'''simple docstring'''





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:
    SCREAMING_SNAKE_CASE_:     Optional[Any]  =None


SCREAMING_SNAKE_CASE_:     List[Any]  =logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={
    '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',
    },
}

SCREAMING_SNAKE_CASE_:     Optional[int]  ={
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# fmt: off
SCREAMING_SNAKE_CASE_:     Union[str, Any]  =['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        __A      ( UpperCamelCase__	):
    a__       :						int          =  VOCAB_FILES_NAMES
    a__       :						Optional[int]          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						List[str]          =  PRETRAINED_VOCAB_FILES_MAP
    a__       :						Optional[int]          =  ["""input_ids""", """attention_mask"""]
    a__       :						Any          =  MBartTokenizer

    a__       :						List[int]          =  []
    a__       :						List[int]          =  []



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

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

         UpperCAmelCase_           =   vocab_file
         UpperCAmelCase_           =   False if not self.vocab_file else True

         UpperCAmelCase_           =   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} )
         UpperCAmelCase_           =   {
             lang_code: self.convert_tokens_to_ids(__a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         UpperCAmelCase_           =   src_lang if src_lang is not None else "en_XX"
         UpperCAmelCase_           =   self.convert_tokens_to_ids(self._src_lang )
         UpperCAmelCase_           =   tgt_lang
         self.set_src_lang_special_tokens(self._src_lang )



    @property
    def 					_lowercase						(self    :    str ):
         return self._src_lang



    @src_lang.setter
    def 					_lowercase						(self    :    int   ,			__a    :    str ):
         UpperCAmelCase_           =   new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    List[int]   ,			__a    :    Optional[List[int]] = None ):
         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 					_lowercase						(self    :    Any   ,			__a    :    List[int]   ,			__a    :    Optional[List[int]] = None ):
         UpperCAmelCase_           =   [self.sep_token_id]
         UpperCAmelCase_           =   [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 					_lowercase						(self    :    Any   ,			__a    :    int   ,			__a    :    str   ,			__a    :    Optional[str]   ,			__a    :    Optional[str]   ,			**__a    :    List[str] ):
         if src_lang is None or tgt_lang is None:
              raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
         UpperCAmelCase_           =   src_lang
         UpperCAmelCase_           =   self(__a   ,			add_special_tokens=__a   ,			return_tensors=__a   ,			**__a )
         UpperCAmelCase_           =   self.convert_tokens_to_ids(__a )
         UpperCAmelCase_           =   tgt_lang_id
         return inputs



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    List[str]   ,			__a    :    str = "en_XX"   ,			__a    :    Optional[List[str]] = None   ,			__a    :    str = "ro_RO"   ,			**__a    :    int   ,			):
         UpperCAmelCase_           =   src_lang
         UpperCAmelCase_           =   tgt_lang
         return super().prepare_seqaseq_batch(__a   ,			__a   ,			**__a )



    def 					_lowercase						(self    :    Optional[int] ):
         return self.set_src_lang_special_tokens(self.src_lang )



    def 					_lowercase						(self    :    Optional[Any] ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang )



    def 					_lowercase						(self    :    int   ,			__a    :    Optional[int] ):
         UpperCAmelCase_           =   self.convert_tokens_to_ids(__a )
         UpperCAmelCase_           =   []
         UpperCAmelCase_           =   [self.eos_token_id, self.cur_lang_code]

         UpperCAmelCase_           =   self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCAmelCase_           =   self.convert_ids_to_tokens(self.suffix_tokens )

         UpperCAmelCase_           =   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 					_lowercase						(self    :    Dict   ,			__a    :    str ):
         UpperCAmelCase_           =   self.convert_tokens_to_ids(__a )
         UpperCAmelCase_           =   []
         UpperCAmelCase_           =   [self.eos_token_id, self.cur_lang_code]

         UpperCAmelCase_           =   self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCAmelCase_           =   self.convert_ids_to_tokens(self.suffix_tokens )

         UpperCAmelCase_           =   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 					_lowercase						(self    :    List[str]   ,			__a    :    str   ,			__a    :    Optional[str] = None ):
         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(__a ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" )
              return
         UpperCAmelCase_           =   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,)





'''simple docstring'''





import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.getLogger()

SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    str ):
         os.makedirs(__a   ,			exist_ok=__a )
         UpperCAmelCase_           =   {"source": "What is love ?", "target": "life"}
         UpperCAmelCase_           =   {"train": 12, "val": 2, "test": 2}
         for split in ["train", "test", "val"]:
              for field in ["source", "target"]:
                   UpperCAmelCase_           =   "\n".join([contents[field]] * n_lines[split] )
                   with open(os.path.join(__a   ,			f"""{split}.{field}""" )   ,			"w" ) as f:
                        f.write(__a )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    str = "pytorch" ):
         UpperCAmelCase_           =   self.get_auto_remove_tmp_dir()
         UpperCAmelCase_           =   os.path.join(__a   ,			"output" )
         UpperCAmelCase_           =   os.path.join(__a   ,			"data" )
         self._create_dummy_data(data_dir=__a )

         UpperCAmelCase_           =   f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

         if gpus > 0:
              testargs.append(f"""--gpus={gpus}""" )
              if is_apex_available():
                   testargs.append("--fp16" )
         else:
              testargs.append("--gpus=0" )
              testargs.append("--distributed_backend=ddp_cpu" )
              testargs.append("--num_processes=2" )

         UpperCAmelCase_           =   [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
         execute_subprocess_async(__a   ,			env=self.get_env() )

         UpperCAmelCase_           =   os.path.join(__a   ,			"metrics.json" )
         with open(__a ) as f:
              UpperCAmelCase_           =   json.load(__a )
         return result



    @require_torch_gpu
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1 )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )



    @require_torch_multi_gpu
    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self._run_finetune(gpus=2 )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )



    @require_torch_gpu
    @require_ray
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1   ,			distributed_retriever="ray" )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )






    @require_torch_multi_gpu
    @require_ray
    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1   ,			distributed_retriever="ray" )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )




