
'''simple docstring'''

import copy

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


snake_case_			=     R"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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


@add_start_docstrings(_lowercase	)
class 	a__     (				_lowercase	):
	__magic_name__		:    Union[str, Any]	    =     "rag"
	__magic_name__		:    List[str]	    =     True


	def __init__(self : Optional[Any],					__UpperCAmelCase : Dict=None,					__UpperCAmelCase : Any=True,					__UpperCAmelCase : int=None,					__UpperCAmelCase : str=None,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : int=None,					__UpperCAmelCase : Optional[Any]=" / ",					__UpperCAmelCase : Union[str, Any]=" // ",					__UpperCAmelCase : List[str]=5,					__UpperCAmelCase : Optional[Any]=300,					__UpperCAmelCase : Optional[int]=768,					__UpperCAmelCase : Dict=8,					__UpperCAmelCase : str="wiki_dpr",					__UpperCAmelCase : Union[str, Any]="train",					__UpperCAmelCase : List[Any]="compressed",					__UpperCAmelCase : Any=None,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : int=False,					__UpperCAmelCase : List[Any]=False,					__UpperCAmelCase : Union[str, Any]=0.0,					__UpperCAmelCase : List[Any]=True,					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : Optional[int]=False,					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : Dict=True,					__UpperCAmelCase : Optional[int]=None,					**__UpperCAmelCase : int,					)     ->			str:

					"""simple docstring"""

					super().__init__(
					    bos_token_id=__UpperCAmelCase,					pad_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase,					decoder_start_token_id=__UpperCAmelCase,					forced_eos_token_id=__UpperCAmelCase,					is_encoder_decoder=__UpperCAmelCase,					prefix=__UpperCAmelCase,					vocab_size=__UpperCAmelCase,					**__UpperCAmelCase,					)
					assert (
					    "question_encoder" in kwargs and "generator" in kwargs
					), "Config has to be initialized with question_encoder and generator config"
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= kwargs.pop('''question_encoder'''      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= question_encoder_config.pop('''model_type'''      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= kwargs.pop('''generator'''      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= decoder_config.pop('''model_type'''      )

					from ..auto.configuration_auto import AutoConfig

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= AutoConfig.for_model(__UpperCAmelCase,					**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= AutoConfig.for_model(__UpperCAmelCase,					**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= reduce_loss
					SCREAMING_SNAKE_CASE  :						List[Any]      					= label_smoothing
					SCREAMING_SNAKE_CASE  :						str      					= exclude_bos_score
					SCREAMING_SNAKE_CASE  :						List[str]      					= do_marginalize

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= title_sep
					SCREAMING_SNAKE_CASE  :						str      					= doc_sep
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= n_docs
					SCREAMING_SNAKE_CASE  :						List[str]      					= max_combined_length

					SCREAMING_SNAKE_CASE  :						str      					= dataset
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= dataset_split
					SCREAMING_SNAKE_CASE  :						List[Any]      					= index_name

					SCREAMING_SNAKE_CASE  :						List[Any]      					= retrieval_vector_size
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= retrieval_batch_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= passages_path
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= index_path
					SCREAMING_SNAKE_CASE  :						Tuple      					= use_dummy_dataset

					SCREAMING_SNAKE_CASE  :						int      					= output_retrieved

					SCREAMING_SNAKE_CASE  :						str      					= do_deduplication

					SCREAMING_SNAKE_CASE  :						List[Any]      					= use_cache

					if self.forced_eos_token_id is None:
									SCREAMING_SNAKE_CASE  :						Tuple      					= getattr(self.generator,					'''forced_eos_token_id''',					__UpperCAmelCase      )


	@classmethod
	def 				lowercase__  (cls : Any,					__UpperCAmelCase : PretrainedConfig,					__UpperCAmelCase : PretrainedConfig,					**__UpperCAmelCase : List[Any]      )     ->			PretrainedConfig:

					"""simple docstring"""

					return cls(question_encoder=question_encoder_config.to_dict(),					generator=generator_config.to_dict(),					**__UpperCAmelCase      )






	def 				lowercase__  (self : Any      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= copy.deepcopy(self.__dict__      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.question_encoder.to_dict()
					SCREAMING_SNAKE_CASE  :						str      					= self.generator.to_dict()
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.__class__.model_type
					return output









'''simple docstring'''

import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				return x + 2




class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Dict      					= {}
					SCREAMING_SNAKE_CASE  :						Any      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )

					SCREAMING_SNAKE_CASE  :						str      					= '''x = y'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''y''': 5}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 5, '''y''': 5}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= '''y = add_two(x)'''
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )

					# Won't work without the tool
					with CaptureStdout() as out:
									SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result is None
					assert "tried to execute add_two" in out.out


	def 				lowercase__  (self : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Any      					= {}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[Any]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}      )


	def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3\ny = 5'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )


	def 				lowercase__  (self : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''text = f\'This is x: {x}.\''''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Dict      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == "This is x: 3."
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''text''': '''This is x: 3.'''}      )


	def 				lowercase__  (self : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 2}      )

					SCREAMING_SNAKE_CASE  :						Any      					= {'''x''': 8}
					SCREAMING_SNAKE_CASE  :						int      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 8, '''y''': 5}      )


	def 				lowercase__  (self : Any      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''test_list = [x, add_two(x)]'''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					self.assertListEqual(__UpperCAmelCase,					[3, 5]      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )


	def 				lowercase__  (self : int      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= '''y = x'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 3}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_list = [x, add_two(x)]\ntest_list[1]'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}      )






	def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 0\nfor i in range(3):\n    x = i'''
					SCREAMING_SNAKE_CASE  :						List[Any]      					= {}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{'''range''': range},					state=__UpperCAmelCase      )
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 2, '''i''': 2}      )




