
"""simple docstring"""

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						=			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						=			'sshleifer/student_marian_en_ro_6_1'
_A						=			'sshleifer/tiny-mbart'






@require_torch
class 	_lowercase       (    __UpperCAmelCase		):




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=False		,			UpperCAmelCase_=None		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			)		->							Any:
			lowerCamelCase					:      List[str]          =   self.run_trainer(
			    eval_steps=1		,			max_len=12		,			model_name=UpperCAmelCase_		,			num_train_epochs=1		,			distributed=UpperCAmelCase_		,			extra_args_str=UpperCAmelCase_		,			predict_with_generate=UpperCAmelCase_		,			do_train=UpperCAmelCase_		,			do_eval=UpperCAmelCase_		,			do_predict=UpperCAmelCase_		,			)
			lowerCamelCase					:      List[str]          =   TrainerState.load_from_json(os.path.join(UpperCAmelCase_		,			'trainer_state.json' ) ).log_history

			if not do_eval:
					return

			lowerCamelCase					:      Dict          =   [log for log in logs if 'eval_loss' in log.keys()]

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

					lowerCamelCase					:      List[str]          =   eval_metrics[-1]
					assert isinstance(last_step_stats['eval_bleu']		,			UpperCAmelCase_ )
					assert not math.isnan(float(last_step_stats['eval_loss'] ) ), "eval_loss must not be `nan`"




	@require_torch_non_multi_gpu
	def       _UpperCamelCase   (			self )		->							Dict:
			self.run_seqaseq_quick()




	@require_torch_multi_gpu
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			self.run_seqaseq_quick(distributed=UpperCAmelCase_ )




	@require_torch_multi_gpu
	def       _UpperCamelCase   (			self )		->							Optional[int]:
			self.run_seqaseq_quick(distributed=UpperCAmelCase_ )




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




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




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




	@require_apex
	@require_torch_gpu
	def       _UpperCamelCase   (			self )		->							int:
			# 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=UpperCAmelCase_		,			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=UpperCAmelCase_		,			extra_args_str='--fp16 --fp16_backend=apex' )




	@parameterized.expand(['base', 'low', 'high', 'mixed'] )
	@require_torch_multi_gpu
	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Union[str, Any]:
			# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
			lowerCamelCase					:      Union[str, Any]          =   {
			    # 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},
			}

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




	@slow
	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      Dict          =   self.run_trainer(
			    eval_steps=2		,			max_len=128		,			model_name=UpperCAmelCase_		,			learning_rate=3E-4		,			num_train_epochs=10		,			distributed=UpperCAmelCase_		,			)

			# Check metrics
			lowerCamelCase					:      List[str]          =   TrainerState.load_from_json(os.path.join(UpperCAmelCase_		,			'trainer_state.json' ) ).log_history
			lowerCamelCase					:      Dict          =   [log for log in logs if 'eval_loss' in log.keys()]
			lowerCamelCase					:      Tuple          =   eval_metrics[0]
			lowerCamelCase					:      str          =   eval_metrics[-1]

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

			# test if do_predict saves generations and metrics
			lowerCamelCase					:      Optional[int]          =   os.listdir(UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   {os.path.basename(UpperCAmelCase_ ) for p in contents}
			assert "generated_predictions.txt" in contents
			assert "predict_results.json" in contents




	@slow
	@require_bitsandbytes
	def       _UpperCamelCase   (			self )		->							Any:
			from transformers.training_args import OptimizerNames

			def train_and_return_metrics(UpperCAmelCase_ ) -> Tuple[int, float]:
					lowerCamelCase					:      Union[str, Any]          =   '--skip_memory_metrics 0'

					lowerCamelCase					:      Dict          =   self.run_trainer(
					    max_len=128		,			model_name=UpperCAmelCase_		,			learning_rate=3E-4		,			num_train_epochs=1		,			optim=UpperCAmelCase_		,			distributed=UpperCAmelCase_		,			extra_args_str=UpperCAmelCase_		,			do_eval=UpperCAmelCase_		,			do_predict=UpperCAmelCase_		,			n_gpus_to_use=1		,			)

					# Check metrics
					lowerCamelCase					:      List[Any]          =   TrainerState.load_from_json(Path(UpperCAmelCase_		,			'trainer_state.json' ) ).log_history
					lowerCamelCase					:      str          =   int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20 )
					lowerCamelCase					:      Optional[int]          =   int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20 )

					lowerCamelCase					:      Tuple          =   logs[0]['train_loss']
					return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

			lowerCamelCase					:      List[str]          =   train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
			lowerCamelCase					:      Optional[Any]          =   train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )

			lowerCamelCase					:      Optional[Any]          =   gpu_alloc_mem_orig - gpu_alloc_mem_bnb

			lowerCamelCase					:      str          =   gpu_peak_mem_orig + gpu_alloc_mem_orig
			lowerCamelCase					:      Optional[int]          =   gpu_peak_mem_bnb + gpu_alloc_mem_bnb
			lowerCamelCase					:      Optional[int]          =   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
			lowerCamelCase					:      str          =   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(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			'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(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			'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(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}""" )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = 3E-3		,			UpperCAmelCase_ = "adafactor"		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = 0		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = True		,			UpperCAmelCase_ = None		,			)		->							Optional[int]:
			lowerCamelCase					:      str          =   self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
			lowerCamelCase					:      Tuple          =   self.get_auto_remove_tmp_dir()
			lowerCamelCase					:      Union[str, Any]          =   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(UpperCAmelCase_ )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCAmelCase_ )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

			lowerCamelCase					:      str          =   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(UpperCAmelCase_ )}
        """.split()

			lowerCamelCase					:      List[str]          =   '\n            --do_predict\n        '.split()

			lowerCamelCase					:      Optional[int]          =   []
			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:
							lowerCamelCase					:      Any          =   get_gpu_count()
					lowerCamelCase					:      List[Any]          =   get_torch_dist_unique_port()
					lowerCamelCase					:      Union[str, Any]          =   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()
					lowerCamelCase					:      Union[str, Any]          =   [sys.executable] + distributed_args + args
					# keep for quick debug
					# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
					execute_subprocess_async(UpperCAmelCase_		,			env=self.get_env() )
			else:
					lowerCamelCase					:      Optional[int]          =   ['run_translation.py'] + args
					with patch.object(UpperCAmelCase_		,			'argv'		,			UpperCAmelCase_ ):
							main()

			return output_dir








"""simple docstring"""

def 		UpperCAmelCase (		):





		'''simple docstring'''


		return 1
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else two_pence(x - 2      ) + one_pence()
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else five_pence(x - 5      ) + two_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else ten_pence(x - 10      ) + five_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else twenty_pence(x - 20      ) + ten_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else fifty_pence(x - 50      ) + twenty_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else one_pound(x - 100      ) + fifty_pence(a_      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return 0 if x < 0 else two_pound(x - 200      ) + one_pound(a_      )
def 		UpperCAmelCase (		a_ = 200      ):





		'''simple docstring'''


		return two_pound(a_      )


if __name__ == "__main__":
					print(solution(int(input().strip())))






