



"""simple docstring"""






import comet  # From: unbabel-comet
import torch

import datasets


_lowercase			:			Union[str, Any]										=						datasets.logging.get_logger(__name__)

_lowercase			:			List[str]										=						"\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

_lowercase			:			int										=						"\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

_lowercase			:			Any										=						"\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,       _KWARGS_DESCRIPTION       )
class 	__SCREAMING_SNAKE_CASE     (				datasets.Metric       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  List[str]					)-> Optional[int]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION,  citation=_CITATION,  homepage='''https://unbabel.github.io/COMET/html/index.html''',  inputs_description=_KWARGS_DESCRIPTION,  features=datasets.Features(
								        {
								            '''sources''': datasets.Value('''string''',  id='''sequence'''					),
								            '''predictions''': datasets.Value('''string''',  id='''sequence'''					),
								            '''references''': datasets.Value('''string''',  id='''sequence'''					),
								        }					),  codebase_urls=['''https://github.com/Unbabel/COMET'''],  reference_urls=[
								        '''https://github.com/Unbabel/COMET''',
								        '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
								        '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
								    ],  )



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  List[Any]					)-> Any:




								if self.config_name == "default":
											lowerCamelCase__		:		Dict		     =comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''					)					)
								else:
											lowerCamelCase__		:		Dict		     =comet.load_from_checkpoint(comet.download_model(self.config_name					)					)




					def 		snake_case	(	self							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Dict,  lowerCamelCase							:  str=None,  lowerCamelCase							:  Union[str, Any]=False					)-> str:




								if gpus is None:
											lowerCamelCase__		:		Optional[int]		     =1 if torch.cuda.is_available() else 0
								lowerCamelCase__		:		Tuple		     ={"""src""": sources, """mt""": predictions, """ref""": references}
								lowerCamelCase__		:		Tuple		     =[dict(zip(_SCREAMING_SNAKE_CASE,  _SCREAMING_SNAKE_CASE					)					) for t in zip(*data.values()					)]
								lowerCamelCase__		:		Optional[Any]		     =self.scorer.predict(_SCREAMING_SNAKE_CASE,  gpus=_SCREAMING_SNAKE_CASE,  progress_bar=_SCREAMING_SNAKE_CASE					)
								return {"mean_score": mean_score, "scores": scores}









"""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




