

"""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


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_     :			Union[str, Any]	  =			'''sshleifer/student_marian_en_ro_6_1'''
SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			'''sshleifer/tiny-mbart'''


@require_torch
class 		_A       (			__a					):





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						)   ->					List[str]:
         lowerCamelCase__						=			self.run_trainer(
             eval_steps=1    ,						max_len=12    ,						model_name=SCREAMING_SNAKE_CASE__    ,						num_train_epochs=1    ,						distributed=SCREAMING_SNAKE_CASE__    ,						extra_args_str=SCREAMING_SNAKE_CASE__    ,						predict_with_generate=SCREAMING_SNAKE_CASE__    ,						do_train=SCREAMING_SNAKE_CASE__    ,						do_eval=SCREAMING_SNAKE_CASE__    ,						do_predict=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__    ,						"trainer_state.json"		)		).log_history

         if not do_eval:
            return

         lowerCamelCase__						=			[log for log in logs if "eval_loss" in log.keys()]

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

            lowerCamelCase__						=			eval_metrics[-1]
            assert isinstance(last_step_stats["eval_bleu"]    ,						SCREAMING_SNAKE_CASE__		)
            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]:
         self.run_seqaseq_quick()





      @require_torch_multi_gpu
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__		)





      @require_torch_multi_gpu
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__		)





      @unittest.skip("Requires an update of the env running those tests"		)
      @require_torch_multi_gpu
      @require_fairscale
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         self.run_seqaseq_quick(distributed=SCREAMING_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 					_lowerCamelCase  (  self		)   ->					List[Any]:
         self.run_seqaseq_quick(distributed=SCREAMING_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 					_lowerCamelCase  (  self		)   ->					str:
         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__    ,						extra_args_str="--sharded_ddp zero_dp_2"    ,						predict_with_generate=SCREAMING_SNAKE_CASE__		)





      @unittest.skip("Requires an update of the env running those tests"		)
      @require_torch_multi_gpu
      @require_fairscale
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         self.run_seqaseq_quick(
             distributed=SCREAMING_SNAKE_CASE__    ,						extra_args_str="--sharded_ddp zero_dp_2 --fp16"    ,						predict_with_generate=SCREAMING_SNAKE_CASE__		)





      @require_apex
      @require_torch_gpu
      def 					_lowerCamelCase  (  self		)   ->					str:
         # 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=SCREAMING_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=SCREAMING_SNAKE_CASE__    ,						extra_args_str="--fp16 --fp16_backend=apex"		)





      @parameterized.expand(["base", "low", "high", "mixed"]		)
      @require_torch_multi_gpu
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
         lowerCamelCase__						=			{
             # 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},
         }

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





      @slow
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			self.run_trainer(
             eval_steps=2    ,						max_len=128    ,						model_name=SCREAMING_SNAKE_CASE__    ,						learning_rate=3e-4    ,						num_train_epochs=10    ,						distributed=SCREAMING_SNAKE_CASE__    ,						)

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

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

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





      @slow
      @require_bitsandbytes
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(SCREAMING_SNAKE_CASE__		) -> Tuple[int, float]:
            lowerCamelCase__						=			"--skip_memory_metrics 0"

            lowerCamelCase__						=			self.run_trainer(
                max_len=128    ,						model_name=SCREAMING_SNAKE_CASE__    ,						learning_rate=3e-4    ,						num_train_epochs=1    ,						optim=SCREAMING_SNAKE_CASE__    ,						distributed=SCREAMING_SNAKE_CASE__    ,						extra_args_str=SCREAMING_SNAKE_CASE__    ,						do_eval=SCREAMING_SNAKE_CASE__    ,						do_predict=SCREAMING_SNAKE_CASE__    ,						n_gpus_to_use=1    ,						)

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

            lowerCamelCase__						=			logs[0]["train_loss"]
            return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value		)
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			train_and_return_metrics(OptimizerNames.ADAMW_BNB.value		)

         lowerCamelCase__						=			gpu_alloc_mem_orig - gpu_alloc_mem_bnb

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = 3e-3    ,						SCREAMING_SNAKE_CASE__ = "adafactor"    ,						SCREAMING_SNAKE_CASE__ = False    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = 0    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = None    ,						)   ->					List[str]:
         lowerCamelCase__						=			self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
         lowerCamelCase__						=			self.get_auto_remove_tmp_dir()
         lowerCamelCase__						=			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(SCREAMING_SNAKE_CASE__		)}\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(SCREAMING_SNAKE_CASE__		)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        '.split()

         lowerCamelCase__						=			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(SCREAMING_SNAKE_CASE__		)}\n        '.split()

         lowerCamelCase__						=			"\n            --do_predict\n        ".split()

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

         return output_dir







"""simple docstring"""




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class 		_A       (			__a				,       unittest.TestCase					):
      __a								=	PhobertTokenizer
      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					Any:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowerCamelCase__						=			["T@@", "i", "I", "R@@", "r", "e@@"]
         lowerCamelCase__						=			dict(zip(SCREAMING_SNAKE_CASE__    ,						range(len(SCREAMING_SNAKE_CASE__		)		)		)		)
         lowerCamelCase__						=			["#version: 0.2", "l à</w>"]
         lowerCamelCase__						=			{"unk_token": "<unk>"}

         lowerCamelCase__						=			os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"]		)
         lowerCamelCase__						=			os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["merges_file"]		)

         with open(self.vocab_file    ,						"w"    ,						encoding="utf-8"		) as fp:
            for token in vocab_tokens:
               fp.write(f'{token} {vocab_tokens[token]}\n'		)
         with open(self.merges_file    ,						"w"    ,						encoding="utf-8"		) as fp:
            fp.write("\n".join(SCREAMING_SNAKE_CASE__		)		)





      def 					_lowerCamelCase  (  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         kwargs.update(self.special_tokens_map		)
         return PhobertTokenizer.from_pretrained(self.tmpdirname    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			"Tôi là VinAI Research"
         lowerCamelCase__						=			"T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			PhobertTokenizer(self.vocab_file    ,						self.merges_file    ,						**self.special_tokens_map		)
         lowerCamelCase__						=			"Tôi là VinAI Research"
         lowerCamelCase__						=			"T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
         lowerCamelCase__						=			tokenizer.tokenize(SCREAMING_SNAKE_CASE__		)
         print(SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			tokens + [tokenizer.unk_token]

         lowerCamelCase__						=			[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__		)




