



"""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


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


set_seed(4_2)
lowercase__		:				Tuple          =	"""sshleifer/student_marian_en_ro_6_1"""
lowercase__		:				Dict          =	"""sshleifer/tiny-mbart"""







@require_torch
class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      str=False    ,			SCREAMING_SNAKE_CASE_				:      List[str]=None    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      Tuple=True    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      Any=True    ,			):
        lowerCAmelCase_   :				Dict								= 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_   :				Dict								= TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE_    ,			'trainer_state.json'       )       ).log_history

        if not do_eval:
          return

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

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

          lowerCAmelCase_   :				Optional[Any]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        self.run_seqaseq_quick()



      @require_torch_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE_       )



      @require_torch_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      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      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        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      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        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      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        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      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        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      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
        # program and it breaks other tests that run from the same pytest worker, therefore until this is
        # sorted out it must be run only in an external program, that is distributed=True in this
        # test and only under one or more gpus - if we want cpu will need to make a special test
        #
        # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
        # 2nd main() call it botches the future eval.
        #
        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      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
        lowerCAmelCase_   :				Optional[int]								= {
            # 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_   :				List[Any]								= experiments[experiment_id]
        lowerCAmelCase_   :				Optional[int]								= {'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
        lowerCAmelCase_   :				List[str]								= 'Running training'
        with CaptureStderr() as cl:
          self.run_seqaseq_quick(**SCREAMING_SNAKE_CASE_    ,			extra_args_str=data['extra_args_str']       )
        lowerCAmelCase_   :				Tuple								= len(re.findall(SCREAMING_SNAKE_CASE_    ,			cl.err       )       )
        self.assertEqual(SCREAMING_SNAKE_CASE_    ,			data['n_matches']       )



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				int								= 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_   :				Union[str, Any]								= TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE_    ,			'trainer_state.json'       )       ).log_history
        lowerCAmelCase_   :				Tuple								= [log for log in logs if 'eval_loss' in log.keys()]
        lowerCAmelCase_   :				List[str]								= eval_metrics[0]
        lowerCAmelCase_   :				Optional[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']    ,			SCREAMING_SNAKE_CASE_       )

        # test if do_predict saves generations and metrics
        lowerCAmelCase_   :				Dict								= os.listdir(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Optional[Any]								= {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      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(SCREAMING_SNAKE_CASE_				:      str       ) -> Tuple[int, float]:
          lowerCAmelCase_   :				List[str]								= '--skip_memory_metrics 0'

          lowerCAmelCase_   :				int								= 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_   :				Tuple								= TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE_    ,			'trainer_state.json'       )       ).log_history
          lowerCAmelCase_   :				str								= int(logs[0]['train_mem_gpu_peaked_delta'] / 2**2_0       )
          lowerCAmelCase_   :				Optional[int]								= int(logs[0]['train_mem_gpu_alloc_delta'] / 2**2_0       )

          lowerCAmelCase_   :				Any								= logs[0]['train_loss']
          return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Optional[Any]								= train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value       )
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= train_and_return_metrics(OptimizerNames.ADAMW_BNB.value       )

        lowerCAmelCase_   :				List[Any]								= gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        lowerCAmelCase_   :				Union[str, Any]								= gpu_peak_mem_orig + gpu_alloc_mem_orig
        lowerCAmelCase_   :				Tuple								= gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        lowerCAmelCase_   :				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
        lowerCAmelCase_   :				Dict								= 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      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      float = 3E-3    ,			SCREAMING_SNAKE_CASE_				:      str = "adafactor"    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      str = None    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      int = None    ,			):
        lowerCAmelCase_   :				List[Any]								= self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
        lowerCAmelCase_   :				Union[str, Any]								= self.get_auto_remove_tmp_dir()
        lowerCAmelCase_   :				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(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_   :				List[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(SCREAMING_SNAKE_CASE_       )}\n        ".split()

        lowerCAmelCase_   :				Tuple								= '\n            --do_predict\n        '.split()

        lowerCAmelCase_   :				Union[str, Any]								= []
        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_   :				int								= get_gpu_count()
          lowerCAmelCase_   :				List[str]								= get_torch_dist_unique_port()
          lowerCAmelCase_   :				Union[str, Any]								= 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_   :				List[Any]								= [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_   :				Union[str, Any]								= ['run_translation.py'] + args
          with patch.object(SCREAMING_SNAKE_CASE_    ,			'argv'    ,			SCREAMING_SNAKE_CASE_       ):
            main()

        return output_dir










"""simple docstring"""







from typing import Union

import fire
import torch
from tqdm import tqdm

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str = "cpu"    ,   lowerCAmelCase__      :				Union[str, None] = None						)						->   None:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__						)
  for k, v in tqdm(state_dict.items()						):
    if not isinstance(lowerCAmelCase__    ,   torch.Tensor						):
      raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'						)
    lowerCAmelCase_   :				str								= v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_   :				Dict								= src_path
  torch.save(lowerCAmelCase__    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     fire.Fire(convert)





