






"""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)
_UpperCamelCase    :   int   			=      logging.getLogger()

_UpperCamelCase    :   Optional[int]   			=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 						snake_case  (  UpperCAmelCase	):





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : str      ):


      '''simple docstring'''




      os.makedirs(A    ,		exist_ok=A      )
      a					:      Tuple    =       {'source': 'What is love ?', 'target': 'life'}
      a					:      Optional[Any]    =       {'train': 1_2, 'val': 2, 'test': 2}
      for split in ["train", "test", "val"]:
        for field in ["source", "target"]:
          a					:      List[Any]    =       '\n'.join([contents[field]] * n_lines[split]      )
          with open(os.path.join(A    ,		F'''{split}.{field}'''      )    ,		'w'      ) as f:
            f.write(A      )





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : int    ,		A     : str = "pytorch"      ):


      '''simple docstring'''




      a					:      Dict    =       self.get_auto_remove_tmp_dir()
      a					:      Tuple    =       os.path.join(A    ,		'output'      )
      a					:      str    =       os.path.join(A    ,		'data'      )
      self._create_dummy_data(data_dir=A      )

      a					:      List[str]    =       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'      )

      a					:      List[str]    =       [sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
      execute_subprocess_async(A    ,		env=self.get_env()      )

      a					:      List[Any]    =       os.path.join(A    ,		'metrics.json'      )
      with open(A      ) as f:
        a					:      str    =       json.load(A      )
      return result





    @require_torch_gpu
    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      Any    =       self._run_finetune(gpus=1      )
      self.assertGreaterEqual(result['test'][0]['test_avg_em']    ,		0.2      )





    @require_torch_multi_gpu
    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       self._run_finetune(gpus=2      )
      self.assertGreaterEqual(result['test'][0]['test_avg_em']    ,		0.2      )





    @require_torch_gpu
    @require_ray
    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      Any    =       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     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Any    =       self._run_finetune(gpus=1    ,		distributed_retriever='ray'      )
      self.assertGreaterEqual(result['test'][0]['test_avg_em']    ,		0.2      )












"""simple docstring"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase    :   int   			=						get_tests_dir('fixtures/test_sentencepiece_bpe.model')






class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					BartphoTokenizer
    __magic_name__   					=					False
    __magic_name__   					=					True





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      super().setUp()

      a					:      Any    =       ['▁This', '▁is', '▁a', '▁t', 'est']
      a					:      List[Any]    =       dict(zip(A    ,		range(len(A      )      )      )      )
      a					:      int    =       {'unk_token': '<unk>'}

      a					:      Tuple    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['monolingual_vocab_file']      )
      with open(self.monolingual_vocab_file    ,		'w'    ,		encoding='utf-8'      ) as fp:
        for token in vocab_tokens:
          fp.write(F'''{token} {vocab_tokens[token]}\n'''      )

      a					:      Optional[int]    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      tokenizer.save_pretrained(self.tmpdirname      )





    def     lowerCamelCase__				(							self     : Dict    ,		**A     : str      ):


      '''simple docstring'''




      kwargs.update(self.special_tokens_map      )
      return BartphoTokenizer.from_pretrained(self.tmpdirname    ,		**A      )





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       'This is a là test'
      a					:      List[Any]    =       'This is a<unk><unk> test'
      return input_text, output_text





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      a					:      int    =       'This is a là test'
      a					:      int    =       '▁This ▁is ▁a ▁l à ▁t est'.split()
      a					:      str    =       tokenizer.tokenize(A      )
      self.assertListEqual(A    ,		A      )

      a					:      Union[str, Any]    =       tokens + [tokenizer.unk_token]
      a					:      Dict    =       [4, 5, 6, 3, 3, 7, 8, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		A      )




