


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 :   int							      =				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 :   List[str]							      =				"sshleifer/student_marian_en_ro_6_1"
SCREAMING_SNAKE_CASE :   List[str]							      =				"sshleifer/tiny-mbart"






@require_torch
class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase=False,   lowerCamelCase=None,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=True,   ) ->     Tuple:




           """simple docstring"""

           _lowercase :							Any   	=	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,   )
           _lowercase :							int   	=	TrainerState.load_from_json(os.path.join(lowerCamelCase,   'trainer_state.json')).log_history

           if not do_eval:
               return

           _lowercase :							str   	=	[log for log in logs if 'eval_loss' in log.keys()]

           _lowercase :							Optional[Any]   	=	eval_metrics[0]
           if predict_with_generate:
               assert "eval_bleu" in first_step_stats

               _lowercase :							Optional[int]   	=	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) ->     int:




           """simple docstring"""

           self.run_seqaseq_quick()
       @require_torch_multi_gpu
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           self.run_seqaseq_quick(distributed=lowerCamelCase)
       @require_torch_multi_gpu
       def     UpperCamelCase  (      self) ->     Optional[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[str]:




           """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[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     UpperCamelCase  (      self) ->     List[str]:




           """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) ->     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     UpperCamelCase  (      self) ->     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     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							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},
           }

           _lowercase :							int   	=	experiments[experiment_id]
           _lowercase :							Any   	=	{'distributed': True, 'predict_with_generate': False, 'do_eval': False, 'do_predict': False}
           _lowercase :							List[str]   	=	'Running training'
           with CaptureStderr() as cl:
               self.run_seqaseq_quick(**lowerCamelCase,   extra_args_str=data['extra_args_str'])
           _lowercase :							Dict   	=	len(re.findall(lowerCamelCase,   cl.err))
           self.assertEqual(lowerCamelCase,   data['n_matches'])
       @slow
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.run_trainer(
               eval_steps=2,   max_len=1_28,   model_name=lowerCamelCase,   learning_rate=3E-4,   num_train_epochs=10,   distributed=lowerCamelCase,   )

           # Check metrics
           _lowercase :							Tuple   	=	TrainerState.load_from_json(os.path.join(lowerCamelCase,   'trainer_state.json')).log_history
           _lowercase :							str   	=	[log for log in logs if 'eval_loss' in log.keys()]
           _lowercase :							Tuple   	=	eval_metrics[0]
           _lowercase :							List[str]   	=	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
           _lowercase :							Optional[int]   	=	os.listdir(lowerCamelCase)
           _lowercase :							Dict   	=	{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) ->     Tuple:




           """simple docstring"""

           from transformers.training_args import OptimizerNames

           def train_and_return_metrics(lowerCamelCase) -> Tuple[int, float]:
               _lowercase :							List[Any]   	=	'--skip_memory_metrics 0'

               _lowercase :							List[Any]   	=	self.run_trainer(
                   max_len=1_28,   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
               _lowercase :							Dict   	=	TrainerState.load_from_json(Path(lowerCamelCase,   'trainer_state.json')).log_history
               _lowercase :							Any   	=	int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20)
               _lowercase :							int   	=	int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20)

               _lowercase :							int   	=	logs[0]['train_loss']
               return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

           _lowercase	,       _lowercase	,       _lowercase :							Tuple   	=	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value)
           _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value)

           _lowercase :							Dict   	=	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

           _lowercase :							List[Any]   	=	gpu_peak_mem_orig + gpu_alloc_mem_orig
           _lowercase :							Any   	=	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
           _lowercase :							List[Any]   	=	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
           _lowercase :							str   	=	1_20

           # 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,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase = 3E-3,   lowerCamelCase = "adafactor",   lowerCamelCase = False,   lowerCamelCase = None,   lowerCamelCase = 0,   lowerCamelCase = True,   lowerCamelCase = True,   lowerCamelCase = True,   lowerCamelCase = True,   lowerCamelCase = None,   ) ->     Any:




