

'''simple docstring'''

import copy

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


SCREAMING_SNAKE_CASE							:				Optional[int]     	=			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    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """rag"""
				_a    	=     True



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

											from ..auto.configuration_auto import AutoConfig

											SCREAMING_SNAKE_CASE_				=				AutoConfig.for_model(_lowercase,     **_lowercase       )
											SCREAMING_SNAKE_CASE_				=				AutoConfig.for_model(_lowercase,     **_lowercase       )

											SCREAMING_SNAKE_CASE_				=				reduce_loss
											SCREAMING_SNAKE_CASE_				=				label_smoothing
											SCREAMING_SNAKE_CASE_				=				exclude_bos_score
											SCREAMING_SNAKE_CASE_				=				do_marginalize

											SCREAMING_SNAKE_CASE_				=				title_sep
											SCREAMING_SNAKE_CASE_				=				doc_sep
											SCREAMING_SNAKE_CASE_				=				n_docs
											SCREAMING_SNAKE_CASE_				=				max_combined_length

											SCREAMING_SNAKE_CASE_				=				dataset
											SCREAMING_SNAKE_CASE_				=				dataset_split
											SCREAMING_SNAKE_CASE_				=				index_name

											SCREAMING_SNAKE_CASE_				=				retrieval_vector_size
											SCREAMING_SNAKE_CASE_				=				retrieval_batch_size
											SCREAMING_SNAKE_CASE_				=				passages_path
											SCREAMING_SNAKE_CASE_				=				index_path
											SCREAMING_SNAKE_CASE_				=				use_dummy_dataset

											SCREAMING_SNAKE_CASE_				=				output_retrieved

											SCREAMING_SNAKE_CASE_				=				do_deduplication

											SCREAMING_SNAKE_CASE_				=				use_cache

											if self.forced_eos_token_id is None:
																		SCREAMING_SNAKE_CASE_				=				getattr(self.generator,     'forced_eos_token_id',     _lowercase       )



				@classmethod
				def   a__					(    cls,     _lowercase,     _lowercase,     **_lowercase       )     ->							PretrainedConfig:
											return cls(question_encoder=question_encoder_config.to_dict(),     generator=generator_config.to_dict(),     **_lowercase       )





				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				copy.deepcopy(self.__dict__       )
											SCREAMING_SNAKE_CASE_				=				self.question_encoder.to_dict()
											SCREAMING_SNAKE_CASE_				=				self.generator.to_dict()
											SCREAMING_SNAKE_CASE_				=				self.__class__.model_type
											return output








'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class    snake_case     :






				"""simple docstring"""
				# setable values
				_a    	=     None
				_a    	=     None
				_a    	=     None  # sigma(t_i)



				@classmethod
				def   a__					(    cls       )     ->							Dict:
											return cls()






@dataclass
class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     42
				_a    	=     42
				_a    	=     42






class    snake_case     (      lowercase_,						lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							int:
											return True



				@register_to_config
				def __init__(    self,     _lowercase = 0.02,     _lowercase = 100,     _lowercase = 1.007,     _lowercase = 80,     _lowercase = 0.05,     _lowercase = 50,     )     ->							List[Any]:
											pass



				def   a__					(    self       )     ->							List[Any]:
											return KarrasVeSchedulerState.create()



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = ()       )     ->							KarrasVeSchedulerState:
											SCREAMING_SNAKE_CASE_				=				jnp.arange(0,     _lowercase       )[::-1].copy()
											SCREAMING_SNAKE_CASE_				=				[
											    (
											        self.config.sigma_max**2
											        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
											    )
											    for i in timesteps
											]

											return state.replace(
											    num_inference_steps=_lowercase,     schedule=jnp.array(_lowercase,     dtype=jnp.floataa       ),     timesteps=_lowercase,     )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							Tuple[jnp.ndarray, float]:
											if self.config.s_min <= sigma <= self.config.s_max:
																		SCREAMING_SNAKE_CASE_				=				min(self.config.s_churn / state.num_inference_steps,     2**0.5 - 1       )
											else:
																		SCREAMING_SNAKE_CASE_				=				0

											# sample eps ~ N(0, S_noise^2 * I)
											SCREAMING_SNAKE_CASE_				=				random.split(_lowercase,     num=1       )
											SCREAMING_SNAKE_CASE_				=				self.config.s_noise * random.normal(key=_lowercase,     shape=sample.shape       )
											SCREAMING_SNAKE_CASE_				=				sigma + gamma * sigma
											SCREAMING_SNAKE_CASE_				=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

											return sample_hat, sigma_hat



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_hat + sigma_hat * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_hat - pred_original_sample) / sigma_hat
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * derivative

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_prev + sigma_prev * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_prev - pred_original_sample) / sigma_prev
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											raise NotImplementedError()





