
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)
__a        = '\\n'

__a        = '\\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'

__a        = '\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'

__a        = ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
__a        = ''

try:
						from jiwer import transforms as tr

						__a        = True
except ImportError:
						__a        = False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):
						class 			lowercase__(    tr.AbstractTransform							):



							"""simple docstring"""




							def __init__(	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      str = " "	)	->					Any:
											lowercase_					  = sentence_delimiter
							def 						_lowercase  (	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      str	)	->					Union[str, Any]:
											return list(SCREAMING_SNAKE_CASE_	)




							def 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      List[str]	)	->					List[str]:
											lowercase_					  = []
											for sent_idx, sentence in enumerate(SCREAMING_SNAKE_CASE_	):
															chars.extend(self.process_string(SCREAMING_SNAKE_CASE_	)	)
															if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(SCREAMING_SNAKE_CASE_	) - 1:
																			chars.append(self.sentence_delimiter	)
											return chars

						__a        = tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
elif _jiwer_available:
						__a        = tr.Compose(
						    [
						        tr.RemoveMultipleSpaces(),
						        tr.Strip(),
						        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
						        tr.ReduceToListOfListOfChars(),
						    ]
						)
else:
						__a        = None




def     _a						(							snake_case__: List[Any]						,       snake_case__: List[str] ):


				'''simple docstring'''
				return float((preds == labels).mean() )




def     _a						(							snake_case__: Optional[int]						,       snake_case__: Optional[Any] ):


				'''simple docstring'''
				return {
				    "f1": float(fa_score(y_true=snake_case__						,       y_pred=snake_case__						,       average='''macro''' ) ),
				    "accuracy": simple_accuracy(snake_case__						,       snake_case__ ),
				}




def     _a						(							snake_case__: Any						,       snake_case__: Union[str, Any]						,       snake_case__: Tuple="exp"						,       snake_case__: Optional[int]=None						,       snake_case__: Optional[int]=False						,       snake_case__: Any=False						,       snake_case__: List[str]=None						,       snake_case__: int=False						,       ):


				'''simple docstring'''
				lowercase_					  = [[label] for label in labels]
				lowercase_					  = 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"`.''' )

				lowercase_					  = 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''' )
				lowercase_					  = [[refs[i] for refs in labels] for i in range(snake_case__ )]
				lowercase_					  = 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     _a						(							snake_case__: Union[str, Any]						,       snake_case__: List[str]						,       snake_case__: int						,       snake_case__: List[Any] ):


				'''simple docstring'''
				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:
								lowercase_					  = compute_measures(snake_case__						,       snake_case__ )['''wer''']

								lowercase_					  = 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__: List[Any]						,       snake_case__: Optional[int]						,       snake_case__: Union[str, Any]="wer" ):
												lowercase_					  = 0
												lowercase_					  = 0
												for prediction, reference in zip(snake_case__						,       snake_case__ ):
																if score_type == "wer":
																				lowercase_					  = compute_measures(snake_case__						,       snake_case__ )
																elif score_type == "cer":
																				lowercase_					  = 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 			lowercase__(    datasets.Metric							):



	"""simple docstring"""




	def 						_lowercase  (	self  :      Dict	)	->					Optional[Any]:
					if self.config_name not in _CONFIG_NAMES:
									raise KeyError(f'''You should supply a configuration name selected in {_CONFIG_NAMES}'''	)

					lowercase_					  = '''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(SCREAMING_SNAKE_CASE_	), '''references''': datasets.Value(SCREAMING_SNAKE_CASE_	)}	)	,							codebase_urls=[]	,							reference_urls=[]	,							format='''numpy'''	,							)




	def 						_lowercase  (	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							SCREAMING_SNAKE_CASE_  :      Dict=None	)	->					Optional[int]:
					lowercase_					  = bleu_kwargs if bleu_kwargs is not None else {}
					lowercase_					  = wer_kwargs if wer_kwargs is not None else {}

