


'''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


__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)
__snake_case       		='''sshleifer/student_marian_en_ro_6_1'''
__snake_case       		='''sshleifer/tiny-mbart'''

@require_torch
class 						UpperCAmelCase_ (    __snake_case						):





     def 				__UpperCAmelCase (					self			: int       , UpperCAmelCase__			: Dict=False       , UpperCAmelCase__			: Dict=None       , UpperCAmelCase__			: List[str]=True       , UpperCAmelCase__			: List[Any]=True       , UpperCAmelCase__			: Tuple=True       , UpperCAmelCase__			: int=True       , )							->    Dict:
        lowerCAmelCase	=     self.run_trainer(
            eval_steps=1       , max_len=1_2       , model_name=_SCREAMING_SNAKE_CASE       , num_train_epochs=1       , distributed=_SCREAMING_SNAKE_CASE       , extra_args_str=_SCREAMING_SNAKE_CASE       , predict_with_generate=_SCREAMING_SNAKE_CASE       , do_train=_SCREAMING_SNAKE_CASE       , do_eval=_SCREAMING_SNAKE_CASE       , do_predict=_SCREAMING_SNAKE_CASE       , )
        lowerCAmelCase	=     TrainerState.load_from_json(os.path.join(_SCREAMING_SNAKE_CASE       , 'trainer_state.json'       )       ).log_history

        if not do_eval:
           return

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

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

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





     @require_torch_non_multi_gpu
     def 				__UpperCAmelCase (					self			: Union[str, Any]       )							->    int:
        self.run_seqaseq_quick()





     @require_torch_multi_gpu
     def 				__UpperCAmelCase (					self			: str       )							->    Optional[Any]:
        self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE       )





     @require_torch_multi_gpu
     def 				__UpperCAmelCase (					self			: int       )							->    int:
        self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE       )





     @unittest.skip('Requires an update of the env running those tests'       )
     @require_torch_multi_gpu
     @require_fairscale
     def 				__UpperCAmelCase (					self			: Union[str, Any]       )							->    int:
        self.run_seqaseq_quick(distributed=_SCREAMING_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 				__UpperCAmelCase (					self			: Optional[Any]       )							->    str:
        self.run_seqaseq_quick(distributed=_SCREAMING_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 				__UpperCAmelCase (					self			: List[Any]       )							->    Union[str, Any]:
        self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE       , extra_args_str='--sharded_ddp zero_dp_2'       , predict_with_generate=_SCREAMING_SNAKE_CASE       )





     @unittest.skip('Requires an update of the env running those tests'       )
     @require_torch_multi_gpu
     @require_fairscale
     def 				__UpperCAmelCase (					self			: List[Any]       )							->    Any:
        self.run_seqaseq_quick(
            distributed=_SCREAMING_SNAKE_CASE       , extra_args_str='--sharded_ddp zero_dp_2 --fp16'       , predict_with_generate=_SCREAMING_SNAKE_CASE       )





     @require_apex
     @require_torch_gpu
     def 				__UpperCAmelCase (					self			: str       )							->    List[Any]:
        self.run_seqaseq_quick(distributed=_SCREAMING_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=_SCREAMING_SNAKE_CASE       , extra_args_str='--fp16 --fp16_backend=apex'       )





     @parameterized.expand(['base', 'low', 'high', 'mixed']       )
     @require_torch_multi_gpu
     def 				__UpperCAmelCase (					self			: Dict       , UpperCAmelCase__			: Tuple       )							->    int:
        lowerCAmelCase	=     {
            # 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},
        }

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





     @slow
     def 				__UpperCAmelCase (					self			: int       )							->    List[str]:
        lowerCAmelCase	=     self.run_trainer(
            eval_steps=2       , max_len=1_2_8       , model_name=_SCREAMING_SNAKE_CASE       , learning_rate=3E-4       , num_train_epochs=1_0       , distributed=_SCREAMING_SNAKE_CASE       , )

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

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

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





     @slow
     @require_bitsandbytes
     def 				__UpperCAmelCase (					self			: Dict       )							->    Optional[Any]:
        from transformers.training_args import OptimizerNames

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

           lowerCAmelCase	=     self.run_trainer(
               max_len=1_2_8       , model_name=_SCREAMING_SNAKE_CASE       , learning_rate=3E-4       , num_train_epochs=1       , optim=_SCREAMING_SNAKE_CASE       , distributed=_SCREAMING_SNAKE_CASE       , extra_args_str=_SCREAMING_SNAKE_CASE       , do_eval=_SCREAMING_SNAKE_CASE       , do_predict=_SCREAMING_SNAKE_CASE       , n_gpus_to_use=1       , )

           # Check metrics
           lowerCAmelCase	=     TrainerState.load_from_json(Path(_SCREAMING_SNAKE_CASE       , 'trainer_state.json'       )       ).log_history
           lowerCAmelCase	=     int(logs[0]['train_mem_gpu_peaked_delta'] / 2**2_0       )
           lowerCAmelCase	=     int(logs[0]['train_mem_gpu_alloc_delta'] / 2**2_0       )

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

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

        lowerCAmelCase	=     gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        lowerCAmelCase	=     gpu_peak_mem_orig + gpu_alloc_mem_orig
        lowerCAmelCase	=     gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        lowerCAmelCase	=     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
        lowerCAmelCase	=     1_2_0

        # 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(
            _SCREAMING_SNAKE_CASE       , _SCREAMING_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(
            _SCREAMING_SNAKE_CASE       , _SCREAMING_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(
            _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''       )





     def 				__UpperCAmelCase (					self			: List[str]       , UpperCAmelCase__			: int       , UpperCAmelCase__			: str       , UpperCAmelCase__			: int       , UpperCAmelCase__			: float = 3E-3       , UpperCAmelCase__			: str = "adafactor"       , UpperCAmelCase__			: bool = False       , UpperCAmelCase__			: str = None       , UpperCAmelCase__			: int = 0       , UpperCAmelCase__			: bool = True       , UpperCAmelCase__			: bool = True       , UpperCAmelCase__			: bool = True       , UpperCAmelCase__			: bool = True       , UpperCAmelCase__			: int = None       , )							->    Any:
        lowerCAmelCase	=     self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
        lowerCAmelCase	=     self.get_auto_remove_tmp_dir()
        lowerCAmelCase	=     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(_SCREAMING_SNAKE_CASE       )}\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(_SCREAMING_SNAKE_CASE       )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '''.split()

        lowerCAmelCase	=     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(_SCREAMING_SNAKE_CASE       )}\n        '''.split()

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

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

        return output_dir













from datetime import datetime as dt
import os

from github import Github


__magic_name__   :  Dict        =							[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def 		lowercase__  (				) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  Github(os.environ['GITHUB_TOKEN'])
      UpperCamelCase					       =  g.get_repo('huggingface/transformers')
      UpperCamelCase					       =  repo.get_issues(state='open')

      for issue in open_issues:
            UpperCamelCase					       =  sorted([comment for comment in issue.get_comments()]       ,							key=lambda _UpperCamelCase: i.created_at       ,							reverse=_UpperCamelCase)
            UpperCamelCase					       =  comments[0] if len(_UpperCamelCase) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state='closed')
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  issue.create_comment(
                      'This issue has been automatically marked as stale because it has not had '
                      'recent activity. If you think this still needs to be addressed '
                      'please comment on this thread.\n\nPlease note that issues that do not follow the '
                      '[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '
                      'are likely to be ignored.')


if __name__ == "__main__":
       main()



