from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("""3.8"""):
	import importlib_metadata
else:
	import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
lowerCamelCase_				:		List[str]       						=       """\
"""

lowerCamelCase_				:		Any       						=       """\
    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.
    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.
"""

lowerCamelCase_				:		Tuple       						=       """
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.
        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',
        'tokenize', 'use_effective_order'.
    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.
        Keywords include 'concatenate_texts'.
Returns: depending on the XTREME-S task, one or several of:
    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'
    \"f1\": F1 score - for 'minds14'
    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'
Examples:

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'
    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]
    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'wer': 0.56, 'cer': 0.27}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')
    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]
    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'bleu': 31.65}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'accuracy': 0.6}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'f1': 0.58, 'accuracy': 0.6}
"""

lowerCamelCase_				:		Dict       						=       ["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
lowerCamelCase_				:		Optional[Any]       						=       """"""

try:
	from jiwer import transforms as tr

	lowerCamelCase_				:		str       						=       True
except ImportError:
	lowerCamelCase_				:		List[Any]       						=       False

if _jiwer_available and version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):


	class 						a__							(   tr.AbstractTransform		):



				def __init__(						self      ,						UpperCAmelCase = " "    ) ->    List[str]:
									__a							=   sentence_delimiter



				def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    List[str]:
									return list(UpperCAmelCase    )







				def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
									__a							=   []
									for sent_idx, sentence in enumerate(UpperCAmelCase    ):
														chars.extend(self.process_string(UpperCAmelCase    )    )
														if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase    ) - 1:
																			chars.append(self.sentence_delimiter    )
									return chars






	lowerCamelCase_				:		Any       						=       tr.Compose(
	    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
	)
elif _jiwer_available:
	lowerCamelCase_				:		Optional[Any]       						=       tr.Compose(
	    [
	        tr.RemoveMultipleSpaces(),
	        tr.Strip(),
	        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
	        tr.ReduceToListOfListOfChars(),
	    ]
	)
else:
	lowerCamelCase_				:		Tuple       						=       None
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					return float((preds == labels).mean()						)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					return {
					    "f1": float(fa_score(y_true=__lowerCamelCase      ,				y_pred=__lowerCamelCase      ,				average='macro'						)						),
					    "accuracy": simple_accuracy(__lowerCamelCase      ,				__lowerCamelCase						),
					}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase="exp"      ,				__lowerCamelCase=None      ,				__lowerCamelCase=False      ,				__lowerCamelCase=False      ,				__lowerCamelCase=None      ,				__lowerCamelCase=False      ,				):
					# xtreme-s can only have one label
					__a							=   [[label] for label in labels]
					__a							=   list(__lowerCamelCase						)
					try:
										import sacrebleu as scb
					except ImportError:
										raise ValueError(
										    'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
										    'You can install it via `pip install sacrebleu`.'						)

					if version.parse(scb.__version__						) < version.parse('1.4.12'						):
										raise ImportWarning(
										    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
										    'You can install it with `pip install "sacrebleu>=1.4.12"`.'						)

					__a							=   len(labels[0]						)
					if any(len(__lowerCamelCase						) != references_per_prediction for refs in labels						):
										raise ValueError('Sacrebleu requires the same number of references for each prediction'						)
					__a							=   [[refs[i] for refs in labels] for i in range(__lowerCamelCase						)]
					__a							=   scb.corpus_bleu(
					    __lowerCamelCase      ,				__lowerCamelCase      ,				smooth_method=__lowerCamelCase      ,				smooth_value=__lowerCamelCase      ,				force=__lowerCamelCase      ,				lowercase=__lowerCamelCase      ,				use_effective_order=__lowerCamelCase      ,				**({'tokenize': tokenize} if tokenize else {})      ,				)
					return {"bleu": output.score}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					try:
										from jiwer import compute_measures
					except ImportError:
										raise ValueError(
										    f'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
										    'You can install it via `pip install jiwer`.'						)

					if concatenate_texts:
										__a							=   compute_measures(__lowerCamelCase      ,				__lowerCamelCase						)['wer']

