
'''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


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


set_seed(42)
__lowerCAmelCase												=	'sshleifer/student_marian_en_ro_6_1'
__lowerCAmelCase												=	'sshleifer/tiny-mbart'


@require_torch
class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def 	lowercase							(self  ,  UpperCAmelCase=False  ,  UpperCAmelCase=None  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  )					->       Optional[Any]:
														_snake_case       =  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  ,  )
														_snake_case       =  TrainerState.load_from_json(os.path.join(UpperCAmelCase  ,  """trainer_state.json"""  )  ).log_history

														if not do_eval:
																					return

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

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

																					_snake_case       =  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 	lowercase							(self  )					->       int:
														self.run_seqaseq_quick()

							@require_torch_multi_gpu
							def 	lowercase							(self  )					->       Any:
														self.run_seqaseq_quick(distributed=UpperCAmelCase  )

							@require_torch_multi_gpu
							def 	lowercase							(self  )					->       List[str]:
														self.run_seqaseq_quick(distributed=UpperCAmelCase  )

							@unittest.skip("""Requires an update of the env running those tests"""  )
							@require_torch_multi_gpu
							@require_fairscale
							def 	lowercase							(self  )					->       List[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 	lowercase							(self  )					->       Union[str, 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 	lowercase							(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 	lowercase							(self  )					->       Dict:
														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 	lowercase							(self  )					->       Optional[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=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 	lowercase							(self  ,  UpperCAmelCase  )					->       List[Any]:
														# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
														_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},
														}

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

							@slow
							def 	lowercase							(self  )					->       List[str]:
														_snake_case       =  self.run_trainer(
														    eval_steps=2  ,  max_len=128  ,  model_name=UpperCAmelCase  ,  learning_rate=3e-4  ,  num_train_epochs=10  ,  distributed=UpperCAmelCase  ,  )

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

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

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

														def train_and_return_metrics(UpperCAmelCase  ) -> Tuple[int, float]:
																					_snake_case       =  """--skip_memory_metrics 0"""

																					_snake_case       =  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
																					_snake_case       =  TrainerState.load_from_json(Path(UpperCAmelCase  ,  """trainer_state.json"""  )  ).log_history
																					_snake_case       =  int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20  )
																					_snake_case       =  int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20  )

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

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

														_snake_case       =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

														_snake_case       =  gpu_peak_mem_orig + gpu_alloc_mem_orig
														_snake_case       =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
														_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
														_snake_case       =  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 	lowercase							(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  ,  )					->       List[Any]:
														_snake_case       =  self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
														_snake_case       =  self.get_auto_remove_tmp_dir()
														_snake_case       =  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()

														_snake_case       =  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()

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

														_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:
																												_snake_case       =  get_gpu_count()
																					_snake_case       =  get_torch_dist_unique_port()
																					_snake_case       =  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()
																					_snake_case       =  [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:
																					_snake_case       =  ["""run_translation.py"""] + args
																					with patch.object(UpperCAmelCase  ,  """argv"""  ,  UpperCAmelCase  ):
																												main()

														return output_dir

'''simple docstring'''
from __future__ import annotations

import typing
from collections import Counter






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  Counter()
							for base in range(1							, max_perimeter + 1				):
														for perpendicular in range(_SCREAMING_SNAKE_CASE							, max_perimeter + 1				):
																					_snake_case       =  (base * base + perpendicular * perpendicular) ** 0.5
																					if hypotenuse == int(_SCREAMING_SNAKE_CASE				):
																												_snake_case       =  int(base + perpendicular + hypotenuse				)
																												if perimeter > max_perimeter:
																																			continue
																												triplets[perimeter] += 1
							return triplets






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE = 1000				):
							_snake_case       =  pythagorean_triple(_SCREAMING_SNAKE_CASE				)
							return triplets.most_common(1				)[0][0]


if __name__ == "__main__":
				print(f'''Perimeter {solution()} has maximum solutions''')