


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


A       :		Optional[int]    =   os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
	from run_translation import main  # noqa


set_seed(4_2)
A       :		Any    =   "sshleifer/student_marian_en_ro_6_1"
A       :		Tuple    =   "sshleifer/tiny-mbart"



@require_torch
class 							A    (       __lowercase							):
					'''simple docstring'''






					def   lowerCamelCase__     (self    :						List[Any]      ,					_UpperCAmelCase    :						Tuple=False      ,					_UpperCAmelCase    :						Optional[Any]=None      ,					_UpperCAmelCase    :						Optional[Any]=True      ,					_UpperCAmelCase    :						str=True      ,					_UpperCAmelCase    :						Optional[Any]=True      ,					_UpperCAmelCase    :						Optional[int]=True      ,					)						->   Optional[int]:




							"""simple docstring"""


							lowercase__     			=		self.run_trainer(
							    eval_steps=1      ,					max_len=12      ,					model_name=SCREAMING_SNAKE_CASE__      ,					num_train_epochs=1      ,					distributed=SCREAMING_SNAKE_CASE__      ,					extra_args_str=SCREAMING_SNAKE_CASE__      ,					predict_with_generate=SCREAMING_SNAKE_CASE__      ,					do_train=SCREAMING_SNAKE_CASE__      ,					do_eval=SCREAMING_SNAKE_CASE__      ,					do_predict=SCREAMING_SNAKE_CASE__      ,					)
							lowercase__     			=		TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__      ,					"""trainer_state.json"""		)		).log_history

							if not do_eval:
									return

							lowercase__     			=		[log for log in logs if """eval_loss""" in log.keys()]

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

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

					@require_torch_non_multi_gpu
					def   lowerCamelCase__     (self    :						Optional[int]		)						->   int:




							"""simple docstring"""


							self.run_seqaseq_quick()

					@require_torch_multi_gpu
					def   lowerCamelCase__     (self    :						int		)						->   List[str]:




							"""simple docstring"""


							self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__		)

					@require_torch_multi_gpu
					def   lowerCamelCase__     (self    :						Optional[Any]		)						->   Tuple:




							"""simple docstring"""


							self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__		)

					@unittest.skip("""Requires an update of the env running those tests"""		)
					@require_torch_multi_gpu
					@require_fairscale
					def   lowerCamelCase__     (self    :						Dict		)						->   Tuple:




							"""simple docstring"""


							self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__      ,					extra_args_str="""--sharded_ddp simple"""		)

					@unittest.skip("""Requires an update of the env running those tests"""		)
					@require_torch_multi_gpu
					@require_fairscale
					def   lowerCamelCase__     (self    :						Optional[int]		)						->   List[str]:




							"""simple docstring"""


							self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__      ,					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   lowerCamelCase__     (self    :						Tuple		)						->   Any:




							"""simple docstring"""


							self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__      ,					extra_args_str="""--sharded_ddp zero_dp_2"""      ,					predict_with_generate=SCREAMING_SNAKE_CASE__		)

					@unittest.skip("""Requires an update of the env running those tests"""		)
					@require_torch_multi_gpu
					@require_fairscale
					def   lowerCamelCase__     (self    :						Dict		)						->   Tuple:




							"""simple docstring"""


							self.run_seqaseq_quick(
							    distributed=SCREAMING_SNAKE_CASE__      ,					extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""      ,					predict_with_generate=SCREAMING_SNAKE_CASE__		)

					@require_apex
					@require_torch_gpu
					def   lowerCamelCase__     (self    :						Union[str, Any]		)						->   List[str]:




							"""simple docstring"""


							self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__      ,					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=SCREAMING_SNAKE_CASE__      ,					extra_args_str="""--fp16 --fp16_backend=apex"""		)

					@parameterized.expand(["""base""", """low""", """high""", """mixed"""]		)
					@require_torch_multi_gpu
					def   lowerCamelCase__     (self    :						Any      ,					_UpperCAmelCase    :						List[str]		)						->   Optional[Any]:




							"""simple docstring"""


							lowercase__     			=		{
							    # 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},
							}

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

					@slow
					def   lowerCamelCase__     (self    :						Any		)						->   Optional[Any]:




