"""simple docstring"""






# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


__A			   = float("nan")


class     UpperCAmelCase						:



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[Any]					=      sys.stdout
       lowercase__:   str					=      open(__lowerCamelCase    ,			'''a'''							)






    def __getattr__(   self    ,			_UpperCAmelCase							):
       return getattr(self.stdout    ,			__lowerCamelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       self.stdout.write(__lowerCamelCase							)
       # strip tqdm codes
       self.file.write(re.sub(r'''^.*\r'''    ,			''''''    ,			__lowerCamelCase    ,			0    ,			re.M							)							)



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase=8_0       ,    __UpperCAmelCase=False )  ->  Dict:
   lowercase__:   Tuple					=      []

   # deal with critical env vars
   lowercase__:   Dict					=      ["CUDA_VISIBLE_DEVICES"]
   for key in env_keys:
      lowercase__:   Optional[int]					=      os.environ.get(UpperCamelCase__       ,    UpperCamelCase__ )
      if val is not None:
         cmd.append(F"""{key}={val}""" )

    # python executable (not always needed if the script is executable)
   lowercase__:   Optional[int]					=      sys.executable if full_python_path else sys.executable.split('''/''' )[-1]
   cmd.append(UpperCamelCase__ )

   # now the normal args
   cmd += list(map(shlex.quote       ,    sys.argv ) )

   # split up into up to MAX_WIDTH lines with shell multi-line escapes
   lowercase__:   Tuple					=      []
   lowercase__:   Dict					=      ""
   while len(UpperCamelCase__ ) > 0:
      current_line += F"""{cmd.pop(0 )} """
      if len(UpperCamelCase__ ) == 0 or len(UpperCamelCase__ ) + len(cmd[0] ) + 1 > max_width - 1:
         lines.append(UpperCamelCase__ )
         lowercase__:   Union[str, Any]					=      ""
   return "\\\n".join(UpperCamelCase__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Optional[Any]:
   # unwrap multi-line input
   lowercase__:   Union[str, Any]					=      re.sub(R'''[\\\n]+'''       ,    ''' '''       ,    args.base_cmd )

   # remove --output_dir if any and set our own
   lowercase__:   int					=      re.sub('''--output_dir\s+[^\s]+'''       ,    ''''''       ,    args.base_cmd )
   args.base_cmd += F""" --output_dir {output_dir}"""

   # ensure we have --overwrite_output_dir
   lowercase__:   int					=      re.sub('''--overwrite_output_dir\s+'''       ,    ''''''       ,    args.base_cmd )
   args.base_cmd += " --overwrite_output_dir"

   return [sys.executable] + shlex.split(args.base_cmd )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Union[str, Any]:
   # Enable to debug everything but the run itself, to do it fast and see the progress.
   # This is useful for debugging the output formatting quickly - we can remove it later once
   # everybody is happy with the output
   if 0:
      import random
      from time import sleep

      sleep(0 )
      return dict(
          {k: random.uniform(0       ,    1_0_0 ) for k in metric_keys}       ,    **{target_metric_key: random.choice([nan, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2] )}       ,    )

   lowercase__:   Dict					=      subprocess.run(UpperCamelCase__       ,    capture_output=UpperCamelCase__       ,    text=UpperCamelCase__ )

   if verbose:
      print('''STDOUT'''       ,    result.stdout )
      print('''STDERR'''       ,    result.stderr )

   # save the streams
   lowercase__:   Tuple					=      variation.replace(''' '''       ,    '''-''' )
   with open(Path(UpperCamelCase__ ) / F"""log.{prefix}.stdout.txt"""       ,    '''w''' ) as f:
      f.write(result.stdout )
   with open(Path(UpperCamelCase__ ) / F"""log.{prefix}.stderr.txt"""       ,    '''w''' ) as f:
      f.write(result.stderr )

   if result.returncode != 0:
      if verbose:
         print('''failed''' )
      return {target_metric_key: nan}

   with io.open(F"""{output_dir}/all_results.json"""       ,    '''r'''       ,    encoding='''utf-8''' ) as f:
      lowercase__:   List[str]					=      json.load(UpperCamelCase__ )

   # filter out just the keys we want
   return {k: v for k, v in metrics.items() if k in metric_keys}



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    )  ->  Any:
   lowercase__:   Union[str, Any]					=      []
   lowercase__:   Optional[int]					=      []
   lowercase__:   Any					=      F"""{id}: {variation:<{longest_variation_len}}"""
   lowercase__:   Dict					=      F"""{preamble}: """
   lowercase__:   Union[str, Any]					=      set(report_metric_keys + [target_metric_key] )
   for i in tqdm(range(UpperCamelCase__ )       ,    desc=UpperCamelCase__       ,    leave=UpperCamelCase__ ):
      lowercase__:   Optional[Any]					=      process_run_single(
          UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ )
      lowercase__:   Optional[Any]					=      single_run_metrics[target_metric_key]
      if not math.isnan(UpperCamelCase__ ):
         metrics.append(UpperCamelCase__ )
         results.append(UpperCamelCase__ )
         outcome += "✓"
      else:
         outcome += "✘"
   lowercase__:   str					=      F"""\33[2K\r{outcome}"""
   if len(UpperCamelCase__ ) > 0:
      lowercase__:   List[str]					=      {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
      lowercase__:   Any					=      round(mean_metrics[target_metric_key]       ,    2 )
      lowercase__:   Tuple					=      F"""{outcome} {mean_target}"""
      if len(UpperCamelCase__ ) > 1:
         results_str += F""" {tuple(round(UpperCamelCase__       ,    2 ) for x in results )}"""
      print(UpperCamelCase__ )
      lowercase__:   Optional[int]					=      variation
      return mean_metrics
   else:
      print(UpperCamelCase__ )
      return {variation_key: variation, target_metric_key: nan}



