






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_    (						_A			):






 '''simple docstring'''




 def  snake_case__				(	self							,  _A=False							,  _A=None							,  _A=True							,  _A=True							,  _A=True							,  _A=True							,  ) ->		Dict:

     """simple docstring"""



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

     if not do_eval:
         return

     _UpperCAmelCase				:      List[Any]			  =				[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				:      List[str]			  =				eval_metrics[-1]
         assert isinstance(last_step_stats['''eval_bleu''']							,  __lowerCamelCase)
         assert not math.isnan(float(last_step_stats['''eval_loss'''])), "eval_loss must not be `nan`"


 @require_torch_non_multi_gpu
 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     self.run_seqaseq_quick()


 @require_torch_multi_gpu
 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     self.run_seqaseq_quick(distributed=__lowerCamelCase)


 @require_torch_multi_gpu
 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     self.run_seqaseq_quick(distributed=__lowerCamelCase)


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

     """simple docstring"""



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


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

     """simple docstring"""



     self.run_seqaseq_quick(distributed=__lowerCamelCase							,  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  snake_case__				(	self) ->		int:

     """simple docstring"""



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


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

     """simple docstring"""



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


 @require_apex
 @require_torch_gpu
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



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


 @parameterized.expand(['''base''', '''low''', '''high''', '''mixed'''])
 @require_torch_multi_gpu
 def  snake_case__				(	self							,  _A) ->		Tuple:

     """simple docstring"""



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


 @slow
 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



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

     # Check metrics
     _UpperCAmelCase				:      str			  =				TrainerState.load_from_json(os.path.join(__lowerCamelCase							,  '''trainer_state.json''')).log_history
     _UpperCAmelCase				:      Dict			  =				[log for log in logs if "eval_loss" in log.keys()]
     _UpperCAmelCase				:      Dict			  =				eval_metrics[0]
     _UpperCAmelCase				:      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''']							,  __lowerCamelCase)

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


 @slow
 @require_bitsandbytes
 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     from transformers.training_args import OptimizerNames

     def train_and_return_metrics(_A) -> Tuple[int, float]:
         _UpperCAmelCase				:      Optional[int]			  =				"--skip_memory_metrics 0"

         _UpperCAmelCase				:      str			  =				self.run_trainer(
             max_len=128							,  model_name=__lowerCamelCase							,  learning_rate=3e-4							,  num_train_epochs=1							,  optim=__lowerCamelCase							,  distributed=__lowerCamelCase							,  extra_args_str=__lowerCamelCase							,  do_eval=__lowerCamelCase							,  do_predict=__lowerCamelCase							,  n_gpus_to_use=1							,  )

         # Check metrics
         _UpperCAmelCase				:      Union[str, Any]			  =				TrainerState.load_from_json(Path(__lowerCamelCase							,  '''trainer_state.json''')).log_history
         _UpperCAmelCase				:      str			  =				int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20)
         _UpperCAmelCase				:      List[Any]			  =				int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20)

         _UpperCAmelCase				:      int			  =				logs[0]["train_loss"]
         return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

     _UpperCAmelCase				:      Tuple			  =				gpu_alloc_mem_orig - gpu_alloc_mem_bnb

     _UpperCAmelCase				:      Dict			  =				gpu_peak_mem_orig + gpu_alloc_mem_orig
     _UpperCAmelCase				:      Dict			  =				gpu_peak_mem_bnb + gpu_alloc_mem_bnb
     _UpperCAmelCase				:      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
     _UpperCAmelCase				:      Tuple			  =				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(
         __lowerCamelCase							,  __lowerCamelCase							,  '''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(
         __lowerCamelCase							,  __lowerCamelCase							,  '''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(
         __lowerCamelCase							,  __lowerCamelCase							,  f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''')


 def  snake_case__				(	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							,  ) ->		List[str]:

     """simple docstring"""



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

     _UpperCAmelCase				:      Optional[Any]			  =				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(__lowerCamelCase)}
        '''.split()

     _UpperCAmelCase				:      Optional[Any]			  =				"\n            --do_predict\n        ".split()

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

     return output_dir












import argparse
import os
import re


SCREAMING_SNAKE_CASE 							=						'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE 							=						re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE 							=						re.compile(R'\s*\(\s*"(\S[^"]+)"')

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							bool = False )      ->					int:
    with open(__A		,		'''r'''		,		encoding='''utf-8''' ) as f:
        _UpperCAmelCase				:      Union[str, Any]			  =				f.read()

    _UpperCAmelCase				:      Any			  =				content.split('''\n''' )
    _UpperCAmelCase				:      Any			  =				[]
    _UpperCAmelCase				:      Tuple			  =				0
    while line_idx < len(__A ):
        if _re_intro_mapping.search(lines[line_idx] ) is not None:
            _UpperCAmelCase				:      Union[str, Any]			  =				len(re.search(r'''^(\s*)\S'''		,		lines[line_idx] ).groups()[0] ) + 8
            # Start of a new mapping!
            while not lines[line_idx].startswith(''' ''' * indent + '''(''' ):
                new_lines.append(lines[line_idx] )
                line_idx += 1

            _UpperCAmelCase				:      str			  =				[]
            while lines[line_idx].strip() != "]":
                # Blocks either fit in one line or not
                if lines[line_idx].strip() == "(":
                    _UpperCAmelCase				:      List[str]			  =				line_idx
                    while not lines[line_idx].startswith(''' ''' * indent + ''')''' ):
                        line_idx += 1
                    blocks.append('''\n'''.join(lines[start_idx : line_idx + 1] ) )
                else:
                    blocks.append(lines[line_idx] )
                line_idx += 1

            # Sort blocks by their identifiers
            _UpperCAmelCase				:      Tuple			  =				sorted(__A		,		key=lambda __A : _re_identifier.search(__A ).groups()[0] )
            new_lines += blocks
        else:
            new_lines.append(lines[line_idx] )
            line_idx += 1

    if overwrite:
        with open(__A		,		'''w'''		,		encoding='''utf-8''' ) as f:
            f.write('''\n'''.join(__A ) )
    elif "\n".join(__A ) != content:
        return True

def      _lowerCamelCase	(						__A    :							bool = False )      ->					List[str]:
    _UpperCAmelCase				:      List[str]			  =				[os.path.join(__A		,		__A ) for f in os.listdir(__A ) if f.endswith('''.py''' )]
    _UpperCAmelCase				:      List[Any]			  =				[sort_auto_mapping(__A		,		overwrite=__A ) for fname in fnames]

    if not overwrite and any(__A ):
        _UpperCAmelCase				:      Optional[int]			  =				[f for f, d in zip(__A		,		__A ) if d]
        raise ValueError(
            f'''The following files have auto mappings that need sorting: {', '.join(__A )}. Run `make style` to fix'''
            ''' this.''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()

  sort_all_auto_mappings(not args.check_only)




