


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       :       str				   =			"\\n"

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

__lowerCamelCase       :       Tuple				   =			"\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.\n        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',\n        'tokenize', 'use_effective_order'.\n    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.\n        Keywords include 'concatenate_texts'.\nReturns: depending on the XTREME-S task, one or several of:\n    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'\n    \"f1\": F1 score - for 'minds14'\n    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'\n    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]\n    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'wer': 0.56, 'cer': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')\n    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]\n    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'bleu': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'accuracy': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'f1': 0.58, 'accuracy': 0.6}\n"

__lowerCamelCase       :       List[str]				   =			["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
__lowerCamelCase       :       List[Any]				   =			""

try:
						from jiwer import transforms as tr

						__lowerCamelCase       :       Tuple				   =			True
except ImportError:
						__lowerCamelCase       :       int				   =			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[Any]:
												'''simple docstring'''


												A__     :							Union[str, Any]   				=sentence_delimiter






								def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Optional[int]:
												'''simple docstring'''


												return list(__UpperCamelCase							)






								def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			int:
												'''simple docstring'''


												A__     :							str   				=[]
												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       :       Optional[int]				   =			tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
elif _jiwer_available:
						__lowerCamelCase       :       Dict				   =			tr.Compose(
						    [
						        tr.RemoveMultipleSpaces(),
						        tr.Strip(),
						        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
						        tr.ReduceToListOfListOfChars(),
						    ]
						)
else:
						__lowerCamelCase       :       Union[str, Any]				   =			None



def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							int:
				return float((preds == labels).mean()	)



def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Tuple:
				return {
				    "f1": float(fa_score(y_true=snake_case_,	y_pred=snake_case_,	average='''macro'''	)	),
				    "accuracy": simple_accuracy(snake_case_,	snake_case_	),
				}



def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_="exp",	snake_case_=None,	snake_case_=False,	snake_case_=False,	snake_case_=None,	snake_case_=False,	)			->							Optional[Any]:
				# xtreme-s can only have one label
				A__     :							str   				=[[label] for label in labels]
				A__     :							List[Any]   				=list(snake_case_	)
				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__     :							List[str]   				=len(labels[0]	)
				if any(len(snake_case_	) != references_per_prediction for refs in labels	):
								raise ValueError('''Sacrebleu requires the same number of references for each prediction'''	)
				A__     :							int   				=[[refs[i] for refs in labels] for i in range(snake_case_	)]
				A__     :							Any   				=scb.corpus_bleu(
				    snake_case_,	snake_case_,	smooth_method=snake_case_,	smooth_value=snake_case_,	force=snake_case_,	lowercase=snake_case_,	use_effective_order=snake_case_,	**({'''tokenize''': tokenize} if tokenize else {}),	)
				return {"bleu": output.score}



def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_	)			->							List[Any]:
				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__     :							str   				=compute_measures(snake_case_,	snake_case_	)['''wer''']

								A__     :							int   				=compute_measures(snake_case_,	snake_case_,	truth_transform=snake_case_,	hypothesis_transform=snake_case_	)['''wer''']
								return {"wer": wer, "cer": cer}
				else:

								def compute_score(snake_case_,	snake_case_,	snake_case_="wer"	):
												A__     :							Dict   				=0
												A__     :							Tuple   				=0
												for prediction, reference in zip(snake_case_,	snake_case_	):
																if score_type == "wer":
																				A__     :							Optional[int]   				=compute_measures(snake_case_,	snake_case_	)
																elif score_type == "cer":
																				A__     :							str   				=compute_measures(
																				    snake_case_,	snake_case_,	truth_transform=snake_case_,	hypothesis_transform=snake_case_	)
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total

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




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






		def       lowerCAmelCase_  (  self							)->			Tuple:
						'''simple docstring'''


						if self.config_name not in _CONFIG_NAMES:
										raise KeyError(F'You should supply a configuration name selected in {_CONFIG_NAMES}'							)

						A__     :							Any   				='''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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase=None	,     __UpperCamelCase=None							)->			List[str]:
						'''simple docstring'''


						A__     :							int   				=bleu_kwargs if bleu_kwargs is not None else {}
						A__     :							Optional[int]   				=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__     :							List[Any]   				=bleu_kwargs.pop('''smooth_method'''	,     '''exp'''							)
										A__     :							Union[str, Any]   				=bleu_kwargs.pop('''smooth_value'''	,     __UpperCamelCase							)
										A__     :							int   				=bleu_kwargs.pop('''force'''	,     __UpperCamelCase							)
										A__     :							List[str]   				=bleu_kwargs.pop('''lowercase'''	,     __UpperCamelCase							)
										A__     :							Union[str, Any]   				=bleu_kwargs.pop('''tokenize'''	,     __UpperCamelCase							)
										A__     :							int   				=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__     :							Union[str, Any]   				=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 argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


