


"""simple docstring"""


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 snake_case      (  _UpperCamelCase):







		def  a_ (					self      :       str    ,   a__      :       str=False    ,   a__      :       str=None    ,   a__      :       Tuple=True    ,   a__      :       Optional[Any]=True    ,   a__      :       Union[str, Any]=True    ,   a__      :       Union[str, Any]=True    ,   )       ->						Any:





						'''simple docstring'''







						_A					  =			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__    ,   )
						_A					  =			TrainerState.load_from_json(os.path.join(a__    ,   "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"]    ,   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      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







						self.run_seqaseq_quick()







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





						'''simple docstring'''







						self.run_seqaseq_quick(distributed=a__		)







		@require_torch_multi_gpu
		def  a_ (					self      :       str		)       ->						List[str]:





						'''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      :       Any		)       ->						List[Any]:





						'''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      :       Dict		)       ->						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      :       str		)       ->						Union[str, Any]:





						'''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      :       int		)       ->						Optional[int]:





						'''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      :       str		)       ->						int:





						'''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      :       Optional[Any]    ,   a__      :       Tuple		)       ->						List[str]:





						'''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(**a__    ,   extra_args_str=data["extra_args_str"]		)
						_A					  =			len(re.findall(a__    ,   cl.err		)		)
						self.assertEqual(a__    ,   data["n_matches"]		)







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





						'''simple docstring'''







						_A					  =			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
						_A					  =			TrainerState.load_from_json(os.path.join(a__    ,   "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"]    ,   a__		)

						# test if do_predict saves generations and metrics
						_A					  =			os.listdir(a__		)
						_A					  =			{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      :       List[Any]		)       ->						str:





						'''simple docstring'''







						from transformers.training_args import OptimizerNames

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

										_A					  =			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
										_A					  =			TrainerState.load_from_json(Path(a__    ,   "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					  =			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    ,   a__      :       int    ,   a__      :       str    ,   a__      :       int    ,   a__      :       float = 3E-3    ,   a__      :       str = "adafactor"    ,   a__      :       bool = False    ,   a__      :       str = None    ,   a__      :       int = 0    ,   a__      :       bool = True    ,   a__      :       bool = True    ,   a__      :       bool = True    ,   a__      :       bool = True    ,   a__      :       int = None    ,   )       ->						str:





						'''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(a__		)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(a__		)}
            --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(a__		)}
        """.split()

						_A					  =			"\n            --do_predict\n        ".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(a__    ,   env=self.get_env()		)
						else:
										_A					  =			["run_translation.py"] + args
										with patch.object(a__    ,   "argv"    ,   a__		):
														main()

						return output_dir



"""simple docstring"""


def      a__ (				__lowercase     ,			__lowercase   )	->	int:
				while a != 0:
								_A  ,		_A					  =			b % a, a
				return b






def      a__ (				__lowercase     ,			__lowercase   )	->	int:
				if gcd(__lowercase     ,			__lowercase   ) != 1:
								_A					  =			f"""mod inverse of {a!r} and {m!r} does not exist"""
								raise ValueError(__lowercase   )
				_A  ,		_A  ,		_A					  =			1, 0, a
				_A  ,		_A  ,		_A					  =			0, 1, m
				while va != 0:
								_A					  =			ua // va
								_A  ,		_A  ,		_A  ,		_A  ,		_A  ,		_A					  =			(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
				return ua % m