


import copy

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


SCREAMING_SNAKE_CASE :   Optional[Any]							      =				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(UpperCamelCase__     )
class _lowerCamelCase(		UpperCamelCase__     ):
							lowercase_				:			Tuple									=    """rag"""
							lowercase_				:			List[str]									=    True
							def __init__(      self,   lowerCamelCase=None,   lowerCamelCase=True,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=" / ",   lowerCamelCase=" // ",   lowerCamelCase=5,   lowerCamelCase=3_00,   lowerCamelCase=7_68,   lowerCamelCase=8,   lowerCamelCase="wiki_dpr",   lowerCamelCase="train",   lowerCamelCase="compressed",   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=False,   lowerCamelCase=False,   lowerCamelCase=0.0,   lowerCamelCase=True,   lowerCamelCase=False,   lowerCamelCase=False,   lowerCamelCase=False,   lowerCamelCase=True,   lowerCamelCase=None,   **lowerCamelCase,   ) ->     Dict:




											"""simple docstring"""

											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"
											_lowercase :							List[Any]   	=	kwargs.pop('question_encoder')
											_lowercase :							List[str]   	=	question_encoder_config.pop('model_type')
											_lowercase :							Union[str, Any]   	=	kwargs.pop('generator')
											_lowercase :							Any   	=	decoder_config.pop('model_type')

											from ..auto.configuration_auto import AutoConfig

											_lowercase :							List[str]   	=	AutoConfig.for_model(__A,   **__A)
											_lowercase :							Optional[Any]   	=	AutoConfig.for_model(__A,   **__A)

											_lowercase :							Any   	=	reduce_loss
											_lowercase :							List[Any]   	=	label_smoothing
											_lowercase :							Optional[Any]   	=	exclude_bos_score
											_lowercase :							Union[str, Any]   	=	do_marginalize

											_lowercase :							str   	=	title_sep
											_lowercase :							Optional[int]   	=	doc_sep
											_lowercase :							Any   	=	n_docs
											_lowercase :							Optional[int]   	=	max_combined_length

											_lowercase :							Tuple   	=	dataset
											_lowercase :							Tuple   	=	dataset_split
											_lowercase :							str   	=	index_name

											_lowercase :							int   	=	retrieval_vector_size
											_lowercase :							List[str]   	=	retrieval_batch_size
											_lowercase :							Dict   	=	passages_path
											_lowercase :							Tuple   	=	index_path
											_lowercase :							int   	=	use_dummy_dataset

											_lowercase :							Optional[Any]   	=	output_retrieved

											_lowercase :							Optional[Any]   	=	do_deduplication

											_lowercase :							Dict   	=	use_cache

											if self.forced_eos_token_id is None:
															_lowercase :							Tuple   	=	getattr(self.generator,   'forced_eos_token_id',   __A)
							@classmethod
							def     UpperCamelCase  (      cls,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase) ->     PretrainedConfig:




											"""simple docstring"""

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




							def     UpperCamelCase  (      self) ->     int:




											"""simple docstring"""

											_lowercase :							List[str]   	=	copy.deepcopy(self.__dict__)
											_lowercase :							Union[str, Any]   	=	self.question_encoder.to_dict()
											_lowercase :							Optional[int]   	=	self.generator.to_dict()
											_lowercase :							Optional[int]   	=	self.__class__.model_type
											return output






from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE :   Tuple							      =				"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

SCREAMING_SNAKE_CASE :   Any							      =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION     )
class _lowerCamelCase(		datasets.Metric     ):
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           return datasets.MetricInfo(
               description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
                   {
                       'predictions': datasets.Value('string',   id='sequence'),
                       'references': datasets.Value('string',   id='sequence'),
                   }),   codebase_urls=['https://github.com/jitsi/jiwer/'],   reference_urls=[
                   'https://en.wikipedia.org/wiki/Word_error_rate',
               ],   )




       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=False) ->     Optional[Any]:




           """simple docstring"""

           if concatenate_texts:
               return compute_measures(lowerCamelCase,   lowerCamelCase)["wer"]
           else:
               _lowercase :							Dict   	=	0
               _lowercase :							Optional[int]   	=	0
               for prediction, reference in zip(lowerCamelCase,   lowerCamelCase):
                   _lowercase :							List[Any]   	=	compute_measures(lowerCamelCase,   lowerCamelCase)
                   incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                   total += measures["substitutions"] + measures["deletions"] + measures["hits"]
               return incorrect / total