__lowerCamelCase       :       Optional[Any]				   =			collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							CheckpointState:
				print(f'Reading saved model from {model_file}'	)
				A__     :							Optional[int]   				=torch.load(snake_case_,	map_location=lambda snake_case_,	snake_case_	: default_restore_location(snake_case_,	'''cpu'''	)	)
				return CheckpointState(**snake_case_	)



class 			a      :






		def __init__(  self	,     __UpperCamelCase							)->			Union[str, Any]:
						'''simple docstring'''


						A__     :							Dict   				=src_file






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						raise NotImplementedError






		@staticmethod
		def       lowerCAmelCase_  (  __UpperCamelCase	,     *__UpperCamelCase	,     **__UpperCamelCase							)->			"DPRState":
						'''simple docstring'''


						if comp_type.startswith('''c'''							):
										return DPRContextEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
						if comp_type.startswith('''q'''							):
										return DPRQuestionEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
						if comp_type.startswith('''r'''							):
										return DPRReaderState(*__UpperCamelCase	,     **__UpperCamelCase							)
						else:
										raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''							)







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						A__     :							Optional[int]   				=DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
						print(F'Loading DPR biencoder from {self.src_file}'							)
						A__     :							Optional[int]   				=load_states_from_checkpoint(self.src_file							)
						A__				,  A__     :							List[str]   				=model.ctx_encoder, '''ctx_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						A__     :							Optional[int]   				={'''bert_model.embeddings.position_ids''': model.ctx_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
										if key.startswith(__UpperCamelCase							):
														A__     :							int   				=key[len(__UpperCamelCase							) :]
														if not key.startswith('''encode_proj.'''							):
																		A__     :							Any   				='''bert_model.''' + key
														A__     :							List[str]   				=value
						encoder.load_state_dict(__UpperCamelCase							)
						return model







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			Dict:
						'''simple docstring'''


						A__     :							Union[str, Any]   				=DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
						print(F'Loading DPR biencoder from {self.src_file}'							)
						A__     :							List[str]   				=load_states_from_checkpoint(self.src_file							)
						A__				,  A__     :							Optional[int]   				=model.question_encoder, '''question_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						A__     :							Optional[Any]   				={'''bert_model.embeddings.position_ids''': model.question_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
										if key.startswith(__UpperCamelCase							):
														A__     :							List[Any]   				=key[len(__UpperCamelCase							) :]
														if not key.startswith('''encode_proj.'''							):
																		A__     :							Tuple   				='''bert_model.''' + key
														A__     :							str   				=value
						encoder.load_state_dict(__UpperCamelCase							)
						return model







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			int:
						'''simple docstring'''


						A__     :							Optional[int]   				=DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
						print(F'Loading DPR reader from {self.src_file}'							)
						A__     :							Dict   				=load_states_from_checkpoint(self.src_file							)
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						A__     :							Optional[Any]   				={
						    '''encoder.bert_model.embeddings.position_ids''': model.span_predictor.encoder.bert_model.embeddings.position_ids
						}
						for key, value in saved_state.model_dict.items():
										if key.startswith('''encoder.'''							) and not key.startswith('''encoder.encode_proj'''							):
														A__     :							List[Any]   				='''encoder.bert_model.''' + key[len('''encoder.'''							) :]
										A__     :							Optional[int]   				=value
						model.span_predictor.load_state_dict(__UpperCamelCase							)
						return model







def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Optional[int]:
				A__     :							Optional[int]   				=Path(snake_case_	)
				dest_dir.mkdir(exist_ok=snake_case_	)

				A__     :							Tuple   				=DPRState.from_type(snake_case_,	src_file=snake_case_	)
				A__     :							str   				=dpr_state.load_dpr_model()
				model.save_pretrained(snake_case_	)
				model.from_pretrained(snake_case_	)  # sanity check


if __name__ == "__main__":
						__lowerCamelCase       :       Optional[Any]				   =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						)
						parser.add_argument(
						    "--src",
						    type=str,
						    help=(
						        "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"
						        " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"
						        " 'retriever' checkpoints."
						    ),
						)
						parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.")
						__lowerCamelCase       :       Tuple				   =			parser.parse_args()

						__lowerCamelCase       :       Optional[Any]				   =			Path(args.src)
						__lowerCamelCase       :       Optional[int]				   =			F"converted-{src_file.name}" if args.dest is None else args.dest
						__lowerCamelCase       :       Optional[Any]				   =			Path(dest_dir)
						assert src_file.exists()
						assert (
						    args.type is not None
						), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						convert(args.type, src_file, dest_dir)



