




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 						_A					(  a__      ):






  def 					__a			(  self		:	Optional[int]      ,						_A		:	Optional[int]=False      ,						_A		:	Dict=None      ,						_A		:	int=True      ,						_A		:	Optional[int]=True      ,						_A		:	Optional[int]=True      ,						_A		:	Optional[int]=True      ,						)		->    int:






       """simple docstring"""







       lowercase					:   Any								= 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					:   Dict								= TrainerState.load_from_json(os.path.join(UpperCamelCase_      ,						'''trainer_state.json'''      )      ).log_history

       if not do_eval:
            return

       lowercase					:   Any								= [log for log in logs if '''eval_loss''' in log.keys()]

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

            lowercase					:   Tuple								= 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 					__a			(  self		:	Optional[int]      )		->    str:






       """simple docstring"""







       self.run_seqaseq_quick()






  @require_torch_multi_gpu
  def 					__a			(  self		:	Dict      )		->    List[Any]:






       """simple docstring"""







       self.run_seqaseq_quick(distributed=UpperCamelCase_      )






  @require_torch_multi_gpu
  def 					__a			(  self		:	Tuple      )		->    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 					__a			(  self		:	Optional[Any]      )		->    Union[str, Any]:






       """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 					__a			(  self		:	Optional[int]      )		->    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 					__a			(  self		:	Optional[Any]      )		->    Union[str, Any]:






       """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 					__a			(  self		:	List[str]      )		->    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 					__a			(  self		:	List[Any]      )		->    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 					__a			(  self		:	Any      ,						_A		:	Optional[Any]      )		->    Dict:






       """simple docstring"""







       lowercase					:   Optional[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},
       }

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






  @slow
  def 					__a			(  self		:	str      )		->    Any:






       """simple docstring"""







       lowercase					:   Tuple								= 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					:   Optional[int]								= TrainerState.load_from_json(os.path.join(UpperCamelCase_      ,						'''trainer_state.json'''      )      ).log_history
       lowercase					:   List[Any]								= [log for log in logs if '''eval_loss''' in log.keys()]
       lowercase					:   Optional[int]								= eval_metrics[0]
       lowercase					:   List[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
       lowercase					:   List[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 					__a			(  self		:	Tuple      )		->    List[str]:






       """simple docstring"""







       from transformers.training_args import OptimizerNames

       def train_and_return_metrics(_A		:	str      ) -> Tuple[int, float]:
            lowercase					:   List[Any]								= '''--skip_memory_metrics 0'''

            lowercase					:   Optional[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					:   Dict								= TrainerState.load_from_json(Path(UpperCamelCase_      ,						'''trainer_state.json'''      )      ).log_history
            lowercase					:   Optional[int]								= int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20      )
            lowercase					:   Tuple								= int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20      )

            lowercase					:   List[Any]								= logs[0]['''train_loss''']
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

       lowercase					:   List[str]								= train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value      )
       lowercase					:   Dict								= train_and_return_metrics(OptimizerNames.ADAMW_BNB.value      )

       lowercase					:   Union[str, Any]								= gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       lowercase					:   int								= gpu_peak_mem_orig + gpu_alloc_mem_orig
       lowercase					:   Union[str, Any]								= gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       lowercase					:   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
       lowercase					:   Optional[Any]								= 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 					__a			(  self		:	Optional[int]      ,						_A		:	int      ,						_A		:	str      ,						_A		:	int      ,						_A		:	float = 3E-3      ,						_A		:	str = "adafactor"      ,						_A		:	bool = False      ,						_A		:	str = None      ,						_A		:	int = 0      ,						_A		:	bool = True      ,						_A		:	bool = True      ,						_A		:	bool = True      ,						_A		:	bool = True      ,						_A		:	int = None      ,						)		->    Tuple:






       """simple docstring"""







       lowercase					:   str								= self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
       lowercase					:   Tuple								= self.get_auto_remove_tmp_dir()
       lowercase					:   Dict								= 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					:   int								= 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					:   List[Any]								= '''
            --do_predict
        '''.split()

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

       return output_dir



"""simple docstring"""





from datetime import datetime

import matplotlib.pyplot as plt
import torch


def    _snake_case  (  _snake_case :	int	):
      for param in module.parameters():
            lowerCAmelCase    :		Optional[int]				=      False


def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            lowerCAmelCase    :		Any				=      '''mps'''
      if device == "mps":
            print(
                '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
                ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
                ''' with generations.'''	)
      return device


def    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[int]				=      plt.imshow(_snake_case	)
      fig.axes.get_xaxis().set_visible(_snake_case	)
      fig.axes.get_yaxis().set_visible(_snake_case	)
      plt.show()






def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datetime.now()
      lowerCAmelCase    :		Union[str, Any]				=      current_time.strftime('''%H:%M:%S'''	)
      return timestamp


