





'''simple docstring'''






import copy

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


lowerCAmelCase       		=      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(_A       )
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   'rag'
	snake_case_      =   True



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

			from ..auto.configuration_auto import AutoConfig

			lowerCAmelCase     :    Tuple						=   AutoConfig.for_model(a_		,      **a_	)
			lowerCAmelCase     :    Dict						=   AutoConfig.for_model(a_		,      **a_	)

			lowerCAmelCase     :    str						=   reduce_loss
			lowerCAmelCase     :    str						=   label_smoothing
			lowerCAmelCase     :    Optional[int]						=   exclude_bos_score
			lowerCAmelCase     :    str						=   do_marginalize

			lowerCAmelCase     :    int						=   title_sep
			lowerCAmelCase     :    Optional[int]						=   doc_sep
			lowerCAmelCase     :    Any						=   n_docs
			lowerCAmelCase     :    int						=   max_combined_length

			lowerCAmelCase     :    Union[str, Any]						=   dataset
			lowerCAmelCase     :    Optional[Any]						=   dataset_split
			lowerCAmelCase     :    List[str]						=   index_name

			lowerCAmelCase     :    Tuple						=   retrieval_vector_size
			lowerCAmelCase     :    Tuple						=   retrieval_batch_size
			lowerCAmelCase     :    Any						=   passages_path
			lowerCAmelCase     :    Union[str, Any]						=   index_path
			lowerCAmelCase     :    int						=   use_dummy_dataset

			lowerCAmelCase     :    Union[str, Any]						=   output_retrieved

			lowerCAmelCase     :    Optional[Any]						=   do_deduplication

			lowerCAmelCase     :    Tuple						=   use_cache

			if self.forced_eos_token_id is None:
					lowerCAmelCase     :    Union[str, Any]						=   getattr(self.generator		,      "forced_eos_token_id"		,      a_	)



	@classmethod
	def   _lowerCamelCase    (   cls		,      a_		,      a_		,      **a_	):
			return cls(question_encoder=question_encoder_config.to_dict()		,      generator=generator_config.to_dict()		,      **a_	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   copy.deepcopy(self.__dict__	)
			lowerCAmelCase     :    List[str]						=   self.question_encoder.to_dict()
			lowerCAmelCase     :    str						=   self.generator.to_dict()
			lowerCAmelCase     :    Any						=   self.__class__.model_type
			return output









'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. 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 torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
	    "text to read (in English) and returns a waveform object containing the sound."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   self.pre_processor(text=a_		,      return_tensors="pt"		,      truncation=a_	)

			if speaker_embeddings is None:
					if not is_datasets_available():
							raise ImportError("Datasets needs to be installed if not passing speaker embeddings."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

			return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