										__a							=   compute_measures(__lowerCamelCase      ,				__lowerCamelCase      ,				truth_transform=__lowerCamelCase      ,				hypothesis_transform=__lowerCamelCase						)['wer']
										return {"wer": wer, "cer": cer}
					else:

										def compute_score(__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase="wer"						):
															__a							=   0
															__a							=   0
															for prediction, reference in zip(__lowerCamelCase      ,				__lowerCamelCase						):
																				if score_type == "wer":
																									__a							=   compute_measures(__lowerCamelCase      ,				__lowerCamelCase						)
																				elif score_type == "cer":
																									__a							=   compute_measures(
																									    __lowerCamelCase      ,				__lowerCamelCase      ,				truth_transform=__lowerCamelCase      ,				hypothesis_transform=__lowerCamelCase						)
																				incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																				total += measures["substitutions"] + measures["deletions"] + measures["hits"]
															return incorrect / total

										return {"wer": compute_score(__lowerCamelCase      ,				__lowerCamelCase      ,				'wer'						), "cer": compute_score(__lowerCamelCase      ,				__lowerCamelCase      ,				'cer'						)}



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



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								if self.config_name not in _CONFIG_NAMES:
													raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''    )

								__a							=   'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

								return datasets.MetricInfo(
								    description=_DESCRIPTION      ,						citation=_CITATION      ,						inputs_description=_KWARGS_DESCRIPTION      ,						features=datasets.Features(
								        {'predictions': datasets.Value(UpperCAmelCase    ), 'references': datasets.Value(UpperCAmelCase    )}    )      ,						codebase_urls=[]      ,						reference_urls=[]      ,						format='numpy'      ,						)







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None    ) ->    Optional[Any]:
								__a							=   bleu_kwargs if bleu_kwargs is not None else {}
								__a							=   wer_kwargs if wer_kwargs is not None else {}

								if self.config_name == "fleurs-lang_id":
													return {"accuracy": simple_accuracy(UpperCAmelCase      ,						UpperCAmelCase    )}
								elif self.config_name == "minds14":
													return fa_and_simple_accuracy(UpperCAmelCase      ,						UpperCAmelCase    )
								elif self.config_name == "covost2":
													__a							=   bleu_kwargs.pop('smooth_method'      ,						'exp'    )
													__a							=   bleu_kwargs.pop('smooth_value'      ,						UpperCAmelCase    )
													__a							=   bleu_kwargs.pop('force'      ,						UpperCAmelCase    )
													__a							=   bleu_kwargs.pop('lowercase'      ,						UpperCAmelCase    )
													__a							=   bleu_kwargs.pop('tokenize'      ,						UpperCAmelCase    )
													__a							=   bleu_kwargs.pop('use_effective_order'      ,						UpperCAmelCase    )
													return bleu(
													    preds=UpperCAmelCase      ,						labels=UpperCAmelCase      ,						smooth_method=UpperCAmelCase      ,						smooth_value=UpperCAmelCase      ,						force=UpperCAmelCase      ,						lowercase=UpperCAmelCase      ,						tokenize=UpperCAmelCase      ,						use_effective_order=UpperCAmelCase      ,						)
								elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
													__a							=   wer_kwargs.pop('concatenate_texts'      ,						UpperCAmelCase    )
													return wer_and_cer(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						self.config_name    )
								else:
													raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''    )





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


lowerCamelCase_				:		Union[str, Any]       						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_				:		List[Any]       						=       """cuda""" if torch.cuda.is_available() else """cpu"""
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=100      ,				__lowerCamelCase=" "						):
					__a							=   text.split(__lowerCamelCase						)
					return [character.join(text[i : i + n]						).strip() for i in range(0      ,				len(__lowerCamelCase						)      ,				__lowerCamelCase						)]
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a					,      __a							=   [], []
					for title, text in zip(documents['title']      ,				documents['text']						):
										if text is not None:
															for passage in split_text(__lowerCamelCase						):
																				titles.append(title if title is not None else ''						)
																				texts.append(__lowerCamelCase						)
					return {"title": titles, "text": texts}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   ctx_tokenizer(
					    documents['title']      ,				documents['text']      ,				truncation=__lowerCamelCase      ,				padding='longest'      ,				return_tensors='pt'						)['input_ids']
					__a							=   ctx_encoder(input_ids.to(device=__lowerCamelCase						)      ,				return_dict=__lowerCamelCase						).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				):
					######################################
					logger.info('Step 1 - Create the dataset'						)
					######################################

					# The dataset needed for RAG must have three columns:
					# - title (string): title of the document
					# - text (string): text of a passage of the document
					# - embeddings (array of dimension d): DPR representation of the passage

					# Let's say you have documents in tab-separated csv files with columns "title" and "text"
					assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

