

'''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


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


set_seed(42)
UpperCamelCase_    						=       '''sshleifer/student_marian_en_ro_6_1'''
UpperCamelCase_    						=       '''sshleifer/tiny-mbart'''





@require_torch
class 		__SCREAMING_SNAKE_CASE							(			_lowercase      ):







				def  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		Union[str, Any]=False		,					UpperCAmelCase__     :		str=None		,					UpperCAmelCase__     :		List[Any]=True		,					UpperCAmelCase__     :		Union[str, Any]=True		,					UpperCAmelCase__     :		Union[str, Any]=True		,					UpperCAmelCase__     :		Union[str, Any]=True		,					):
										'''simple docstring'''
										lowercase :							List[Any]    		=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 :							int    		=TrainerState.load_from_json(os.path.join(__A		,					'''trainer_state.json'''   )   ).log_history

										if not do_eval:
																return

										lowercase :							Tuple    		=[log for log in logs if """eval_loss""" in log.keys()]

										lowercase :							Union[str, Any]    		=eval_metrics[0]
										if predict_with_generate:
																assert "eval_bleu" in first_step_stats

																lowercase :							Tuple    		=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  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										self.run_seqaseq_quick()







				@require_torch_multi_gpu
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										self.run_seqaseq_quick(distributed=__A   )







				@require_torch_multi_gpu
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''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  lowerCamelCase_	(       self     :		Tuple   ):
										'''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  lowerCamelCase_	(       self     :		Dict   ):
										'''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  lowerCamelCase_	(       self     :		Tuple   ):
										'''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  lowerCamelCase_	(       self     :		Optional[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  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
										# program and it breaks other tests that run from the same pytest worker, therefore until this is
										# sorted out it must be run only in an external program, that is distributed=True in this
										# test and only under one or more gpus - if we want cpu will need to make a special test
										#
										# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
										# 2nd main() call it botches the future eval.
										#
										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  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		List[str]   ):
										'''simple docstring'''
										# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
										lowercase :							Optional[Any]    		={
										    # 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 :							Union[str, Any]    		={"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
										lowercase :							Optional[int]    		="""Running training"""
										with CaptureStderr() as cl:
																self.run_seqaseq_quick(**__A		,					extra_args_str=data['''extra_args_str''']   )
										lowercase :							List[Any]    		=len(re.findall(__A		,					cl.err   )   )
										self.assertEqual(__A		,					data['''n_matches''']   )







				@slow
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.run_trainer(
										    eval_steps=2		,					max_len=128		,					model_name=__A		,					learning_rate=3E-4		,					num_train_epochs=10		,					distributed=__A		,					)

										# Check metrics
										lowercase :							str    		=TrainerState.load_from_json(os.path.join(__A		,					'''trainer_state.json'''   )   ).log_history
										lowercase :							List[str]    		=[log for log in logs if """eval_loss""" in log.keys()]
										lowercase :							int    		=eval_metrics[0]
										lowercase :							List[Any]    		=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 :							Any    		=os.listdir(__A   )
										lowercase :							Tuple    		={os.path.basename(__A   ) for p in contents}
										assert "generated_predictions.txt" in contents
										assert "predict_results.json" in contents







				@slow
				@require_bitsandbytes
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										from transformers.training_args import OptimizerNames

										def train_and_return_metrics(UpperCAmelCase__     :		str   ) -> Tuple[int, float]:
																lowercase :							List[Any]    		="""--skip_memory_metrics 0"""

																lowercase :							Any    		=self.run_trainer(
																    max_len=128		,					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 :							List[Any]    		=TrainerState.load_from_json(Path(__A		,					'''trainer_state.json'''   )   ).log_history
																lowercase :							List[Any]    		=int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20   )
																lowercase :							Any    		=int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20   )

																lowercase :							Union[str, Any]    		=logs[0]["""train_loss"""]
																return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

										lowercase :							str    		=train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value   )
										lowercase :							Any    		=train_and_return_metrics(OptimizerNames.ADAMW_BNB.value   )

										lowercase :							Optional[Any]    		=gpu_alloc_mem_orig - gpu_alloc_mem_bnb

										lowercase :							Dict    		=gpu_peak_mem_orig + gpu_alloc_mem_orig
										lowercase :							List[Any]    		=gpu_peak_mem_bnb + gpu_alloc_mem_bnb
										lowercase :							Union[str, 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 :							List[Any]    		=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(
										    __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  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		float = 3E-3		,					UpperCAmelCase__     :		str = "adafactor"		,					UpperCAmelCase__     :		bool = False		,					UpperCAmelCase__     :		str = None		,					UpperCAmelCase__     :		int = 0		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		int = None		,					):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
										lowercase :							Optional[int]    		=self.get_auto_remove_tmp_dir()
										lowercase :							str    		=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()

										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(__A   )}
        '''.split()

										lowercase :							str    		="""
            --do_predict
        """.split()

										lowercase :							Union[str, 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 :							Union[str, Any]    		=get_gpu_count()
																lowercase :							Tuple    		=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 :							Union[str, Any]    		=[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 :							Any    		=["""run_translation.py"""] + args
																with patch.object(__A		,					'''argv'''		,					__A   ):
																						main()

										return output_dir








UpperCAmelCase_							:					dict[str, float]		 =   {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.35_5818,
}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   float       )					->				float:
	if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
		__A :			Optional[int]		    =			(
		    f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
		    f"""Valid values are: {", ".join(a__       )}"""
		)
		raise ValueError(a__       )
	return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