							"""simple docstring"""


							lowercase__     			=		self.run_trainer(
							    eval_steps=2      ,					max_len=128      ,					model_name=SCREAMING_SNAKE_CASE__      ,					learning_rate=3E-4      ,					num_train_epochs=10      ,					distributed=SCREAMING_SNAKE_CASE__      ,					)

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

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

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

					@slow
					@require_bitsandbytes
					def   lowerCamelCase__     (self    :						Optional[int]		)						->   str:




							"""simple docstring"""


							from transformers.training_args import OptimizerNames

							def train_and_return_metrics(_UpperCAmelCase    :						str		) -> Tuple[int, float]:
									lowercase__     			=		"""--skip_memory_metrics 0"""

									lowercase__     			=		self.run_trainer(
									    max_len=128      ,					model_name=SCREAMING_SNAKE_CASE__      ,					learning_rate=3E-4      ,					num_train_epochs=1      ,					optim=SCREAMING_SNAKE_CASE__      ,					distributed=SCREAMING_SNAKE_CASE__      ,					extra_args_str=SCREAMING_SNAKE_CASE__      ,					do_eval=SCREAMING_SNAKE_CASE__      ,					do_predict=SCREAMING_SNAKE_CASE__      ,					n_gpus_to_use=1      ,					)

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

									lowercase__     			=		logs[0]["""train_loss"""]
									return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

							lowercase__     			=		gpu_alloc_mem_orig - gpu_alloc_mem_bnb

							lowercase__     			=		gpu_peak_mem_orig + gpu_alloc_mem_orig
							lowercase__     			=		gpu_peak_mem_bnb + gpu_alloc_mem_bnb
							lowercase__     			=		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
							lowercase__     			=		120

							# 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(
							    SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					"""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(
							    SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					"""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(
							    SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''		)


					def   lowerCamelCase__     (self    :						Dict      ,					_UpperCAmelCase    :						int      ,					_UpperCAmelCase    :						str      ,					_UpperCAmelCase    :						int      ,					_UpperCAmelCase    :						float = 3E-3      ,					_UpperCAmelCase    :						str = "adafactor"      ,					_UpperCAmelCase    :						bool = False      ,					_UpperCAmelCase    :						str = None      ,					_UpperCAmelCase    :						int = 0      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						int = None      ,					)						->   List[Any]:




							"""simple docstring"""


							lowercase__     			=		self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
							lowercase__     			=		self.get_auto_remove_tmp_dir()
							lowercase__     			=		f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(SCREAMING_SNAKE_CASE__		)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(SCREAMING_SNAKE_CASE__		)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

							lowercase__     			=		f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(SCREAMING_SNAKE_CASE__		)}
        '''.split()

							lowercase__     			=		"""
            --do_predict
        """.split()

							lowercase__     			=		[]
							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:
											lowercase__     			=		get_gpu_count()
									lowercase__     			=		get_torch_dist_unique_port()
									lowercase__     			=		f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
									lowercase__     			=		[sys.executable] + distributed_args + args
									# keep for quick debug
									# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
									execute_subprocess_async(SCREAMING_SNAKE_CASE__      ,					env=self.get_env()		)
							else:
									lowercase__     			=		["""run_translation.py"""] + args
									with patch.object(SCREAMING_SNAKE_CASE__      ,					"""argv"""      ,					SCREAMING_SNAKE_CASE__		):
											main()

							return output_dir



from __future__ import annotations

from fractions import Fraction




def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						int							)			->   bool:
				return (
				    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
				)




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   list[str]:
				__lowerCamelCase    					=	[]
				__lowerCamelCase    					=	11
				__lowerCamelCase    					=	int('''1''' + '''0''' * digit_len							)
				for num in range(__lowerCAmelCase		,  __lowerCAmelCase							):
								while den <= 99:
												if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																if is_digit_cancelling(__lowerCAmelCase		,  __lowerCAmelCase							):
																				solutions.append(f'''{num}/{den}'''							)
												den += 1
								num += 1
								__lowerCamelCase    					=	10
				return solutions




def  __magic_name__ (   __lowerCAmelCase	:						int = 2							)			->   int:
				__lowerCamelCase    					=	1.0
				for fraction in fraction_list(__lowerCAmelCase							):
								__lowerCamelCase    					=	Fraction(__lowerCAmelCase							)
								result *= frac.denominator / frac.numerator
				return int(__lowerCAmelCase							)


if __name__ == "__main__":
					print(solution())