def SCREAMING_SNAKE_CASE__			(   )  ->  List[Any]:
   lowercase__:   int					=      torch.cuda.get_device_properties(torch.device('''cuda''' ) )
   return F"""\nDatetime    : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch       : {torch.__version__}\ncuda        : {torch.version.cuda}\npython      : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**3_0:0.2f}GB\n"""



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Dict:
   lowercase__:   Any					=      pd.DataFrame(UpperCamelCase__ )
   lowercase__:   List[str]					=      "variation"
   lowercase__:   List[Any]					=      "diff_%"

   lowercase__:   int					=      nan
   if base_variation is not None and len(df[df[variation_key] == base_variation] ):
      # this may still return nan
      lowercase__:   int					=      df.loc[df[variation_key] == base_variation][target_metric_key].item()
   if math.isnan(UpperCamelCase__ ):
      # as a fallback, use the minimal value as the sentinel
      lowercase__:   List[str]					=      df.loc[df[target_metric_key] != nan][target_metric_key].min()

   # create diff column if possible
   if not math.isnan(UpperCamelCase__ ):
      lowercase__:   Optional[Any]					=      df.apply(
          lambda __UpperCAmelCase : round(1_0_0 * (r[target_metric_key] - sentinel_value) / sentinel_value )
          if not math.isnan(r[target_metric_key] )
          else 0       ,    axis='''columns'''       ,    )

   # re-order columns
   lowercase__:   Union[str, Any]					=      [variation_key, target_metric_key, diff_key, *report_metric_keys]
   lowercase__:   Any					=      df.reindex(UpperCamelCase__       ,    axis='''columns''' )  # reorder cols

   # capitalize
   lowercase__:   Tuple					=      df.rename(str.capitalize       ,    axis='''columns''' )

   # make the cols as narrow as possible
   lowercase__:   List[str]					=      df.rename(lambda __UpperCAmelCase : c.replace('''_'''       ,    '''<br>''' )       ,    axis='''columns''' )
   lowercase__:   Union[str, Any]					=      df.rename(lambda __UpperCAmelCase : c.replace('''_'''       ,    '''\n''' )       ,    axis='''columns''' )

   lowercase__:   Optional[int]					=      ["", "Copy between the cut-here-lines and paste as is to github or a forum"]
   report += ["----------8<-----------------8<--------"]
   report += ["*** Results:", df_github.to_markdown(index=UpperCamelCase__       ,    floatfmt='''.2f''' )]
   report += ["```"]
   report += ["*** Setup:", get_versions()]
   report += ["*** The benchmark command line was:", get_original_command()]
   report += ["```"]
   report += ["----------8<-----------------8<--------"]
   report += ["*** Results (console):", df_console.to_markdown(index=UpperCamelCase__       ,    floatfmt='''.2f''' )]

   print('''\n\n'''.join(UpperCamelCase__ ) )



def SCREAMING_SNAKE_CASE__			(   )  ->  List[str]:
   lowercase__:   int					=      argparse.ArgumentParser()
   parser.add_argument(
       '''--base-cmd'''       ,    default=UpperCamelCase__       ,    type=UpperCamelCase__       ,    required=UpperCamelCase__       ,    help='''Base cmd'''       ,    )
   parser.add_argument(
       '''--variations'''       ,    default=UpperCamelCase__       ,    type=UpperCamelCase__       ,    nargs='''+'''       ,    required=UpperCamelCase__       ,    help='''Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\''''       ,    )
   parser.add_argument(
       '''--base-variation'''       ,    default=UpperCamelCase__       ,    type=UpperCamelCase__       ,    help='''Baseline variation to compare to. if None the minimal target value will be used to compare against'''       ,    )
   parser.add_argument(
       '''--target-metric-key'''       ,    default=UpperCamelCase__       ,    type=UpperCamelCase__       ,    required=UpperCamelCase__       ,    help='''Target metric key in output_dir/all_results.json, e.g., train_samples_per_second'''       ,    )
   parser.add_argument(
       '''--report-metric-keys'''       ,    default=''''''       ,    type=UpperCamelCase__       ,    help='''Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples'''       ,    )
   parser.add_argument(
       '''--repeat-times'''       ,    default=1       ,    type=UpperCamelCase__       ,    help='''How many times to re-run each variation - an average will be reported'''       ,    )
   parser.add_argument(
       '''--output_dir'''       ,    default='''output_benchmark'''       ,    type=UpperCamelCase__       ,    help='''The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked'''       ,    )
   parser.add_argument(
       '''--verbose'''       ,    default=UpperCamelCase__       ,    action='''store_true'''       ,    help='''Whether to show the outputs of each run or just the benchmark progress'''       ,    )
   lowercase__:   int					=      parser.parse_args()

   lowercase__:   Union[str, Any]					=      args.output_dir
   Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
   lowercase__:   Tuple					=      get_base_command(UpperCamelCase__       ,    UpperCamelCase__ )

