



'''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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 	a__     (				_lowercase	):
	__magic_name__		:    Dict	    =     "wav2vec2"


	def __init__(self : Tuple,					__UpperCAmelCase : Tuple=32,					__UpperCAmelCase : int=768,					__UpperCAmelCase : Optional[Any]=12,					__UpperCAmelCase : Any=12,					__UpperCAmelCase : Any=3072,					__UpperCAmelCase : Optional[int]="gelu",					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : List[str]=0.1,					__UpperCAmelCase : List[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.0,					__UpperCAmelCase : str=0.0,					__UpperCAmelCase : Optional[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.02,					__UpperCAmelCase : Dict=1e-5,					__UpperCAmelCase : List[str]="group",					__UpperCAmelCase : str="gelu",					__UpperCAmelCase : str=(512, 512, 512, 512, 512, 512, 512),					__UpperCAmelCase : str=(5, 2, 2, 2, 2, 2, 2),					__UpperCAmelCase : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2),					__UpperCAmelCase : Any=False,					__UpperCAmelCase : Optional[Any]=128,					__UpperCAmelCase : Tuple=16,					__UpperCAmelCase : str=False,					__UpperCAmelCase : int=True,					__UpperCAmelCase : Tuple=0.05,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Union[str, Any]=2,					__UpperCAmelCase : Optional[Any]=0.0,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Dict=0,					__UpperCAmelCase : Any=320,					__UpperCAmelCase : int=2,					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : Tuple=100,					__UpperCAmelCase : Any=256,					__UpperCAmelCase : Tuple=256,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Any="sum",					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : str=False,					__UpperCAmelCase : str=256,					__UpperCAmelCase : Union[str, Any]=(512, 512, 512, 512, 1500),					__UpperCAmelCase : Optional[int]=(5, 3, 3, 1, 1),					__UpperCAmelCase : Dict=(1, 2, 3, 1, 1),					__UpperCAmelCase : List[str]=512,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : List[Any]=1,					__UpperCAmelCase : List[str]=2,					__UpperCAmelCase : List[Any]=False,					__UpperCAmelCase : str=3,					__UpperCAmelCase : Tuple=2,					__UpperCAmelCase : str=3,					__UpperCAmelCase : int=None,					__UpperCAmelCase : List[Any]=None,					**__UpperCAmelCase : int,					)     ->			Tuple:

					"""simple docstring"""

					super().__init__(**__UpperCAmelCase,					pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= feat_extract_norm
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= feat_extract_activation
					SCREAMING_SNAKE_CASE  :						Dict      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= conv_bias
					SCREAMING_SNAKE_CASE  :						Dict      					= num_conv_pos_embeddings
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_conv_pos_embedding_groups
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= len(self.conv_dim      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						str      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						str      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						int      					= hidden_dropout
					SCREAMING_SNAKE_CASE  :						Any      					= attention_dropout
					SCREAMING_SNAKE_CASE  :						int      					= activation_dropout
					SCREAMING_SNAKE_CASE  :						int      					= feat_proj_dropout
					SCREAMING_SNAKE_CASE  :						List[str]      					= final_dropout
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= layerdrop
					SCREAMING_SNAKE_CASE  :						Tuple      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						Dict      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= do_stable_layer_norm
					SCREAMING_SNAKE_CASE  :						Dict      					= use_weighted_layer_sum

					if (
					    (len(self.conv_stride      ) != self.num_feat_extract_layers)
					    or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
					    or (len(self.conv_dim      ) != self.num_feat_extract_layers)
					):
									raise ValueError(
									    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
									    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
									    F''' {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,'''
									    F''' `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'''      )

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					SCREAMING_SNAKE_CASE  :						str      					= apply_spec_augment
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_time_prob
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= mask_time_length
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= mask_time_min_masks
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_feature_prob
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_length
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_min_masks

					# parameters for pretraining with codevector quantized representations
					SCREAMING_SNAKE_CASE  :						Any      					= num_codevectors_per_group
					SCREAMING_SNAKE_CASE  :						int      					= num_codevector_groups
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= contrastive_logits_temperature
					SCREAMING_SNAKE_CASE  :						List[Any]      					= feat_quantizer_dropout
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_negatives
					SCREAMING_SNAKE_CASE  :						Any      					= codevector_dim
					SCREAMING_SNAKE_CASE  :						str      					= proj_codevector_dim
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= diversity_loss_weight

					# ctc loss
					SCREAMING_SNAKE_CASE  :						Tuple      					= ctc_loss_reduction
					SCREAMING_SNAKE_CASE  :						Any      					= ctc_zero_infinity

					# adapter
					SCREAMING_SNAKE_CASE  :						int      					= add_adapter
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_kernel_size
					SCREAMING_SNAKE_CASE  :						int      					= adapter_stride
					SCREAMING_SNAKE_CASE  :						List[str]      					= num_adapter_layers
					SCREAMING_SNAKE_CASE  :						Tuple      					= output_hidden_size or hidden_size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_attn_dim

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						Dict      					= classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= xvector_output_dim






	@property
	def 				lowercase__  (self : Tuple      )     ->			Optional[Any]:

					"""simple docstring"""

					return functools.reduce(operator.mul,					self.conv_stride,					1      )




