import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


UpperCamelCase			 =	"""block_records.npy"""


UpperCamelCase			 =	logging.get_logger(__name__)



def     lowerCamelCase_    (  _lowercase					,	_lowercase				)							->       int:
		import tensorflow.compat.va as tf

		__A							:    Dict         =      tf.data.TFRecordDataset(__lowerCamelCase					,	buffer_size=512 * 1_024 * 1_024				)
		__A							:    Tuple         =      blocks_dataset.batch(__lowerCamelCase					,	drop_remainder=__lowerCamelCase				)
		__A							:    Optional[Any]         =      next(blocks_dataset.take(1				).as_numpy_iterator()				)

		return np_record


class _a   :

	'''simple docstring'''



	def __init__(   self							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase=2							, __UpperCAmelCase=1_000							, __UpperCAmelCase=100							, __UpperCAmelCase=100_000							, ):
			from scann.scann_ops.py.scann_ops_pybind import builder as Builder

			__A							:    List[Any]         =      Builder(db=__UpperCAmelCase							, num_neighbors=__UpperCAmelCase							, distance_measure="dot_product"    )
			__A							:    Optional[int]         =      builder.tree(
			    num_leaves=__UpperCAmelCase							, num_leaves_to_search=__UpperCAmelCase							, training_sample_size=__UpperCAmelCase    )
			__A							:    Optional[Any]         =      builder.score_ah(dimensions_per_block=__UpperCAmelCase    )

			__A							:    List[Any]         =      builder.build()







	def      __UpperCAmelCase(   self							, __UpperCAmelCase    ):
			__A  ,   __A							:    Optional[Any]         =      self.searcher.search_batched(question_projection.detach().cpu()    )
			return retrieved_block_ids.astype("int64"    )


class _a   :

	'''simple docstring'''



	def __init__(   self							, __UpperCAmelCase							, __UpperCAmelCase    ):
			super().__init__()
			__A							:    List[str]         =      block_records
			__A							:    Dict         =      tokenizer

	def __call__(   self							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase=None							, __UpperCAmelCase="pt"    ):
			__A							:    str         =      np.take(self.block_records							, indices=__UpperCAmelCase							, axis=0    )

			__A							:    Any         =      self.tokenizer.decode(question_input_ids[0]							, skip_special_tokens=__UpperCAmelCase    )

			__A							:    Tuple         =      []
			__A							:    Dict         =      []
			for retrieved_block in retrieved_blocks:
					text.append(__UpperCAmelCase    )
					text_pair.append(retrieved_block.decode()    )

			__A							:    Optional[int]         =      self.tokenizer(
			    __UpperCAmelCase							, __UpperCAmelCase							, padding=__UpperCAmelCase							, truncation=__UpperCAmelCase							, return_special_tokens_mask=__UpperCAmelCase							, max_length=__UpperCAmelCase    )
			__A							:    Any         =      concat_inputs.convert_to_tensors(__UpperCAmelCase    )

			if answer_ids is not None:
					return self.block_has_answer(__UpperCAmelCase							, __UpperCAmelCase    ) + (concat_inputs_tensors,)
			else:
					return (None, None, None, concat_inputs_tensors)

	@classmethod
	def      __UpperCAmelCase(   cls							, __UpperCAmelCase							, *__UpperCAmelCase							, **__UpperCAmelCase    ):
			if os.path.isdir(__UpperCAmelCase    ):
					__A							:    List[str]         =      os.path.join(__UpperCAmelCase							, _REALM_BLOCK_RECORDS_FILENAME    )
			else:
					__A							:    Optional[int]         =      hf_hub_download(
					    repo_id=__UpperCAmelCase							, filename=_REALM_BLOCK_RECORDS_FILENAME							, **__UpperCAmelCase    )
			__A							:    List[str]         =      np.load(__UpperCAmelCase							, allow_pickle=__UpperCAmelCase    )

			__A							:    List[Any]         =      AutoTokenizer.from_pretrained(__UpperCAmelCase							, *__UpperCAmelCase							, **__UpperCAmelCase    )

			return cls(__UpperCAmelCase							, __UpperCAmelCase    )

	def      __UpperCAmelCase(   self							, __UpperCAmelCase    ):
			# save block records
			np.save(os.path.join(__UpperCAmelCase							, _REALM_BLOCK_RECORDS_FILENAME    )							, self.block_records    )
			# save tokenizer
			self.tokenizer.save_pretrained(__UpperCAmelCase    )







	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase    ):
			__A							:    Dict         =      []
			__A							:    int         =      []
			__A							:    Optional[Any]         =      []
			__A							:    Optional[Any]         =      0

			for input_id in concat_inputs.input_ids:
					__A							:    Tuple         =      input_id.tolist()
					# Check answers between two [SEP] tokens
					__A							:    List[str]         =      input_id_list.index(self.tokenizer.sep_token_id    )
					__A							:    List[Any]         =      first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id    )

					start_pos.append([]    )
					end_pos.append([]    )
					for answer in answer_ids:
							for idx in range(first_sep_idx + 1							, __UpperCAmelCase    ):
									if answer[0] == input_id_list[idx]:
											if input_id_list[idx : idx + len(__UpperCAmelCase    )] == answer:
													start_pos[-1].append(__UpperCAmelCase    )
													end_pos[-1].append(idx + len(__UpperCAmelCase    ) - 1    )

					if len(start_pos[-1]    ) == 0:
							has_answers.append(__UpperCAmelCase    )
					else:
							has_answers.append(__UpperCAmelCase    )
							if len(start_pos[-1]    ) > max_answers:
									__A							:    Optional[Any]         =      len(start_pos[-1]    )

        # Pad -1 to max_answers
			for start_pos_, end_pos_ in zip(__UpperCAmelCase							, __UpperCAmelCase    ):
					if len(start_pos_    ) < max_answers:
							__A							:    str         =      [-1] * (max_answers - len(start_pos_    ))
							start_pos_ += padded
							end_pos_ += padded
			return has_answers, start_pos, end_pos






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}'''    )




