






"""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					:				Optional[Any]					    =						"""sshleifer/student_marian_en_ro_6_1"""
_SCREAMING_SNAKE_CASE					:				Dict					    =						"""sshleifer/tiny-mbart"""






@require_torch
class      a						(					a_		):






    def    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							str=False       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=True       ,						__SCREAMING_SNAKE_CASE     :							Dict=True       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=True       ,						)     ->		Optional[int]:
      lowerCamelCase_         =						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       ,						)
      lowerCamelCase_         =						TrainerState.load_from_json(os.path.join(_a       ,						'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']       ,						_a					)
        assert not math.isnan(float(last_step_stats['eval_loss']					)					), "eval_loss must not be `nan`"






    @require_torch_non_multi_gpu
    def    UpperCamelCase       (       self     :							str					)     ->		Optional[Any]:
      self.run_seqaseq_quick()






    @require_torch_multi_gpu
    def    UpperCamelCase       (       self     :							int					)     ->		Any:
      self.run_seqaseq_quick(distributed=_a					)






    @require_torch_multi_gpu
    def    UpperCamelCase       (       self     :							Any					)     ->		Optional[int]:
      self.run_seqaseq_quick(distributed=_a					)






    @unittest.skip('Requires an update of the env running those tests'					)
    @require_torch_multi_gpu
    @require_fairscale
    def    UpperCamelCase       (       self     :							Any					)     ->		List[Any]:
      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    UpperCamelCase       (       self     :							str					)     ->		Tuple:
      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    UpperCamelCase       (       self     :							Tuple					)     ->		Dict:
      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    UpperCamelCase       (       self     :							str					)     ->		str:
      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    UpperCamelCase       (       self     :							int					)     ->		Optional[Any]:
      # 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    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Dict					)     ->		Optional[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(**_a       ,						extra_args_str=data['extra_args_str']					)
      lowerCamelCase_         =						len(re.findall(_a       ,						cl.err					)					)
      self.assertEqual(_a       ,						data['n_matches']					)






    @slow
    def    UpperCamelCase       (       self     :							int					)     ->		int:
      lowerCamelCase_         =						self.run_trainer(
          eval_steps=2       ,						max_len=128       ,						model_name=_a       ,						learning_rate=3e-4       ,						num_train_epochs=10       ,						distributed=_a       ,						)

      # Check metrics
      lowerCamelCase_         =						TrainerState.load_from_json(os.path.join(_a       ,						'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']       ,						_a					)

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






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

      def train_and_return_metrics(__SCREAMING_SNAKE_CASE     :							Union[str, Any]					) -> Tuple[int, float]:
        lowerCamelCase_         =						"--skip_memory_metrics 0"

        lowerCamelCase_         =						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
        lowerCamelCase_         =						TrainerState.load_from_json(Path(_a       ,						'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_         =						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value					)
      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(
          _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    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any] = 3e-3       ,						__SCREAMING_SNAKE_CASE     :							Dict = "adafactor"       ,						__SCREAMING_SNAKE_CASE     :							Tuple = False       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = None       ,						__SCREAMING_SNAKE_CASE     :							List[str] = 0       ,						__SCREAMING_SNAKE_CASE     :							Tuple = True       ,						__SCREAMING_SNAKE_CASE     :							List[str] = True       ,						__SCREAMING_SNAKE_CASE     :							List[str] = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = True       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any] = None       ,						)     ->		int:
      lowerCamelCase_         =						self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
      lowerCamelCase_         =						self.get_auto_remove_tmp_dir()
      lowerCamelCase_         =						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()

      lowerCamelCase_         =						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()

      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'''
                -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()
        lowerCamelCase_         =						[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:
        lowerCamelCase_         =						["run_translation.py"] + args
        with patch.object(_a       ,						'argv'       ,						_a					):
          main()

      return output_dir






"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_fnet import FNetTokenizer
else:
   _A			:       Union[str, Any]   		=       None

_A			:       str   		=       logging.get_logger(__name__)
_A			:       Tuple   		=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A			:       Optional[Any]   		=       {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

_A			:       Optional[Any]   		=       {
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

_A			:       List[str]   		=       """▁"""

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """token_type_ids"""]
   __lowerCAmelCase            =				FNetTokenizer
   def __init__(			self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=True	,    _a="<unk>"	,    _a="[SEP]"	,    _a="<pad>"	,    _a="[CLS]"	,    _a="[MASK]"	,    **_a	,    ):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          lowercase    :				int       		=							(
              AddedToken(_a	,    lstrip=_a	,    rstrip=_a	,    normalized=_a				)
              if isinstance(_a	,    _a				)
              else mask_token
          )

          super().__init__(
              _a	,    tokenizer_file=_a	,    do_lower_case=_a	,    remove_space=_a	,    keep_accents=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    **_a	,    )

          lowercase    :				Dict       		=							do_lower_case
          lowercase    :				Union[str, Any]       		=							remove_space
          lowercase    :				Any       		=							keep_accents
          lowercase    :				List[Any]       		=							vocab_file
          lowercase    :				Union[str, Any]       		=							False if not self.vocab_file else True
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[Any]       		=							[self.sep_token_id]
          lowercase    :				str       		=							[self.cls_token_id]
          if token_ids_a is None:
                 return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Any       		=							[self.sep_token_id]
          lowercase    :				Dict       		=							[self.cls_token_id]

          if token_ids_a is None:
                 return len(cls + token_ids_a + sep				) * [0]
          return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				Optional[Any]       		=							os.path.join(
              _a	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

          if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				):
                 copyfile(self.vocab_file	,    _a				)

          return (out_vocab_file,)


