



"""simple docstring"""






import math
from typing import Optional

import numpy as np

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


_lowercase			:			Dict										=						logging.get_logger(__name__)

_lowercase			:			Tuple										=						{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'encodec'



					def __init__(	self							:  str,  lowerCamelCase							:  Optional[Any]=[1.5, 3.0, 6.0, 12.0, 24.0],  lowerCamelCase							:  Tuple=2_4000,  lowerCamelCase							:  List[str]=1,  lowerCamelCase							:  Tuple=False,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Tuple=128,  lowerCamelCase							:  Any=32,  lowerCamelCase							:  Tuple=1,  lowerCamelCase							:  Optional[int]=[8, 5, 4, 2],  lowerCamelCase							:  Any="weight_norm",  lowerCamelCase							:  Union[str, Any]=7,  lowerCamelCase							:  Tuple=7,  lowerCamelCase							:  Optional[Any]=3,  lowerCamelCase							:  int=2,  lowerCamelCase							:  Optional[Any]=True,  lowerCamelCase							:  int="reflect",  lowerCamelCase							:  Any=2,  lowerCamelCase							:  List[Any]=2,  lowerCamelCase							:  int=1.0,  lowerCamelCase							:  Optional[int]=1024,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Tuple=True,  **lowerCamelCase							:  Dict,  )-> int:
								lowerCamelCase__		:		int		     =target_bandwidths
								lowerCamelCase__		:		List[Any]		     =sampling_rate
								lowerCamelCase__		:		Union[str, Any]		     =audio_channels
								lowerCamelCase__		:		List[str]		     =normalize
								lowerCamelCase__		:		Tuple		     =chunk_length_s
								lowerCamelCase__		:		int		     =overlap
								lowerCamelCase__		:		int		     =hidden_size
								lowerCamelCase__		:		List[Any]		     =num_filters
								lowerCamelCase__		:		Any		     =num_residual_layers
								lowerCamelCase__		:		int		     =upsampling_ratios
								lowerCamelCase__		:		str		     =norm_type
								lowerCamelCase__		:		int		     =kernel_size
								lowerCamelCase__		:		Union[str, Any]		     =last_kernel_size
								lowerCamelCase__		:		List[Any]		     =residual_kernel_size
								lowerCamelCase__		:		Dict		     =dilation_growth_rate
								lowerCamelCase__		:		List[Any]		     =use_causal_conv
								lowerCamelCase__		:		List[str]		     =pad_mode
								lowerCamelCase__		:		List[str]		     =compress
								lowerCamelCase__		:		List[str]		     =num_lstm_layers
								lowerCamelCase__		:		Any		     =trim_right_ratio
								lowerCamelCase__		:		List[Any]		     =codebook_size
								lowerCamelCase__		:		Optional[int]		     =codebook_dim if codebook_dim is not None else hidden_size
								lowerCamelCase__		:		Tuple		     =use_conv_shortcut

								if self.norm_type not in ["weight_norm", "time_group_norm"]:
											raise ValueError(
											    F'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''					)

								super().__init__(**lowerCamelCase					)



					@property
					def 		snake_case	(	self							:  str					)-> Optional[int]:
								if self.chunk_length_s is None:
											return None
								else:
											return int(self.chunk_length_s * self.sampling_rate					)



					@property
					def 		snake_case	(	self							:  str					)-> Optional[int]:
								if self.chunk_length_s is None or self.overlap is None:
											return None
								else:
											return max(1,  int((1.0 - self.overlap) * self.chunk_length					)					)



					@property
					def 		snake_case	(	self							:  Any					)-> int:
								lowerCamelCase__		:		int		     =np.prod(self.upsampling_ratios					)
								return math.ceil(self.sampling_rate / hop_length					)




					@property
					def 		snake_case	(	self							:  List[str]					)-> int:
								return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)









"""simple docstring"""






import copy

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


_lowercase			:			Tuple										=						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(lowerCAmelCase_       )
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'rag'
					_a             =							True



					def __init__(	self							:  Optional[int],  lowerCamelCase							:  Union[str, Any]=None,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Dict=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=" / ",  lowerCamelCase							:  Union[str, Any]=" // ",  lowerCamelCase							:  List[Any]=5,  lowerCamelCase							:  int=300,  lowerCamelCase							:  Optional[Any]=768,  lowerCamelCase							:  Optional[Any]=8,  lowerCamelCase							:  Tuple="wiki_dpr",  lowerCamelCase							:  Tuple="train",  lowerCamelCase							:  Union[str, Any]="compressed",  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Tuple=False,  lowerCamelCase							:  List[str]=False,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  Optional[Any]=None,  **lowerCamelCase							:  str,  )-> List[Any]:
								super().__init__(
								    bos_token_id=lowerCamelCase,  pad_token_id=lowerCamelCase,  eos_token_id=lowerCamelCase,  decoder_start_token_id=lowerCamelCase,  forced_eos_token_id=lowerCamelCase,  is_encoder_decoder=lowerCamelCase,  prefix=lowerCamelCase,  vocab_size=lowerCamelCase,  **lowerCamelCase,  )
								assert (
								    "question_encoder" in kwargs and "generator" in kwargs
								), "Config has to be initialized with question_encoder and generator config"
								lowerCamelCase__		:		Tuple		     =kwargs.pop('''question_encoder'''					)
								lowerCamelCase__		:		int		     =question_encoder_config.pop('''model_type'''					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''generator'''					)
								lowerCamelCase__		:		Tuple		     =decoder_config.pop('''model_type'''					)

								from ..auto.configuration_auto import AutoConfig

								lowerCamelCase__		:		Tuple		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)

								lowerCamelCase__		:		Any		     =reduce_loss
								lowerCamelCase__		:		Tuple		     =label_smoothing
								lowerCamelCase__		:		List[str]		     =exclude_bos_score
								lowerCamelCase__		:		Dict		     =do_marginalize

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

								lowerCamelCase__		:		List[Any]		     =dataset
								lowerCamelCase__		:		int		     =dataset_split
								lowerCamelCase__		:		List[Any]		     =index_name

								lowerCamelCase__		:		int		     =retrieval_vector_size
								lowerCamelCase__		:		Dict		     =retrieval_batch_size
								lowerCamelCase__		:		str		     =passages_path
								lowerCamelCase__		:		Any		     =index_path
								lowerCamelCase__		:		List[Any]		     =use_dummy_dataset

								lowerCamelCase__		:		Optional[int]		     =output_retrieved

								lowerCamelCase__		:		List[str]		     =do_deduplication

								lowerCamelCase__		:		Tuple		     =use_cache

								if self.forced_eos_token_id is None:
											lowerCamelCase__		:		int		     =getattr(self.generator,  '''forced_eos_token_id''',  lowerCamelCase					)



					@classmethod
					def 		snake_case	(	cls							:  List[str],  lowerCamelCase							:  PretrainedConfig,  lowerCamelCase							:  PretrainedConfig,  **lowerCamelCase							:  int					)-> PretrainedConfig:
								return cls(question_encoder=question_encoder_config.to_dict(),  generator=generator_config.to_dict(),  **lowerCamelCase					)




					def 		snake_case	(	self							:  Optional[int]					)-> int:
								lowerCamelCase__		:		Union[str, Any]		     =copy.deepcopy(self.__dict__					)
								lowerCamelCase__		:		Optional[int]		     =self.question_encoder.to_dict()
								lowerCamelCase__		:		Tuple		     =self.generator.to_dict()
								lowerCamelCase__		:		Optional[Any]		     =self.__class__.model_type
								return output