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






import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
__a        = False
__a        = Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__a        = datasets.Features(
    OrderedDict(
        {
            'attr_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'attr_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'boxes': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='float32'),
            'img_id': datasets.Value('int32'),
            'obj_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'obj_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'roi_features': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=sys.argv[1:]	)	->					int:
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = 1
					lowercase_  ,     lowercase_					  = getopt.getopt(SCREAMING_SNAKE_CASE_	,							'''i:o:b:s'''	,							['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
					for opt, arg in opts:
									if opt in ("-i", "--inputdir"):
													lowercase_					  = arg
									elif opt in ("-o", "--outfile"):
													lowercase_					  = arg
									elif opt in ("-b", "--batch_size"):
													lowercase_					  = int(SCREAMING_SNAKE_CASE_	)
									elif opt in ("-s", "--subset_list"):
													lowercase_					  = arg

					assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
					assert outputfile is not None and not os.path.isfile(SCREAMING_SNAKE_CASE_	), f'''{outputfile}'''
					if subset_list is not None:
									with open(os.path.realpath(SCREAMING_SNAKE_CASE_	)	) as f:
													lowercase_					  = {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE_	)}
					else:
									lowercase_					  = None

					lowercase_					  = CONFIG
					if torch.cuda.is_available():
									lowercase_					  = '''cuda'''
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = Preprocess(self.config	)
					lowercase_					  = GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''	,							config=self.config	)
					lowercase_					  = batch_size if batch_size != 0 else 1
					lowercase_					  = DEFAULT_SCHEMA
	def 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      int	)	->					Any:
					lowercase_					  = int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
					lowercase_					  = os.path.join(self.inputdir	,							SCREAMING_SNAKE_CASE_	)
					return (img_id, filepath)
	@property
	def 						_lowercase  (	self  :      List[str]	)	->					List[Any]:
					lowercase_					  = []
					for i, file in enumerate(os.listdir(self.inputdir	)	):
									if self.subset_list is not None and i not in self.subset_list:
													continue
									batch.append(self._vqa_file_split(SCREAMING_SNAKE_CASE_	)	)
									if len(SCREAMING_SNAKE_CASE_	) == self.batch:
													lowercase_					  = batch
													lowercase_					  = []
													yield list(map(SCREAMING_SNAKE_CASE_	,							zip(*SCREAMING_SNAKE_CASE_	)	)	)

					for i in range(1	):
									yield list(map(SCREAMING_SNAKE_CASE_	,							zip(*SCREAMING_SNAKE_CASE_	)	)	)




	def __call__(	self  :      Any	)	->					List[str]:
					# make writer
					if not TEST:
									lowercase_					  = datasets.ArrowWriter(features=self.schema	,							path=self.outputfile	)
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator	):
									lowercase_  ,     lowercase_  ,     lowercase_					  = self.preprocess(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = self.model(
									    SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							scales_yx=SCREAMING_SNAKE_CASE_	,							padding='''max_detections'''	,							max_detections=self.config.MAX_DETECTIONS	,							pad_value=0	,							return_tensors='''np'''	,							location='''cpu'''	,							)
									lowercase_					  = output_dict.pop('''normalized_boxes'''	)
									if not TEST:
													lowercase_					  = np.array(SCREAMING_SNAKE_CASE_	)
													lowercase_					  = self.schema.encode_batch(SCREAMING_SNAKE_CASE_	)
													writer.write_batch(SCREAMING_SNAKE_CASE_	)
									if TEST:
													break
									# finalizer the writer
					if not TEST:
									lowercase_  ,     lowercase_					  = writer.finalize()
									print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''	)




def     a						(							snake_case__: Optional[Any] ):


				'''simple docstring'''
				try:
								lowercase_					  = json.load(snake_case__ )
								try:
												lowercase_					  = list(data.keys() )
								except Exception:
												lowercase_					  = [d['''img_id'''] for d in data]
				except Exception:
								try:
												lowercase_					  = eval(stream.read() )
								except Exception:
												lowercase_					  = stream.read().split('''\n''' )
				return data


if __name__ == "__main__":
						__a        = Extract(sys.argv[1:])
						extract()
						if not TEST:
												__a        = datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)




