




import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__A     =    logging.get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       List[Any]	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       List[Any])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =question_encoder
   lowerCamelCase__:				Union[str, Any]        =generator
   lowerCamelCase__:				Dict        =self.question_encoder

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       Dict)	->str:



   '''simple docstring'''



   if os.path.isfile(UpperCamelCase__):
     raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""")
   os.makedirs(UpperCamelCase__	,			exist_ok=UpperCamelCase__)
   lowerCamelCase__:				int        =os.path.join(UpperCamelCase__	,			"question_encoder_tokenizer")
   lowerCamelCase__:				int        =os.path.join(UpperCamelCase__	,			"generator_tokenizer")
   self.question_encoder.save_pretrained(UpperCamelCase__)
   self.generator.save_pretrained(UpperCamelCase__)

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       int	,			UpperCAmelCase_    :       Any	,			**UpperCAmelCase_    :       Dict)	->Optional[Any]:



   '''simple docstring'''



   from ..auto.tokenization_auto import AutoTokenizer

   lowerCamelCase__:				int        =kwargs.pop("config"	,			UpperCamelCase__)

   if config is None:
     lowerCamelCase__:				int        =RagConfig.from_pretrained(UpperCamelCase__)

   lowerCamelCase__:				str        =AutoTokenizer.from_pretrained(
       UpperCamelCase__	,			config=config.question_encoder	,			subfolder="question_encoder_tokenizer")
   lowerCamelCase__:				Optional[Any]        =AutoTokenizer.from_pretrained(
       UpperCamelCase__	,			config=config.generator	,			subfolder="generator_tokenizer")
   return cls(question_encoder=UpperCamelCase__	,			generator=UpperCamelCase__)

 def __call__(self    :       Tuple	,			*UpperCAmelCase_    :       Union[str, Any]	,			**UpperCAmelCase_    :       Dict)	->str:



   '''simple docstring'''



   return self.current_tokenizer(*UpperCamelCase__	,			**UpperCamelCase__)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			*UpperCAmelCase_    :       str	,			**UpperCAmelCase_    :       Optional[Any])	->Dict:



   '''simple docstring'''



   return self.generator.batch_decode(*UpperCamelCase__	,			**UpperCamelCase__)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			*UpperCAmelCase_    :       Optional[int]	,			**UpperCAmelCase_    :       Optional[int])	->Optional[Any]:



   '''simple docstring'''



   return self.generator.decode(*UpperCamelCase__	,			**UpperCamelCase__)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =self.question_encoder

 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self.generator







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       Optional[List[str]] = None	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       str = "longest"	,			UpperCAmelCase_    :       str = None	,			UpperCAmelCase_    :       bool = True	,			**UpperCAmelCase_    :       Tuple	,			)	->BatchEncoding:



   '''simple docstring'''



   warnings.warn(
       "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
       "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
       "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
       "details"	,			UpperCamelCase__	,			)
   if max_length is None:
     lowerCamelCase__:				Tuple        =self.current_tokenizer.model_max_length
   lowerCamelCase__:				Dict        =self(
       UpperCamelCase__	,			add_special_tokens=UpperCamelCase__	,			return_tensors=UpperCamelCase__	,			max_length=UpperCamelCase__	,			padding=UpperCamelCase__	,			truncation=UpperCamelCase__	,			**UpperCamelCase__	,			)
   if tgt_texts is None:
     return model_inputs
   # Process tgt_texts
   if max_target_length is None:
     lowerCamelCase__:				Any        =self.current_tokenizer.model_max_length
   lowerCamelCase__:				int        =self(
       text_target=UpperCamelCase__	,			add_special_tokens=UpperCamelCase__	,			return_tensors=UpperCamelCase__	,			padding=UpperCamelCase__	,			max_length=UpperCamelCase__	,			truncation=UpperCamelCase__	,			**UpperCamelCase__	,			)
   lowerCamelCase__:				int        =labels["input_ids"]
   return model_inputs








import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =inspect.getfile(accelerate.test_utils)
   lowerCamelCase__:				str        =os.path.sep.join(
       mod_file.split(os.path.sep)[:-1] + ["scripts", "external_deps", "test_metrics.py"])

   from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

   lowerCamelCase__:				str        =test_metrics

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Any:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main	,			num_processes=1)

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main)

 @require_single_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->int:



   '''simple docstring'''



   self.test_metrics.main()







 @require_multi_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Dict:



   '''simple docstring'''



   print(F"""Found {torch.cuda.device_count()} devices.""")
   lowerCamelCase__:				Optional[Any]        =["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(UpperCAmelCase_	,			env=os.environ.copy())

