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					(    a__    ):

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


									'''simple docstring'''

									lowercase							=		self.run_trainer(
									    eval_steps=1							,							max_len=12							,							model_name=_A							,							num_train_epochs=1							,							distributed=_A							,							extra_args_str=_A							,							predict_with_generate=_A							,							do_train=_A							,							do_eval=_A							,							do_predict=_A							,							)
									lowercase							=		TrainerState.load_from_json(os.path.join(_A							,							'''trainer_state.json'''   )   ).log_history

									if not do_eval:
												return

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

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

												lowercase							=		eval_metrics[-1]
												assert isinstance(last_step_stats['''eval_bleu''']							,							_A   )
												assert not math.isnan(float(last_step_stats['''eval_loss''']   )   ), "eval_loss must not be `nan`"

						@require_torch_non_multi_gpu
						def        __a		(	self				:      Optional[int]   )							->		Dict:


									'''simple docstring'''

									self.run_seqaseq_quick()

						@require_torch_multi_gpu
						def        __a		(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									self.run_seqaseq_quick(distributed=_A   )

						@require_torch_multi_gpu
						def        __a		(	self				:      int   )							->		int:


									'''simple docstring'''

									self.run_seqaseq_quick(distributed=_A   )

						@unittest.skip('''Requires an update of the env running those tests'''   )
						@require_torch_multi_gpu
						@require_fairscale
						def        __a		(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									self.run_seqaseq_quick(distributed=_A							,							extra_args_str='''--sharded_ddp simple'''   )

						@unittest.skip('''Requires an update of the env running those tests'''   )
						@require_torch_multi_gpu
						@require_fairscale
						def        __a		(	self				:      Optional[int]   )							->		Union[str, Any]:


									'''simple docstring'''

									self.run_seqaseq_quick(distributed=_A							,							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        __a		(	self				:      Dict   )							->		Dict:


									'''simple docstring'''

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

						@unittest.skip('''Requires an update of the env running those tests'''   )
						@require_torch_multi_gpu
						@require_fairscale
						def        __a		(	self				:      Dict   )							->		Union[str, Any]:


									'''simple docstring'''

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

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


									'''simple docstring'''

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

						@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']   )
						@require_torch_multi_gpu
						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      str   )							->		List[Any]:


									'''simple docstring'''

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

						@slow
						def        __a		(	self				:      Optional[int]   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.run_trainer(
									    eval_steps=2							,							max_len=1_28							,							model_name=_A							,							learning_rate=3E-4							,							num_train_epochs=10							,							distributed=_A							,							)

									# Check metrics
									lowercase							=		TrainerState.load_from_json(os.path.join(_A							,							'''trainer_state.json'''   )   ).log_history
									lowercase							=		[log for log in logs if 'eval_loss' in log.keys()]
									lowercase							=		eval_metrics[0]
									lowercase							=		eval_metrics[-1]

									assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
									assert isinstance(last_step_stats['''eval_bleu''']							,							_A   )

									# test if do_predict saves generations and metrics
									lowercase							=		os.listdir(_A   )
									lowercase							=		{os.path.basename(_A   ) for p in contents}
									assert "generated_predictions.txt" in contents
									assert "predict_results.json" in contents

						@slow
						@require_bitsandbytes
						def        __a		(	self				:      Tuple   )							->		Optional[int]:


									'''simple docstring'''

									from transformers.training_args import OptimizerNames

									def train_and_return_metrics(__lowerCamelCase				:      Union[str, Any]   ) -> Tuple[int, float]:
												lowercase							=		'--skip_memory_metrics 0'

												lowercase							=		self.run_trainer(
												    max_len=1_28							,							model_name=_A							,							learning_rate=3E-4							,							num_train_epochs=1							,							optim=_A							,							distributed=_A							,							extra_args_str=_A							,							do_eval=_A							,							do_predict=_A							,							n_gpus_to_use=1							,							)

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

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

									lowercase							=		train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value   )
									lowercase							=		train_and_return_metrics(OptimizerNames.ADAMW_BNB.value   )

									lowercase							=		gpu_alloc_mem_orig - gpu_alloc_mem_bnb

									lowercase							=		gpu_peak_mem_orig + gpu_alloc_mem_orig
									lowercase							=		gpu_peak_mem_bnb + gpu_alloc_mem_bnb
									lowercase							=		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							=		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(
									    _A							,							_A							,							'''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(
									    _A							,							_A							,							'''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(
									    _A							,							_A							,							f'loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'   )



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Optional[int] = 3E-3							,							__lowerCamelCase				:      Tuple = "adafactor"							,							__lowerCamelCase				:      Optional[Any] = False							,							__lowerCamelCase				:      Dict = None							,							__lowerCamelCase				:      Any = 0							,							__lowerCamelCase				:      Union[str, Any] = True							,							__lowerCamelCase				:      str = True							,							__lowerCamelCase				:      List[str] = True							,							__lowerCamelCase				:      Any = True							,							__lowerCamelCase				:      Optional[Any] = None							,							)							->		Tuple:


									'''simple docstring'''

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

									lowercase							=		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(_A   )}\n        '.split()

									lowercase							=		'\n            --do_predict\n        '.split()

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

									return output_dir


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A_      =      re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")






@total_ordering
@dataclass
class      __lowercase					:
						lowercase					 =     42
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		_str_to_version_tuple(self.version_str   )

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


									'''simple docstring'''

									return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'

						@property
						def        __a		(	self				:      Dict   )							->		Union[str, Any]:


									'''simple docstring'''

									return self.major, self.minor, self.patch

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return Version(__lowerCamelCase   )
									elif isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return other
									raise TypeError(f'{other} (type {type(__lowerCamelCase   )}) cannot be compared to version.'   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									try:
												lowercase							=		self._validate_operand(__lowerCamelCase   )
									except (TypeError, ValueError):
												return False
									else:
												return self.tuple == other.tuple

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


									'''simple docstring'''

									lowercase							=		self._validate_operand(__lowerCamelCase   )
									return self.tuple < other.tuple

						def __hash__(	self				:      int   )							->		str:


									'''simple docstring'''

									return hash(_version_tuple_to_str(self.tuple   )   )

						@classmethod
						def        __a		(	cls				:      List[str]							,							__lowerCamelCase				:      Any   )							->		int:


									'''simple docstring'''

									lowercase							=		{f.name for f in dataclasses.fields(cls   )}
									return cls(**{k: v for k, v in dic.items() if k in field_names}   )



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


									'''simple docstring'''

									return self.version_str






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		_VERSION_REG.match(UpperCAmelCase       )
			if not res:
						raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'       )
			return tuple(int(UpperCAmelCase       ) for v in [res.group('''major'''       ), res.group('''minor'''       ), res.group('''patch'''       )]       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			return ".".join(str(UpperCAmelCase       ) for v in version_tuple       )





