError:
														raise ValueError('''Please enter a valid number'''     )
							SCREAMING_SNAKE_CASE_   	=    decimal - int(_lowerCAmelCase     )
							if fractional_part == 0:
														return int(_lowerCAmelCase     ), 1
							else:
														SCREAMING_SNAKE_CASE_   	=    len(str(_lowerCAmelCase     ).split('''.'''     )[1]     )
														SCREAMING_SNAKE_CASE_   	=    int(decimal * (1_0**number_of_frac_digits)     )
														SCREAMING_SNAKE_CASE_   	=    1_0**number_of_frac_digits
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    denominator, numerator
														while True:
																					SCREAMING_SNAKE_CASE_   	=    dividend % divisor
																					if remainder == 0:
																												break
																					SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    divisor, remainder
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    numerator / divisor, denominator / divisor
														return int(_lowerCAmelCase     ), int(_lowerCAmelCase     )


if __name__ == "__main__":
			print(f"""{decimal_to_fraction(2) = }""")
			print(f"""{decimal_to_fraction(89.0) = }""")
			print(f"""{decimal_to_fraction('67') = }""")
			print(f"""{decimal_to_fraction('45.0') = }""")
			print(f"""{decimal_to_fraction(1.5) = }""")
			print(f"""{decimal_to_fraction('6.25') = }""")
			print(f"""{decimal_to_fraction('78td') = }""")








import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__SCREAMING_SNAKE_CASE 						=os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
			from run_translation import main  # noqa


set_seed(42)
__SCREAMING_SNAKE_CASE 						="""sshleifer/student_marian_en_ro_6_1"""
__SCREAMING_SNAKE_CASE 						="""sshleifer/tiny-mbart"""






@require_torch
class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   List[Any]=False		,					_lowerCamelCase:   List[Any]=None		,					_lowerCamelCase:   int=True		,					_lowerCamelCase:   Optional[int]=True		,					_lowerCamelCase:   Any=True		,					_lowerCamelCase:   Any=True		,					):
											SCREAMING_SNAKE_CASE_   	=    self.run_trainer(
											    eval_steps=1		,					max_len=12		,					model_name=_lowerCamelCase		,					num_train_epochs=1		,					distributed=_lowerCamelCase		,					extra_args_str=_lowerCamelCase		,					predict_with_generate=_lowerCamelCase		,					do_train=_lowerCamelCase		,					do_eval=_lowerCamelCase		,					do_predict=_lowerCamelCase		,					)
											SCREAMING_SNAKE_CASE_   	=    TrainerState.load_from_json(os.path.join(_lowerCamelCase		,					'''trainer_state.json'''		)		).log_history

											if not do_eval:
																		return

											SCREAMING_SNAKE_CASE_   	=    [log for log in logs if '''eval_loss''' in log.keys()]

											SCREAMING_SNAKE_CASE_   	=    eval_metrics[0]
											if predict_with_generate:
																		assert "eval_bleu" in first_step_stats

																		SCREAMING_SNAKE_CASE_   	=    eval_metrics[-1]
																		assert isinstance(last_step_stats['''eval_bleu''']		,					_lowerCamelCase		)
																		assert not math.isnan(float(last_step_stats['''eval_loss''']		)		), "eval_loss must not be `nan`"


				@require_torch_non_multi_gpu
				def     _A							(							self:   Optional[int]		):
											self.run_seqaseq_quick()


				@require_torch_multi_gpu
				def     _A							(							self:   Optional[Any]		):
											self.run_seqaseq_quick(distributed=_lowerCamelCase		)


				@require_torch_multi_gpu
				def     _A							(							self:   List[Any]		):
											self.run_seqaseq_quick(distributed=_lowerCamelCase		)


				@unittest.skip('''Requires an update of the env running those tests'''		)
				@require_torch_multi_gpu
				@require_fairscale
				def     _A							(							self:   Optional[int]		):
											self.run_seqaseq_quick(distributed=_lowerCamelCase		,					extra_args_str='''--sharded_ddp simple'''		)


				@unittest.skip('''Requires an update of the env running those tests'''		)
				@require_torch_multi_gpu
				@require_fairscale
				def     _A							(							self:   Optional[Any]		):
											self.run_seqaseq_quick(distributed=_lowerCamelCase		,					extra_args_str='''--sharded_ddp simple --fp16'''		)


