





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


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


set_seed(42)
lowercase_					     =	"""sshleifer/student_marian_en_ro_6_1"""
lowercase_					     =	"""sshleifer/tiny-mbart"""
@require_torch
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Union[str, Any]=False    ,  a    :       Optional[Any]=None    ,  a    :       int=True    ,  a    :       List[Any]=True    ,  a    :       Optional[int]=True    ,  a    :       Optional[Any]=True    ,  )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						self.run_trainer(
         eval_steps=1    ,  max_len=12    ,  model_name=a    ,  num_train_epochs=1    ,  distributed=a    ,  extra_args_str=a    ,  predict_with_generate=a    ,  do_train=a    ,  do_eval=a    ,  do_predict=a    ,  )
     lowercase__			    =						TrainerState.load_from_json(os.path.join(a    ,  'trainer_state.json' ) ).log_history

     if not do_eval:
       return

     lowercase__			    =						[log for log in logs if 'eval_loss' in log.keys()]

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

       lowercase__			    =						eval_metrics[-1]
       assert isinstance(last_step_stats['eval_bleu']    ,  a )
       assert not math.isnan(float(last_step_stats['eval_loss'] ) ), "eval_loss must not be `nan`"


   @require_torch_non_multi_gpu
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Any:





     """simple docstring"""







     self.run_seqaseq_quick()


   @require_torch_multi_gpu
   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     int:





     """simple docstring"""







     self.run_seqaseq_quick(distributed=a )


   @require_torch_multi_gpu
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     self.run_seqaseq_quick(distributed=a )


   @unittest.skip('Requires an update of the env running those tests' )
   @require_torch_multi_gpu
   @require_fairscale
   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     int:





     """simple docstring"""







     self.run_seqaseq_quick(distributed=a    ,  extra_args_str='--sharded_ddp simple' )


   @unittest.skip('Requires an update of the env running those tests' )
   @require_torch_multi_gpu
   @require_fairscale
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     List[Any]:





     """simple docstring"""







     self.run_seqaseq_quick(distributed=a    ,  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  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     List[Any]:





     """simple docstring"""







     self.run_seqaseq_quick(distributed=a    ,  extra_args_str='--sharded_ddp zero_dp_2'    ,  predict_with_generate=a )


   @unittest.skip('Requires an update of the env running those tests' )
   @require_torch_multi_gpu
   @require_fairscale
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     int:





     """simple docstring"""







     self.run_seqaseq_quick(
         distributed=a    ,  extra_args_str='--sharded_ddp zero_dp_2 --fp16'    ,  predict_with_generate=a )


   @require_apex
   @require_torch_gpu
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Dict:





     """simple docstring"""







     self.run_seqaseq_quick(distributed=a    ,  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=a    ,  extra_args_str='--fp16 --fp16_backend=apex' )


   @parameterized.expand(['base', 'low', 'high', 'mixed'] )
   @require_torch_multi_gpu
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       int )->     int:





     """simple docstring"""







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


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[str]:





     """simple docstring"""







     lowercase__			    =						self.run_trainer(
         eval_steps=2    ,  max_len=128    ,  model_name=a    ,  learning_rate=3E-4    ,  num_train_epochs=10    ,  distributed=a    ,  )

     # Check metrics
     lowercase__			    =						TrainerState.load_from_json(os.path.join(a    ,  'trainer_state.json' ) ).log_history
     lowercase__			    =						[log for log in logs if 'eval_loss' in log.keys()]
     lowercase__			    =						eval_metrics[0]
     lowercase__			    =						eval_metrics[-1]

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

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


   @slow
   @require_bitsandbytes
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     Dict:





     """simple docstring"""







     from transformers.training_args import OptimizerNames

     def train_and_return_metrics(a    :       str ) -> Tuple[int, float]:
       lowercase__			    =						'--skip_memory_metrics 0'

       lowercase__			    =						self.run_trainer(
           max_len=128    ,  model_name=a    ,  learning_rate=3E-4    ,  num_train_epochs=1    ,  optim=a    ,  distributed=a    ,  extra_args_str=a    ,  do_eval=a    ,  do_predict=a    ,  n_gpus_to_use=1    ,  )

       # Check metrics
       lowercase__			    =						TrainerState.load_from_json(Path(a    ,  'trainer_state.json' ) ).log_history
       lowercase__			    =						int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20 )
       lowercase__			    =						int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20 )

       lowercase__			    =						logs[0]['train_loss']
       return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

     lowercase__			    =						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

     lowercase__			    =						gpu_peak_mem_orig + gpu_alloc_mem_orig
     lowercase__			    =						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
     lowercase__			    =						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__			    =						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(
         a    ,  a    ,  '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(
         a    ,  a    ,  '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(
         a    ,  a    ,  f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}""" )


   def  SCREAMING_SNAKE_CASE_							(					self    :       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    ,  )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
     lowercase__			    =						self.get_auto_remove_tmp_dir()
     lowercase__			    =						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(a )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(a )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

     lowercase__			    =						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(a )}
        """.split()

     lowercase__			    =						'\n            --do_predict\n        '.split()

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

     return output_dir









def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE = 50	)       ->     int:
  lowercase__			    =						[1] * (length + 1)

  for row_length in range(3	,		length + 1	):
    for block_length in range(3	,		row_length + 1	):
      for block_start in range(row_length - block_length	):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


if __name__ == "__main__":
      print(f'''{solution() = }''')


