






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)
snake_case     					=		logging.getLogger()

snake_case     					=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


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


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

       SCREAMING_SNAKE_CASE							:   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"   )

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

       SCREAMING_SNAKE_CASE							:   List[str]						    =     os.path.join(UpperCAmelCase_       ,			"metrics.json"   )
       with open(UpperCAmelCase_   ) as f:
         SCREAMING_SNAKE_CASE							:   Dict						    =     json.load(UpperCAmelCase_   )
       return result


     @require_torch_gpu
     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self._run_finetune(gpus=1   )
       self.assertGreaterEqual(result["test"][0]["test_avg_em"]       ,			0.2   )


     @require_torch_multi_gpu
     def 					_A							(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     self._run_finetune(gpus=2   )
       self.assertGreaterEqual(result["test"][0]["test_avg_em"]       ,			0.2   )


     @require_torch_gpu
     @require_ray
     def 					_A							(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     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 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   int						    =     self._run_finetune(gpus=1       ,			distributed_retriever="ray"   )
       self.assertGreaterEqual(result["test"][0]["test_avg_em"]       ,			0.2   )








"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