				@unittest.skip('''Requires an update of the env running those tests'''		)
				@require_torch_multi_gpu
				@require_fairscale
				def     _A							(							self:   Union[str, Any]		):
											self.run_seqaseq_quick(distributed=_lowerCamelCase		,					extra_args_str='''--sharded_ddp zero_dp_2'''		,					predict_with_generate=_lowerCamelCase		)


				@unittest.skip('''Requires an update of the env running those tests'''		)
				@require_torch_multi_gpu
				@require_fairscale
				def     _A							(							self:   Optional[Any]		):
											self.run_seqaseq_quick(
											    distributed=_lowerCamelCase		,					extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''		,					predict_with_generate=_lowerCamelCase		)


				@require_apex
				@require_torch_gpu
				def     _A							(							self:   List[Any]		):
											# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
											# program and it breaks other tests that run from the same pytest worker, therefore until this is
											# sorted out it must be run only in an external program, that is distributed=True in this
											# test and only under one or more gpus - if we want cpu will need to make a special test
											#
											# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
											# 2nd main() call it botches the future eval.
											#
											self.run_seqaseq_quick(distributed=_lowerCamelCase		,					extra_args_str='''--fp16 --fp16_backend=apex'''		)
											# test 2nd time - was getting eval_loss': nan'
											# to reproduce the problem set distributed=False
											self.run_seqaseq_quick(distributed=_lowerCamelCase		,					extra_args_str='''--fp16 --fp16_backend=apex'''		)


				@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']		)
				@require_torch_multi_gpu
				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		):
											# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
											SCREAMING_SNAKE_CASE_   	=    {
											    # test with the default log_level - should be info and thus log info once
											    '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
											    # test with low log_level and log_level_replica - should be noisy on all processes
											    # now the info string should appear twice on 2 processes
											    '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
											    # test with high log_level and low log_level_replica
											    # now the info string should appear once only on the replica
											    '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
											    # test with high log_level and log_level_replica - should be quiet on all processes
											    '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
											}

											SCREAMING_SNAKE_CASE_   	=    experiments[experiment_id]
											SCREAMING_SNAKE_CASE_   	=    {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
											SCREAMING_SNAKE_CASE_   	=    '''Running training'''
											with CaptureStderr() as cl:
																		self.run_seqaseq_quick(**_lowerCamelCase		,					extra_args_str=data['''extra_args_str''']		)
											SCREAMING_SNAKE_CASE_   	=    len(re.findall(_lowerCamelCase		,					cl.err		)		)
											self.assertEqual(_lowerCamelCase		,					data['''n_matches''']		)


				@slow
				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    self.run_trainer(
											    eval_steps=2		,					max_len=1_28		,					model_name=_lowerCamelCase		,					learning_rate=3E-4		,					num_train_epochs=10		,					distributed=_lowerCamelCase		,					)

											# Check metrics
											SCREAMING_SNAKE_CASE_   	=    TrainerState.load_from_json(os.path.join(_lowerCamelCase		,					'''trainer_state.json'''		)		).log_history
											SCREAMING_SNAKE_CASE_   	=    [log for log in logs if '''eval_loss''' in log.keys()]
											SCREAMING_SNAKE_CASE_   	=    eval_metrics[0]
											SCREAMING_SNAKE_CASE_   	=    eval_metrics[-1]

											assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
											assert isinstance(last_step_stats['''eval_bleu''']		,					_lowerCamelCase		)

											# test if do_predict saves generations and metrics
											SCREAMING_SNAKE_CASE_   	=    os.listdir(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {os.path.basename(_lowerCamelCase		) for p in contents}
											assert "generated_predictions.txt" in contents
											assert "predict_results.json" in contents


				@slow
				@require_bitsandbytes
				def     _A							(							self:   Optional[int]		):
											from transformers.training_args import OptimizerNames

											def train_and_return_metrics(_lowerCamelCase:   str		) -> Tuple[int, float]:
																		SCREAMING_SNAKE_CASE_   	=    '''--skip_memory_metrics 0'''

