
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     :	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)
A     :	Any    = 'sshleifer/student_marian_en_ro_6_1'
A     :	int    = 'sshleifer/tiny-mbart'







@require_torch
class 			__A( a			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=False		,     _snake_case=None		,     _snake_case=True		,     _snake_case=True		,     _snake_case=True		,     _snake_case=True		,     )    ->      Any:


								'''simple docstring'''




								__a										=    self.run_trainer(
								    eval_steps=1		,     max_len=12		,     model_name=_snake_case		,     num_train_epochs=1		,     distributed=_snake_case		,     extra_args_str=_snake_case		,     predict_with_generate=_snake_case		,     do_train=_snake_case		,     do_eval=_snake_case		,     do_predict=_snake_case		,     )
								__a										=    TrainerState.load_from_json(os.path.join(_snake_case		,     '''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''']		,     _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				)    ->      List[str]:


								'''simple docstring'''




								self.run_seqaseq_quick()




				@require_torch_multi_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								self.run_seqaseq_quick(distributed=_snake_case				)




				@require_torch_multi_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								self.run_seqaseq_quick(distributed=_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:


								'''simple docstring'''




								self.run_seqaseq_quick(distributed=_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]:


								'''simple docstring'''




								self.run_seqaseq_quick(distributed=_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				)    ->      Optional[Any]:


								'''simple docstring'''




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




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


								'''simple docstring'''




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




				@require_apex
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




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




				@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']				)
				@require_torch_multi_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      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(**_snake_case		,     extra_args_str=data['''extra_args_str''']				)
								__a										=    len(re.findall(_snake_case		,     cl.err				)				)
								self.assertEqual(_snake_case		,     data['''n_matches''']				)




				@slow
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.run_trainer(
								    eval_steps=2		,     max_len=128		,     model_name=_snake_case		,     learning_rate=3E-4		,     num_train_epochs=10		,     distributed=_snake_case		,     )

								# Check metrics
								__a										=    TrainerState.load_from_json(os.path.join(_snake_case		,     '''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''']		,     _snake_case				)

								# test if do_predict saves generations and metrics
								__a										=    os.listdir(_snake_case				)
								__a										=    {os.path.basename(_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				)    ->      Dict:


								'''simple docstring'''




								from transformers.training_args import OptimizerNames

								def train_and_return_metrics(_snake_case				) -> Tuple[int, float]:
												__a										=    '''--skip_memory_metrics 0'''

												__a										=    self.run_trainer(
												    max_len=128		,     model_name=_snake_case		,     learning_rate=3E-4		,     num_train_epochs=1		,     optim=_snake_case		,     distributed=_snake_case		,     extra_args_str=_snake_case		,     do_eval=_snake_case		,     do_predict=_snake_case		,     n_gpus_to_use=1		,     )

												# Check metrics
												__a										=    TrainerState.load_from_json(Path(_snake_case		,     '''trainer_state.json'''				)				).log_history
												__a										=    int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20				)
												__a										=    int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20				)

												__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										=    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(
								    _snake_case		,     _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(
								    _snake_case		,     _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(
								    _snake_case		,     _snake_case		,     F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case = 3E-3		,     _snake_case = "adafactor"		,     _snake_case = False		,     _snake_case = None		,     _snake_case = 0		,     _snake_case = True		,     _snake_case = True		,     _snake_case = True		,     _snake_case = True		,     _snake_case = None		,     )    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
								__a										=    self.get_auto_remove_tmp_dir()
								__a										=    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(_snake_case				)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(_snake_case				)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

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

								return output_dir

def     __lowerCAmelCase			(			a__  )       ->  str:
				__a										=    []
				__a										=    set({'''(''', '''[''', '''{'''}  )
				__a										=    set({''')''', ''']''', '''}'''}  )
				__a										=    {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

				for i in range(len(a__  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(a__  ) == 0 or (len(a__  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(a__  ) == 0


def     __lowerCAmelCase			(			)       ->  Dict:
				__a										=    input('''Enter sequence of brackets: '''  )
				if is_balanced(a__  ):
								print(a__						,    '''is balanced'''  )
				else:
								print(a__						,    '''is not balanced'''  )


if __name__ == "__main__":
					main()