'''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    a (       __lowerCAmelCase				):



  """simple docstring"""







  def    lowercase_  (							self   ,    snake_case_=False   ,    snake_case_=None   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.run_trainer(
        eval_steps=1   ,    max_len=12   ,    model_name=snake_case_   ,    num_train_epochs=1   ,    distributed=snake_case_   ,    extra_args_str=snake_case_   ,    predict_with_generate=snake_case_   ,    do_train=snake_case_   ,    do_eval=snake_case_   ,    do_predict=snake_case_   ,    )
    __UpperCAmelCase:					str		= TrainerState.load_from_json(os.path.join(snake_case_   ,    """trainer_state.json"""					)					).log_history

    if not do_eval:
      return

    __UpperCAmelCase:					Dict		= [log for log in logs if """eval_loss""" in log.keys()]

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

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







  @require_torch_non_multi_gpu
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick()







  @require_torch_multi_gpu
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=snake_case_					)







  @require_torch_multi_gpu
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=snake_case_					)







  @unittest.skip("""Requires an update of the env running those tests"""					)
  @require_torch_multi_gpu
  @require_fairscale
  def    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=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    lowercase_  (							self					):

    '''simple docstring'''







    self.run_seqaseq_quick(distributed=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    lowercase_  (							self					):

    '''simple docstring'''







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







  @unittest.skip("""Requires an update of the env running those tests"""					)
  @require_torch_multi_gpu
  @require_fairscale
  def    lowercase_  (							self					):

    '''simple docstring'''







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







  @require_apex
  @require_torch_gpu
  def    lowercase_  (							self					):

    '''simple docstring'''







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







  @parameterized.expand(["""base""", """low""", """high""", """mixed"""]					)
  @require_torch_multi_gpu
  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[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},
    }

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







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







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

    # Check metrics
    __UpperCAmelCase:					str		= TrainerState.load_from_json(os.path.join(snake_case_   ,    """trainer_state.json"""					)					).log_history
    __UpperCAmelCase:					Any		= [log for log in logs if """eval_loss""" in log.keys()]
    __UpperCAmelCase:					Tuple		= eval_metrics[0]
    __UpperCAmelCase:					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"""]   ,    snake_case_					)

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







  @slow
  @require_bitsandbytes
  def    lowercase_  (							self					):

    '''simple docstring'''







    from transformers.training_args import OptimizerNames

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

      __UpperCAmelCase:					Union[str, Any]		= self.run_trainer(
          max_len=128   ,    model_name=snake_case_   ,    learning_rate=3e-4   ,    num_train_epochs=1   ,    optim=snake_case_   ,    distributed=snake_case_   ,    extra_args_str=snake_case_   ,    do_eval=snake_case_   ,    do_predict=snake_case_   ,    n_gpus_to_use=1   ,    )

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

      __UpperCAmelCase:					Optional[Any]		= logs[0]["""train_loss"""]
      return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					List[str]		= train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value					)
    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					Tuple		= train_and_return_metrics(OptimizerNames.ADAMW_BNB.value					)

    __UpperCAmelCase:					Optional[int]		= gpu_alloc_mem_orig - gpu_alloc_mem_bnb

    __UpperCAmelCase:					List[str]		= gpu_peak_mem_orig + gpu_alloc_mem_orig
    __UpperCAmelCase:					Tuple		= gpu_peak_mem_bnb + gpu_alloc_mem_bnb
    __UpperCAmelCase:					Tuple		= 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
    __UpperCAmelCase:					Dict		= 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(
        snake_case_   ,    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(
        snake_case_   ,    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(
        snake_case_   ,    snake_case_   ,    F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_ = 3e-3   ,    snake_case_ = "adafactor"   ,    snake_case_ = False   ,    snake_case_ = None   ,    snake_case_ = 0   ,    snake_case_ = True   ,    snake_case_ = True   ,    snake_case_ = True   ,    snake_case_ = True   ,    snake_case_ = None   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
    __UpperCAmelCase:					Union[str, Any]		= self.get_auto_remove_tmp_dir()
    __UpperCAmelCase:					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(snake_case_					)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(snake_case_					)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

    __UpperCAmelCase:					Optional[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(snake_case_					)}
        '''.split()

    __UpperCAmelCase:					Any		= """
            --do_predict
        """.split()

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

    return output_dir
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				list[int]     )							->	None:
  __UpperCAmelCase:					Dict		= len(_lowercase     )
  print("""The following activities are selected:"""     )

  # The first activity is always selected
  __UpperCAmelCase:					Dict		= 0
  print(_lowercase   ,    end=""","""     )

  # Consider rest of the activities
  for j in range(_lowercase     ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(_lowercase   ,    end=""","""     )
      __UpperCAmelCase:					Optional[int]		= j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			[1, 3, 0, 5, 8, 5]
    SCREAMING_SNAKE_CASE_	   =			[2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)