																		SCREAMING_SNAKE_CASE_   	=    self.run_trainer(
																		    max_len=1_28		,					model_name=_lowerCamelCase		,					learning_rate=3E-4		,					num_train_epochs=1		,					optim=_lowerCamelCase		,					distributed=_lowerCamelCase		,					extra_args_str=_lowerCamelCase		,					do_eval=_lowerCamelCase		,					do_predict=_lowerCamelCase		,					n_gpus_to_use=1		,					)

																		# Check metrics
																		SCREAMING_SNAKE_CASE_   	=    TrainerState.load_from_json(Path(_lowerCamelCase		,					'''trainer_state.json'''		)		).log_history
																		SCREAMING_SNAKE_CASE_   	=    int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20		)
																		SCREAMING_SNAKE_CASE_   	=    int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20		)

																		SCREAMING_SNAKE_CASE_   	=    logs[0]['''train_loss''']
																		return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value		)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    train_and_return_metrics(OptimizerNames.ADAMW_BNB.value		)

											SCREAMING_SNAKE_CASE_   	=    gpu_alloc_mem_orig - gpu_alloc_mem_bnb

											SCREAMING_SNAKE_CASE_   	=    gpu_peak_mem_orig + gpu_alloc_mem_orig
											SCREAMING_SNAKE_CASE_   	=    gpu_peak_mem_bnb + gpu_alloc_mem_bnb
											SCREAMING_SNAKE_CASE_   	=    gpu_total_mem_orig - gpu_total_mem_bnb

											# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
											# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
											# in 2 bytes and the diff in optim memory usage is derived as so:
											#
											# - normal 25*8=~200MB (8 bytes per param)
											# - bnb    25*2= ~50MB (2 bytes per param)
											#
											# Thus we should expect ~150MB total memory saved.
											#
											# Peak memory should be the same - the total should be different by about that same margin
											#
											# After leaving a small margin to accommodate for differences between gpus let's check
											# that we have at least 120MB in savings
											SCREAMING_SNAKE_CASE_   	=    1_20

											# uncomment the following if this test starts failing - requires py38 for a new print feature
											# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
											# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
											# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
											# print(f"{gpu_alloc_mem_diff=}MB")
											# print(f"{gpu_peak_mem_diff=}MB")
											# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
											# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

											self.assertGreater(
											    _lowerCamelCase		,					_lowerCamelCase		,					'''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
											    f" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
											    f" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"		,					)

											self.assertGreater(
											    _lowerCamelCase		,					_lowerCamelCase		,					'''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
											    f" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
											    f" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"		,					)

											self.assertEqual(
											    _lowerCamelCase		,					_lowerCamelCase		,					f"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"		)





				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   float = 3E-3		,					_lowerCamelCase:   str = "adafactor"		,					_lowerCamelCase:   bool = False		,					_lowerCamelCase:   str = None		,					_lowerCamelCase:   int = 0		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   int = None		,					):
											SCREAMING_SNAKE_CASE_   	=    self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
											SCREAMING_SNAKE_CASE_   	=    self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE_   	=    f"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(_lowerCamelCase		)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(_lowerCamelCase		)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

											SCREAMING_SNAKE_CASE_   	=    f"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(_lowerCamelCase		)}\n        ".split()

											SCREAMING_SNAKE_CASE_   	=    '''
            --do_predict
        '''.split()

											SCREAMING_SNAKE_CASE_   	=    []
											if do_train:
																		args += args_train

											if do_eval:
																		args += args_eval

											if do_predict:
																		args += args_predict

											if predict_with_generate:
																		args += "--predict_with_generate".split()

											if do_train:
																		if optim == "adafactor":
																									args += "--adafactor".split()
																		else:
																									args += f"--optim {optim}".split()

											if extra_args_str is not None:
																		args += extra_args_str.split()

											if distributed:
																		if n_gpus_to_use is None:
																									SCREAMING_SNAKE_CASE_   	=    get_gpu_count()
																		SCREAMING_SNAKE_CASE_   	=    get_torch_dist_unique_port()
																		SCREAMING_SNAKE_CASE_   	=    f"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
																		SCREAMING_SNAKE_CASE_   	=    [sys.executable] + distributed_args + args
																		# keep for quick debug
																		# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
																		execute_subprocess_async(_lowerCamelCase		,					env=self.get_env()		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    ['''run_translation.py'''] + args
																		with patch.object(_lowerCamelCase		,					'''argv'''		,					_lowerCamelCase		):
																									main()

											return output_dir


