




'''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


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


set_seed(4_2)
UpperCamelCase_    	=				"sshleifer/student_marian_en_ro_6_1"
UpperCamelCase_    	=				"sshleifer/tiny-mbart"






@require_torch
class 							_a				(			_a ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=False,							A=None,							A=True,							A=True,							A=True,							A=True,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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,							)
             SCREAMING_SNAKE_CASE   :      List[str]    					=						TrainerState.load_from_json(os.path.join(A,							'trainer_state.json'  )  ).log_history

             if not do_eval:
                   return

             SCREAMING_SNAKE_CASE   :      Dict    					=						[log for log in logs if 'eval_loss' in log.keys()]

             SCREAMING_SNAKE_CASE   :      List[str]    					=						eval_metrics[0]
             if predict_with_generate:
                   assert "eval_bleu" in first_step_stats

                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self.run_seqaseq_quick()







       @require_torch_multi_gpu
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self.run_seqaseq_quick(distributed=A  )







       @require_torch_multi_gpu
       def        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      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},
             }

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







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.run_trainer(
                 eval_steps=2,							max_len=128,							model_name=A,							learning_rate=3E-4,							num_train_epochs=10,							distributed=A,							)

             # Check metrics
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						TrainerState.load_from_json(os.path.join(A,							'trainer_state.json'  )  ).log_history
             SCREAMING_SNAKE_CASE   :      Any    					=						[log for log in logs if 'eval_loss' in log.keys()]
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						eval_metrics[0]
             SCREAMING_SNAKE_CASE   :      int    					=						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
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						os.listdir(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						{os.path.basename(A  ) for p in contents}
             assert "generated_predictions.txt" in contents
             assert "predict_results.json" in contents







       @slow
       @require_bitsandbytes
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             from transformers.training_args import OptimizerNames

             def train_and_return_metrics(A  ) -> Tuple[int, float]:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'--skip_memory_metrics 0'

                   SCREAMING_SNAKE_CASE   :      Any    					=						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
                   SCREAMING_SNAKE_CASE   :      int    					=						TrainerState.load_from_json(Path(A,							'trainer_state.json'  )  ).log_history
                   SCREAMING_SNAKE_CASE   :      str    					=						int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20  )
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20  )

                   SCREAMING_SNAKE_CASE   :      int    					=						logs[0]['train_loss']
                   return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

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

             SCREAMING_SNAKE_CASE   :      Any    					=						gpu_peak_mem_orig + gpu_alloc_mem_orig
             SCREAMING_SNAKE_CASE   :      Dict    					=						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
             SCREAMING_SNAKE_CASE   :      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
             SCREAMING_SNAKE_CASE   :      List[str]    					=						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        UpperCamelCase_							(      self,							A,							A,							A,							A = 3E-3,							A = "adafactor",							A = False,							A = None,							A = 0,							A = True,							A = True,							A = True,							A = True,							A = None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
             SCREAMING_SNAKE_CASE   :      int    					=						self.get_auto_remove_tmp_dir()
             SCREAMING_SNAKE_CASE   :      List[str]    					=						F"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(A  )}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(A  )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

             SCREAMING_SNAKE_CASE   :      str    					=						F"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(A  )}\n        ".split()

             SCREAMING_SNAKE_CASE   :      Dict    					=						'\n            --do_predict\n        '.split()

             SCREAMING_SNAKE_CASE   :      Optional[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:
                         SCREAMING_SNAKE_CASE   :      Any    					=						get_gpu_count()
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						get_torch_dist_unique_port()
                   SCREAMING_SNAKE_CASE   :      str    					=						F"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						[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:
                   SCREAMING_SNAKE_CASE   :      int    					=						['run_translation.py'] + args
                   with patch.object(A,							'argv',							A  ):
                         main()

             return output_dir






from __future__ import annotations



def 							UpperCamelCase_( lowerCamelCase_					)       ->		float:
    if not nums:
        raise ValueError('List is empty'					)
    return sum(lowerCamelCase_					) / len(lowerCamelCase_					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


