

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


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

@require_torch
class    _lowercase				(					__lowerCamelCase			):

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    Optional[int]=False			,						lowerCamelCase__	:    Union[str, Any]=None			,						lowerCamelCase__	:    Union[str, Any]=True			,						lowerCamelCase__	:    Optional[int]=True			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    List[Any]=True			,						) ->			str:
         """simple docstring"""





         A_							       =		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__			,						)
         A_							       =		TrainerState.load_from_json(os.path.join(lowerCamelCase__			,						'''trainer_state.json'''       )       ).log_history

         if not do_eval:
               return

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

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

               A_							       =		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     UpperCamelCase					(				self	:    List[str]       ) ->			Optional[Any]:
         """simple docstring"""





         self.run_seqaseq_quick()

   @require_torch_multi_gpu
   def     UpperCamelCase					(				self	:    int       ) ->			Optional[Any]:
         """simple docstring"""





         self.run_seqaseq_quick(distributed=lowerCamelCase__       )

   @require_torch_multi_gpu
   def     UpperCamelCase					(				self	:    List[str]       ) ->			List[Any]:
         """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     UpperCamelCase					(				self	:    List[Any]       ) ->			Optional[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     UpperCamelCase					(				self	:    List[str]       ) ->			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     UpperCamelCase					(				self	:    str       ) ->			Dict:
         """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     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			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     UpperCamelCase					(				self	:    List[Any]       ) ->			Tuple:
         """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     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    Tuple       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		{
             # 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},
         }

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

   @slow
   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





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

   @slow
   @require_bitsandbytes
   def     UpperCamelCase					(				self	:    str       ) ->			List[str]:
         """simple docstring"""





         from transformers.training_args import OptimizerNames

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

               A_							       =		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
               A_							       =		TrainerState.load_from_json(Path(lowerCamelCase__			,						'''trainer_state.json'''       )       ).log_history
               A_							       =		int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0       )
               A_							       =		int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0       )

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

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

         A_							       =		gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         A_							       =		gpu_peak_mem_orig + gpu_alloc_mem_orig
         A_							       =		gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         A_							       =		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
         A_							       =		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     UpperCamelCase					(				self	:    int			,						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			,						) ->			List[str]:
         """simple docstring"""





         A_							       =		self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
         A_							       =		self.get_auto_remove_tmp_dir()
         A_							       =		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(lowerCamelCase__       )}\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(lowerCamelCase__       )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

         A_							       =		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(lowerCamelCase__       )}\n        ".split()

         A_							       =		'''
            --do_predict
        '''.split()

         A_							       =		[]
         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:
                     A_							       =		get_gpu_count()
               A_							       =		get_torch_dist_unique_port()
               A_							       =		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()
               A_							       =		[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:
               A_							       =		['''run_translation.py'''] + args
               with patch.object(lowerCamelCase__			,						'''argv'''			,						lowerCamelCase__       ):
                     main()

         return output_dir








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      return int((input_a, input_a).count(0   ) != 0   )







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      assert nand_gate(0		,				0   ) == 1
      assert nand_gate(0		,				1   ) == 1
      assert nand_gate(1		,				0   ) == 1
      assert nand_gate(1		,				1   ) == 0


if __name__ == "__main__":
      print(nand_gate(0, 0))
      print(nand_gate(0, 1))
      print(nand_gate(1, 0))
      print(nand_gate(1, 1))





