




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''Speech2TextFeatureExtractor'''
 UpperCamelCase						       =						'''Speech2TextTokenizer'''

 def __init__(    self :  Any						,							A_ :  Dict						,							A_ :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       super().__init__(A_						,							A_  )
       lowerCamelCase_							=   self.feature_extractor
       lowerCamelCase_							=   False

 def __call__(    self :  Dict						,							*A_ :  Optional[int]						,							**A_ :  str  )       ->     Dict:

       """simple docstring"""






       if self._in_target_context_manager:
             return self.current_processor(*A_						,							**A_  )

       if "raw_speech" in kwargs:
             warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'  )
             lowerCamelCase_							=   kwargs.pop('raw_speech'  )
       else:
             lowerCamelCase_							=   kwargs.pop('audio'						,							A_  )
       lowerCamelCase_							=   kwargs.pop('sampling_rate'						,							A_  )
       lowerCamelCase_							=   kwargs.pop('text'						,							A_  )
       if len(A_  ) > 0:
             lowerCamelCase_							=   args[0]
             lowerCamelCase_							=   args[1:]

       if audio is None and text is None:
             raise ValueError('You need to specify either an `audio` or `text` input to process.'  )

       if audio is not None:
             lowerCamelCase_							=   self.feature_extractor(A_						,							*A_						,							sampling_rate=A_						,							**A_  )
       if text is not None:
             lowerCamelCase_							=   self.tokenizer(A_						,							**A_  )

       if text is None:
             return inputs
       elif audio is None:
             return encodings
       else:
             lowerCamelCase_							=   encodings['input_ids']
             return inputs

 def     a__			(    self :  Any						,							*A_ :  List[str]						,							**A_ :  Any  )       ->     Optional[Any]:

       """simple docstring"""






       return self.tokenizer.batch_decode(*A_						,							**A_  )

 def     a__			(    self :  Union[str, Any]						,							*A_ :  Optional[int]						,							**A_ :  int  )       ->     Tuple:

       """simple docstring"""






       return self.tokenizer.decode(*A_						,							**A_  )





 @contextmanager
 def     a__			(    self :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your audio inputs, or in a separate call.'  )
       lowerCamelCase_							=   True
       lowerCamelCase_							=   self.tokenizer
       yield
       lowerCamelCase_							=   self.feature_extractor
       lowerCamelCase_							=   False









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)
lowerCamelCase    :		int			   =       logging.getLogger()

lowerCamelCase    :		Tuple			   =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 def     a__			(    self :  List[str]						,							A_ :  Optional[Any]  )       ->     int:

       """simple docstring"""






       os.makedirs(A_						,							exist_ok=A_  )
       lowerCamelCase_							=   {'source': 'What is love ?', 'target': 'life'}
       lowerCamelCase_							=   {'train': 12, 'val': 2, 'test': 2}
       for split in ["train", "test", "val"]:
             for field in ["source", "target"]:
                   lowerCamelCase_							=   '\n'.join([contents[field]] * n_lines[split]  )
                   with open(os.path.join(A_						,							f"""{split}.{field}"""  )						,							'w'  ) as f:
                         f.write(A_  )

 def     a__			(    self :  Optional[Any]						,							A_ :  int						,							A_ :  str = "pytorch"  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self.get_auto_remove_tmp_dir()
       lowerCamelCase_							=   os.path.join(A_						,							'output'  )
       lowerCamelCase_							=   os.path.join(A_						,							'data'  )
       self._create_dummy_data(data_dir=A_  )

       lowerCamelCase_							=   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'  )

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

       lowerCamelCase_							=   os.path.join(A_						,							'metrics.json'  )
       with open(A_  ) as f:
             lowerCamelCase_							=   json.load(A_  )
       return result

 @require_torch_gpu
 def     a__			(    self :  List[Any]  )       ->     Any:

       """simple docstring"""






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

 @require_torch_multi_gpu
 def     a__			(    self :  List[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   self._run_finetune(gpus=2  )
       self.assertGreaterEqual(result['test'][0]['test_avg_em']						,							0.2  )

 @require_torch_gpu
 @require_ray
 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   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 :  Dict  )       ->     Dict:

       """simple docstring"""






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