   # split each dimension into its --foo variations
   lowercase__:   Dict					=      [list(map(str.strip       ,    re.split(R'''\|'''       ,    UpperCamelCase__ ) ) ) for x in args.variations]
   # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
   # while stripping white space for inputs that were empty
   lowercase__:   Union[str, Any]					=      list(map(str.strip       ,    map(''' '''.join       ,    itertools.product(*UpperCamelCase__ ) ) ) )
   lowercase__:   Union[str, Any]					=      max(len(UpperCamelCase__ ) for x in variations )

   # split wanted keys
   lowercase__:   str					=      args.report_metric_keys.split()

   # capture prints into a log file for convenience
   lowercase__:   Optional[int]					=      F"""benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S' )}.txt"""
   print(F"""\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt""" )
   print(F"""and this script's output is also piped into {report_fn}""" )

   lowercase__:   Tuple					=      Tee(UpperCamelCase__ )

   print(F"""\n*** Running {len(UpperCamelCase__ )} benchmarks:""" )
   print(F"""Base command: {' '.join(UpperCamelCase__ )}""" )

   lowercase__:   str					=      "variation"
   lowercase__:   Union[str, Any]					=      []
   for id, variation in enumerate(tqdm(UpperCamelCase__       ,    desc='''Total completion: '''       ,    leave=UpperCamelCase__ ) ):
      lowercase__:   Dict					=      base_cmd + variation.split()
      results.append(
          process_run(
              id + 1       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    args.target_metric_key       ,    UpperCamelCase__       ,    args.repeat_times       ,    UpperCamelCase__       ,    args.verbose       ,    ) )

   process_results(UpperCamelCase__       ,    args.target_metric_key       ,    UpperCamelCase__       ,    args.base_variation       ,    UpperCamelCase__ )


if __name__ == "__main__":
       main()




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

from tokenizers import processors

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


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    lowerCAmelCase__      		= None


lowerCAmelCase__      		= logging.get_logger(__name__)


lowerCAmelCase__      		= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__      		= {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__      		= {
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# fmt: off
lowerCAmelCase__      		= ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]
     __SCREAMING_SNAKE_CASE  			= MBartTokenizer

     __SCREAMING_SNAKE_CASE  			= []
     __SCREAMING_SNAKE_CASE  			= []


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<mask>" ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[int]:
            # Mask token behave like a normal word, i.e. include the space before it
            _A      :       List[str]				    = AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,						__lowerCamelCase) else mask_token

            super().__init__(
                vocab_file=__lowerCamelCase ,						tokenizer_file=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						sep_token=__lowerCamelCase ,						cls_token=__lowerCamelCase ,						unk_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						mask_token=__lowerCamelCase ,						src_lang=__lowerCamelCase ,						tgt_lang=__lowerCamelCase ,						additional_special_tokens=__lowerCamelCase ,						**__lowerCamelCase ,						)

            _A      :       Union[str, Any]				    = vocab_file
            _A      :       int				    = False if not self.vocab_file else True

            _A      :       Optional[int]				    = FAIRSEQ_LANGUAGE_CODES.copy()

            if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   _additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in _additional_special_tokens])

            self.add_special_tokens({"additional_special_tokens": _additional_special_tokens})
            _A      :       Union[str, Any]				    = {
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _A      :       Optional[int]				    = src_lang if src_lang is not None else "en_XX"
            _A      :       Union[str, Any]				    = self.convert_tokens_to_ids(self._src_lang)
            _A      :       int				    = tgt_lang
            self.set_src_lang_special_tokens(self._src_lang)


     @property
     def  _lowerCamelCase (						self)			->				str:
            return self._src_lang


     @src_lang.setter
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Dict				    = new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       List[str]				    = [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 + sep + token_ids_a + sep) * [0]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Dict:
            if src_lang is None or tgt_lang is None:
                   raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
            _A      :       str				    = src_lang
            _A      :       Any				    = self(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
            _A      :       Tuple				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       Dict				    = tgt_lang_id
            return inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = "en_XX" ,						__lowerCamelCase = None ,						__lowerCamelCase = "ro_RO" ,						**__lowerCamelCase ,						)			->				BatchEncoding:
            _A      :       Any				    = src_lang
            _A      :       int				    = tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            return self.set_src_lang_special_tokens(self.src_lang)


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       int				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       int				    = []
            _A      :       List[str]				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Union[str, Any]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       str				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       List[Any]				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Optional[int]				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       List[Any]				    = []
            _A      :       str				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Optional[int]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       int				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       str				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                       "tokenizer.")

            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
                   return
            _A      :       int				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

            return (out_vocab_file,)
