


class   __lowercase				:





      def __init__(		self						:					List[Any]					,       __lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					int       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     name
         UpperCAmelCase							=     value
         UpperCAmelCase							=     weight





      def __repr__(		self						:					Tuple       )    ->   Optional[Any]:






         """simple docstring"""







         return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""





      def        _lowercase				(		self						:					List[Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         return self.value





      def        _lowercase				(		self						:					int       )    ->   Union[str, Any]:






         """simple docstring"""







         return self.name





      def        _lowercase				(		self						:					int       )    ->   Any:






         """simple docstring"""







         return self.weight





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Tuple:






         """simple docstring"""







         return self.value / self.weight

def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[Any]:
   UpperCAmelCase							=     []
   for i in range(len(lowerCAmelCase_   )   ):
      menu.append(Things(name[i]  ,						value[i]  ,						weight[i]   )   )
   return menu
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Optional[Any]:
   UpperCAmelCase							=     sorted(lowerCAmelCase_  ,						key=lowerCAmelCase_  ,						reverse=lowerCAmelCase_   )
   UpperCAmelCase							=     []
   UpperCAmelCase				,  UpperCAmelCase							=     0.0, 0.0
   for i in range(len(lowerCAmelCase_   )   ):
      if (total_cost + items_copy[i].get_weight()) <= max_cost:
         result.append(items_copy[i]   )
         total_cost += items_copy[i].get_weight()
         total_value += items_copy[i].get_value()
   return (result, total_value)
def   _UpperCamelCase				(						)						->List[str]:
   pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()







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


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


set_seed(42)
__a     =			"""sshleifer/student_marian_en_ro_6_1"""
__a     =			"""sshleifer/tiny-mbart"""






@require_torch
class   __lowercase				(  __snake_case  ):





      def        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					List[Any]=False					,       __lowerCamelCase						:					str=None					,       __lowerCamelCase						:					Any=True					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					str=True					,       __lowerCamelCase						:					List[str]=True					,       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     self.run_trainer(
             eval_steps=1					,       max_len=1_2					,       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							=     TrainerState.load_from_json(os.path.join(__lowerCamelCase					,       """trainer_state.json"""       )       ).log_history

         if not do_eval:
            return

         UpperCAmelCase							=     [log for log in logs if """eval_loss""" in log.keys()]

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

            UpperCAmelCase							=     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        _lowercase				(		self						:					Dict       )    ->   str:






         """simple docstring"""







         self.run_seqaseq_quick()





      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Tuple       )    ->   Any:






         """simple docstring"""







         self.run_seqaseq_quick(distributed=__lowerCamelCase       )





      @require_torch_multi_gpu
      def        _lowercase				(		self						:					Optional[int]       )    ->   Optional[int]:






         """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        _lowercase				(		self						:					Union[str, Any]       )    ->   Optional[int]:






         """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        _lowercase				(		self						:					List[Any]       )    ->   Union[str, Any]:






         """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        _lowercase				(		self						:					Dict       )    ->   Tuple:






         """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        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """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        _lowercase				(		self						:					str       )    ->   Optional[Any]:






         """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        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Optional[Any]       )    ->   Optional[Any]:






         """simple docstring"""







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





      @slow
      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.run_trainer(
             eval_steps=2					,       max_len=1_2_8					,       model_name=__lowerCamelCase					,       learning_rate=3e-4					,       num_train_epochs=1_0					,       distributed=__lowerCamelCase					,       )

         # Check metrics
         UpperCAmelCase							=     TrainerState.load_from_json(os.path.join(__lowerCamelCase					,       """trainer_state.json"""       )       ).log_history
         UpperCAmelCase							=     [log for log in logs if """eval_loss""" in log.keys()]
         UpperCAmelCase							=     eval_metrics[0]
         UpperCAmelCase							=     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							=     os.listdir(__lowerCamelCase       )
         UpperCAmelCase							=     {os.path.basename(__lowerCamelCase       ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents





      @slow
      @require_bitsandbytes
      def        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(__lowerCamelCase						:					str       ) -> Tuple[int, float]:
            UpperCAmelCase							=     """--skip_memory_metrics 0"""

            UpperCAmelCase							=     self.run_trainer(
                max_len=1_2_8					,       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							=     TrainerState.load_from_json(Path(__lowerCamelCase					,       """trainer_state.json"""       )       ).log_history
            UpperCAmelCase							=     int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**2_0       )
            UpperCAmelCase							=     int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**2_0       )

            UpperCAmelCase							=     logs[0]["""train_loss"""]
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

         UpperCAmelCase							=     gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         UpperCAmelCase							=     gpu_peak_mem_orig + gpu_alloc_mem_orig
         UpperCAmelCase							=     gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         UpperCAmelCase							=     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							=     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(
             __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        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					float = 3e-3					,       __lowerCamelCase						:					str = "adafactor"					,       __lowerCamelCase						:					bool = False					,       __lowerCamelCase						:					str = None					,       __lowerCamelCase						:					int = 0					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					int = None					,       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
         UpperCAmelCase							=     self.get_auto_remove_tmp_dir()
         UpperCAmelCase							=     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							=     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							=     """
            --do_predict
        """.split()

         UpperCAmelCase							=     []
         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							=     get_gpu_count()
            UpperCAmelCase							=     get_torch_dist_unique_port()
            UpperCAmelCase							=     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							=     [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							=     ["""run_translation.py"""] + args
            with patch.object(__lowerCamelCase					,       """argv"""					,       __lowerCamelCase       ):
               main()

         return output_dir