					# You can load a Dataset object this way
					__a							=   load_dataset(
					    'csv'      ,				data_files=[rag_example_args.csv_path]      ,				split='train'      ,				delimiter='\t'      ,				column_names=['title', 'text']						)

					# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

					# Then split the documents into passages of 100 words
					__a							=   dataset.map(__lowerCamelCase      ,				batched=__lowerCamelCase      ,				num_proc=processing_args.num_proc						)

					# And compute the embeddings
					__a							=   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__lowerCamelCase						)
					__a							=   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
					__a							=   Features(
					    {'text': Value('string'						), 'title': Value('string'						), 'embeddings': Sequence(Value('float32'						)						)}						)  # optional, save as float32 instead of float64 to save space
					__a							=   dataset.map(
					    partial(__lowerCamelCase      ,				ctx_encoder=__lowerCamelCase      ,				ctx_tokenizer=__lowerCamelCase						)      ,				batched=__lowerCamelCase      ,				batch_size=processing_args.batch_size      ,				features=__lowerCamelCase      ,				)

					# And finally save your dataset
					__a							=   os.path.join(rag_example_args.output_dir      ,				'my_knowledge_dataset'						)
					dataset.save_to_disk(__lowerCamelCase						)
					# from datasets import load_from_disk
					# dataset = load_from_disk(passages_path)  # to reload the dataset

					######################################
					logger.info('Step 2 - Index the dataset'						)
					######################################

					# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
					__a							=   faiss.IndexHNSWFlat(index_hnsw_args.d      ,				index_hnsw_args.m      ,				faiss.METRIC_INNER_PRODUCT						)
					dataset.add_faiss_index('embeddings'      ,				custom_index=__lowerCamelCase						)

					# And save the index
					__a							=   os.path.join(rag_example_args.output_dir      ,				'my_knowledge_dataset_hnsw_index.faiss'						)
					dataset.get_index('embeddings'						).save(__lowerCamelCase						)
					# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

					######################################
					logger.info('Step 3 - Load RAG'						)
					######################################

					# Easy way to load the model
					__a							=   RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name      ,				index_name='custom'      ,				indexed_dataset=__lowerCamelCase						)
					__a							=   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name      ,				retriever=__lowerCamelCase						)
					__a							=   RagTokenizer.from_pretrained(rag_example_args.rag_model_name						)

					# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
					# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

					######################################
					logger.info('Step 4 - Have fun'						)
					######################################

					__a							=   rag_example_args.question or 'What does Moses\' rod turn into ?'
					__a							=   tokenizer.question_encoder(__lowerCamelCase      ,				return_tensors='pt'						)['input_ids']
					__a							=   model.generate(__lowerCamelCase						)
					__a							=   tokenizer.batch_decode(__lowerCamelCase      ,				skip_special_tokens=__lowerCamelCase						)[0]
					logger.info('Q: ' + question						)
					logger.info('A: ' + generated_string						)


@dataclass
class 						a__							:
			A__				:   str								=			field(
			    default=str(Path(__snake_case		).parent / 'test_data' / 'my_knowledge_dataset.csv'		)      ,						metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}      ,						)
			A__				:   str								=			field(
			    default='facebook/rag-sequence-nq'      ,						metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}      ,						)
			A__				:   str								=			field(
			    default='facebook/dpr-ctx_encoder-multiset-base'      ,						metadata={
			        'help': (
			            'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
			            ' \'facebook/dpr-ctx_encoder-multiset-base\''
			        )
			    }      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}      ,						)






@dataclass
class 						a__							:
			A__				:   Optional[int]								=			field(
			    default=__snake_case      ,						metadata={
			        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
			    }      ,						)
			A__				:   int								=			field(
			    default=16      ,						metadata={
			        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
			    }      ,						)






@dataclass
class 						a__							:
			A__				:   int								=			field(
			    default=7_68      ,						metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}      ,						)
			A__				:   int								=			field(
			    default=1_28      ,						metadata={
			        'help': (
			            'The number of bi-directional links created for every new element during the HNSW index construction.'
			        )
			    }      ,						)


if __name__ == "__main__":
	logging.basicConfig(level=logging.WARNING)
	logger.setLevel(logging.INFO)

	lowerCamelCase_				:		Tuple       						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_				:		Union[str, Any]       						=       parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		lowerCamelCase_				:		Union[str, Any]       						=       rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)




