"""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


__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        UpperCamelCase		(			lowercase_  ):
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=False     ,__UpperCamelCase=None     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,)	->      str:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	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     ,)
        lowercase_	:				List[str]													=	TrainerState.load_from_json(os.path.join(__UpperCamelCase     ,'trainer_state.json'			)			).log_history

        if not do_eval:
             return

        lowercase_	:				Union[str, Any]													=	[log for log in logs if 'eval_loss' in log.keys()]

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

             lowercase_	:				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			)	->      Optional[Any]:


        '''simple docstring'''


        self.run_seqaseq_quick()
   @require_torch_multi_gpu
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


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


        '''simple docstring'''


        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[int]:


        '''simple docstring'''


        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			)	->      List[Any]:


        '''simple docstring'''


        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:


        '''simple docstring'''


        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			)	->      str:


        '''simple docstring'''


        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			)	->      str:


        '''simple docstring'''


        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			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				List[str]													=	{
            # 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_	:				Optional[int]													=	experiments[experiment_id]
        lowercase_	:				List[Any]													=	{'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
        lowercase_	:				int													=	'Running training'
        with CaptureStderr() as cl:
             self.run_seqaseq_quick(**__UpperCamelCase     ,extra_args_str=data['extra_args_str']			)
        lowercase_	:				Union[str, Any]													=	len(re.findall(__UpperCamelCase     ,cl.err			)			)
        self.assertEqual(__UpperCamelCase     ,data['n_matches']			)
   @slow
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				int													=	self.run_trainer(
            eval_steps=2     ,max_len=128     ,model_name=__UpperCamelCase     ,learning_rate=3e-4     ,num_train_epochs=10     ,distributed=__UpperCamelCase     ,)

        # Check metrics
        lowercase_	:				Union[str, Any]													=	TrainerState.load_from_json(os.path.join(__UpperCamelCase     ,'trainer_state.json'			)			).log_history
        lowercase_	:				Optional[int]													=	[log for log in logs if 'eval_loss' in log.keys()]
        lowercase_	:				Optional[int]													=	eval_metrics[0]
        lowercase_	:				List[Any]													=	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
        lowercase_	:				Optional[Any]													=	os.listdir(__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	{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			)	->      List[str]:


        '''simple docstring'''


        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(__UpperCamelCase			) -> Tuple[int, float]:
             lowercase_	:				Any													=	'--skip_memory_metrics 0'

             lowercase_	:				int													=	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
             lowercase_	:				Tuple													=	TrainerState.load_from_json(Path(__UpperCamelCase     ,'trainer_state.json'			)			).log_history
             lowercase_	:				Any													=	int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20			)
             lowercase_	:				Any													=	int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20			)

             lowercase_	:				Union[str, Any]													=	logs[0]['train_loss']
             return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        lowercase_							,  lowercase_							,  lowercase_	:				Tuple													=	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value			)
        lowercase_							,  lowercase_							,  lowercase_	:				Optional[int]													=	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value			)

        lowercase_	:				int													=	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        lowercase_	:				Optional[Any]													=	gpu_peak_mem_orig + gpu_alloc_mem_orig
        lowercase_	:				Any													=	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        lowercase_	:				str													=	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_	:				Tuple													=	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[Any]:


        '''simple docstring'''


        lowercase_	:				List[str]													=	self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
        lowercase_	:				List[str]													=	self.get_auto_remove_tmp_dir()
        lowercase_	:				List[str]													=	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()

        lowercase_	:				Optional[Any]													=	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()

        lowercase_	:				Any													=	'\n            --do_predict\n        '.split()

        lowercase_	:				List[Any]													=	[]
        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_	:				Optional[int]													=	get_gpu_count()
             lowercase_	:				Optional[int]													=	get_torch_dist_unique_port()
             lowercase_	:				List[str]													=	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_	:				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:
             lowercase_	:				Dict													=	['run_translation.py'] + args
             with patch.object(__UpperCamelCase     ,'argv'     ,__UpperCamelCase			):
                  main()

        return output_dir


"""simple docstring"""







from __future__ import annotations



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list      ):
     if not nums:
          raise ValueError('List is empty'      )
     return sum(__SCREAMING_SNAKE_CASE      ) / len(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