           """simple docstring"""

           _lowercase :							str   	=	self.test_file_dir / '../fixtures/tests_samples/wmt_en_ro'
           _lowercase :							Dict   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							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()

           _lowercase :							Optional[int]   	=	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()

           _lowercase :							int   	=	'\n            --do_predict\n        '.split()

           _lowercase :							Optional[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:
                   _lowercase :							List[str]   	=	get_gpu_count()
               _lowercase :							Optional[Any]   	=	get_torch_dist_unique_port()
               _lowercase :							int   	=	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()
               _lowercase :							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:
               _lowercase :							Tuple   	=	['run_translation.py'] + args
               with patch.object(lowerCamelCase,   'argv',   lowerCamelCase):
                   main()

           return output_dir






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				logging.getLogger()



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							List[Any]   	=	'\n'.join(lowerCamelCase_				)
    Path(lowerCamelCase_				).open('w'				).writelines(lowerCamelCase_				)


SCREAMING_SNAKE_CASE :   str							      =				"patrickvonplaten/t5-tiny-random"
SCREAMING_SNAKE_CASE :   List[Any]							      =				"sshleifer/bart-tiny-random"
SCREAMING_SNAKE_CASE :   int							      =				"sshleifer/tiny-mbart"

SCREAMING_SNAKE_CASE :   Optional[int]							      =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							int   	=	Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
           _lowercase :							str   	=	input_file_name.parent / 'utest_output.txt'
           assert not output_file_name.exists()
           _lowercase :							str   	=	[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
           _dump_articles(lowerCamelCase,   lowerCamelCase)

           _lowercase :							List[str]   	=	str(Path(self.get_auto_remove_tmp_dir()) / 'scores.json')
           _lowercase :							Optional[int]   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Any   	=	F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

           with patch.object(lowerCamelCase,   'argv',   lowerCamelCase):
               run_generate()
               assert Path(lowerCamelCase).exists()
               # os.remove(Path(output_file_name))
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)
       @parameterized.expand([BART_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     int:




           """simple docstring"""

           self.run_eval_tester(lowerCamelCase)




       @parameterized.expand([T5_TINY, MBART_TINY])
       @slow
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							str   	=	Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
           _lowercase :							Any   	=	input_file_name.parent / 'utest_output.txt'
           assert not output_file_name.exists()

           _lowercase :							List[str]   	=	{
               'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
               'de': [
                   'Maschinelles Lernen ist großartig, oder?',
                   'Ich esse gerne Bananen',
                   'Morgen ist wieder ein toller Tag!',
               ],
           }

           _lowercase :							Optional[Any]   	=	Path(self.get_auto_remove_tmp_dir())
           _lowercase :							Optional[Any]   	=	str(tmp_dir / 'scores.json')
           _lowercase :							str   	=	str(tmp_dir / 'val.target')
           _dump_articles(lowerCamelCase,   text['en'])
           _dump_articles(lowerCamelCase,   text['de'])
           _lowercase :							Tuple   	=	'translation_en_to_de' if model == T5_TINY else 'summarization'
           _lowercase :							Tuple   	=	F'''
            run_eval_search.py
            {model}
            {str(lowerCamelCase)}
            {str(lowerCamelCase)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
           testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'])

           with patch.object(lowerCamelCase,   'argv',   lowerCamelCase):
               with CaptureStdout() as cs:
                   run_search()
               _lowercase :							Dict   	=	[' num_beams | length_penalty', model, 'Best score args']
               _lowercase :							Optional[Any]   	=	['Info']
               if "translation" in task:
                   expected_strings.append('bleu')
               else:
                   expected_strings.extend(lowerCamelCase)
               for w in expected_strings:
                   assert w in cs.out
               for w in un_expected_strings:
                   assert w not in cs.out
               assert Path(lowerCamelCase).exists()
               os.remove(Path(lowerCamelCase))


