






'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


UpperCamelCase      					=R"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"




@add_start_docstrings(__lowercase    )
class      A     (   __lowercase    ):






 """simple docstring"""





 __a      :    Union[str, Any]			  =    '''rag'''
 __a      :    Optional[int]			  =    True
 def __init__(				self      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=" / "      ,  __lowerCAmelCase=" // "      ,  __lowerCAmelCase=5      ,  __lowerCAmelCase=3_00      ,  __lowerCAmelCase=7_68      ,  __lowerCAmelCase=8      ,  __lowerCAmelCase="wiki_dpr"      ,  __lowerCAmelCase="train"      ,  __lowerCAmelCase="compressed"      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=0.0      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=None      ,  **__lowerCAmelCase      ,  ):
       super().__init__(
           bos_token_id=SCREAMING_SNAKE_CASE_      ,  pad_token_id=SCREAMING_SNAKE_CASE_      ,  eos_token_id=SCREAMING_SNAKE_CASE_      ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_      ,  forced_eos_token_id=SCREAMING_SNAKE_CASE_      ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_      ,  prefix=SCREAMING_SNAKE_CASE_      ,  vocab_size=SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_      ,  )
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       UpperCamelCase_					: Optional[Any]            =				kwargs.pop("""question_encoder"""			)
       UpperCamelCase_					: Optional[int]            =				question_encoder_config.pop("""model_type"""			)
       UpperCamelCase_					: Tuple            =				kwargs.pop("""generator"""			)
       UpperCamelCase_					: Any            =				decoder_config.pop("""model_type"""			)

       from ..auto.configuration_auto import AutoConfig

       UpperCamelCase_					: List[Any]            =				AutoConfig.for_model(SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_			)
       UpperCamelCase_					: str            =				AutoConfig.for_model(SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_			)

       UpperCamelCase_					: Any            =				reduce_loss
       UpperCamelCase_					: Optional[Any]            =				label_smoothing
       UpperCamelCase_					: List[str]            =				exclude_bos_score
       UpperCamelCase_					: Tuple            =				do_marginalize

       UpperCamelCase_					: Tuple            =				title_sep
       UpperCamelCase_					: Optional[int]            =				doc_sep
       UpperCamelCase_					: Union[str, Any]            =				n_docs
       UpperCamelCase_					: Any            =				max_combined_length

       UpperCamelCase_					: Dict            =				dataset
       UpperCamelCase_					: List[Any]            =				dataset_split
       UpperCamelCase_					: Union[str, Any]            =				index_name

       UpperCamelCase_					: str            =				retrieval_vector_size
       UpperCamelCase_					: List[Any]            =				retrieval_batch_size
       UpperCamelCase_					: Union[str, Any]            =				passages_path
       UpperCamelCase_					: List[str]            =				index_path
       UpperCamelCase_					: Tuple            =				use_dummy_dataset

       UpperCamelCase_					: Tuple            =				output_retrieved

       UpperCamelCase_					: str            =				do_deduplication

       UpperCamelCase_					: Tuple            =				use_cache

       if self.forced_eos_token_id is None:
             UpperCamelCase_					: Dict            =				getattr(self.generator      ,  """forced_eos_token_id"""      ,  SCREAMING_SNAKE_CASE_			)
 @classmethod
 def 	_UpperCAmelCase							(				cls      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  **__lowerCAmelCase			):
       return cls(question_encoder=question_encoder_config.to_dict()      ,  generator=generator_config.to_dict()      ,  **SCREAMING_SNAKE_CASE_			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				copy.deepcopy(self.__dict__			)
       UpperCamelCase_					: str            =				self.question_encoder.to_dict()
       UpperCamelCase_					: Optional[int]            =				self.generator.to_dict()
       UpperCamelCase_					: List[Any]            =				self.__class__.model_type
       return output








"""simple docstring"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async



def 							_lowerCamelCase			(			__a=None     ):
		if subparsers is not None:
				SCREAMING_SNAKE_CASE_    			=			subparsers.add_parser('''test'''     )
		else:
				SCREAMING_SNAKE_CASE_    			=			argparse.ArgumentParser('''Accelerate test command'''     )

		parser.add_argument(
		    '''--config_file''',				default=__a,				help=(
		        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
		        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
		        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
		        '''with \'huggingface\'.'''
		    ),				)

		if subparsers is not None:
				parser.set_defaults(func=__a     )
		return parser



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			os.path.sep.join(__file__.split(os.path.sep     )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']     )

		if args.config_file is None:
				SCREAMING_SNAKE_CASE_    			=			script_name
		else:
				SCREAMING_SNAKE_CASE_    			=			F'--config_file={args.config_file} {script_name}'

		SCREAMING_SNAKE_CASE_    			=			['''accelerate-launch'''] + test_args.split()
		SCREAMING_SNAKE_CASE_    			=			execute_subprocess_async(__a,				env=os.environ.copy()     )
		if result.returncode == 0:
				print('''Test is a success! You are ready for your distributed training!'''     )



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			test_command_parser()
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		test_command(__a     )


if __name__ == "__main__":
						main()