import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowerCamelCase_				:		Optional[int]       						=       1_000


class 						a__							:



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=1.2      ,						UpperCAmelCase="g"      ,						UpperCAmelCase=0.5      ,						UpperCAmelCase="-"      ,						UpperCAmelCase="test_out.jpg"      ,						UpperCAmelCase=True      ,						UpperCAmelCase=False      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase=0.7      ,						) ->    List[str]:
								if isinstance(UpperCAmelCase      ,						torch.Tensor    ):
													__a							=   img.numpy().astype('np.uint8'    )
								if isinstance(UpperCAmelCase      ,						UpperCAmelCase    ):
													__a							=   img_tensorize(UpperCAmelCase    )
								assert isinstance(UpperCAmelCase      ,						np.ndarray    )

								__a					,      __a							=   img.shape[1], img.shape[0]
								__a							=   mplfigure.Figure(frameon=UpperCAmelCase    )
								__a							=   fig.get_dpi()
								__a							=   (width * scale + 1e-2) / dpi
								__a							=   (height * scale + 1e-2) / dpi
								fig.set_size_inches(UpperCAmelCase      ,						UpperCAmelCase    )
								__a							=   fig.add_axes([0.0, 0.0, 1.0, 1.0]    )
								ax.axis('off'    )
								ax.set_xlim(0.0      ,						UpperCAmelCase    )
								ax.set_ylim(UpperCAmelCase    )

								__a							=   saveas
								__a							=   rgb
								__a							=   pynb
								__a							=   img
								__a							=   edgecolor
								__a							=   0.5
								__a							=   linestyle
								__a							=   int(np.sqrt(min(UpperCAmelCase      ,						UpperCAmelCase    )    ) * scale // 3    )
								__a							=   width
								__a							=   height
								__a							=   scale
								__a							=   fig
								__a							=   ax
								__a							=   pad
								__a							=   idaobj
								__a							=   idaattr
								__a							=   FigureCanvasAgg(UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=None    ) ->    Tuple:
								if color is None:
													__a							=   self.edgecolor
								((__a)					,      (__a)					,      (__a)					,      (__a))							=   box
								__a							=   xa - xa
								__a							=   ya - ya
								self.ax.add_patch(
								    mpl.patches.Rectangle(
								        (xa, ya)      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						fill=UpperCAmelCase      ,						edgecolor=UpperCAmelCase      ,						linewidth=self.font_size // 3      ,						alpha=self.alpha      ,						linestyle=self.linestyle      ,						)    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None    ) ->    List[str]:
								if len(boxes.shape    ) > 2:
													__a							=   boxes[0]
								if len(obj_ids.shape    ) > 1:
													__a							=   obj_ids[0]
								if len(obj_scores.shape    ) > 1:
													__a							=   obj_scores[0]
								if len(attr_ids.shape    ) > 1:
													__a							=   attr_ids[0]
								if len(attr_scores.shape    ) > 1:
													__a							=   attr_scores[0]
								if isinstance(UpperCAmelCase      ,						torch.Tensor    ):
													__a							=   boxes.numpy()
								if isinstance(UpperCAmelCase      ,						UpperCAmelCase    ):
													__a							=   np.array(UpperCAmelCase    )
								assert isinstance(UpperCAmelCase      ,						np.ndarray    )
								__a							=   np.prod(boxes[:, 2:] - boxes[:, :2]      ,						axis=1    )
								__a							=   np.argsort(-areas    ).tolist()
								__a							=   boxes[sorted_idxs] if boxes is not None else None
								__a							=   obj_ids[sorted_idxs] if obj_ids is not None else None
								__a							=   obj_scores[sorted_idxs] if obj_scores is not None else None
								__a							=   attr_ids[sorted_idxs] if attr_ids is not None else None
								__a							=   attr_scores[sorted_idxs] if attr_scores is not None else None

								__a							=   [self._random_color(maximum=1    ) for _ in range(len(UpperCAmelCase    )    )]
								__a							=   [assigned_colors[idx] for idx in sorted_idxs]
								if obj_ids is not None:
													__a							=   self._create_text_labels_attr(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													for i in range(len(UpperCAmelCase    )    ):
																		__a							=   assigned_colors[i]
																		self.add_box(boxes[i]      ,						UpperCAmelCase    )
																		self.draw_labels(labels[i]      ,						boxes[i]      ,						UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Dict:
								__a					,      __a					,      __a					,      __a							=   box
								__a							=   (xa, ya)
								__a							=   (ya - ya) * (xa - xa)
								__a							=   _SMALL_OBJ * self.scale
								if instance_area < small or ya - ya < 4_0 * self.scale:
													if ya >= self.height - 5:
																		__a							=   (xa, ya)
													else:
																		__a							=   (xa, ya)

								__a							=   (ya - ya) / np.sqrt(self.height * self.width    )
								__a							=   self._change_color_brightness(UpperCAmelCase      ,						brightness_factor=0.7    )
								__a							=   np.clip((height_ratio - 0.02) / 0.08 + 1      ,						1.2      ,						2    )
								font_size *= 0.75 * self.font_size

								self.draw_text(
								    text=UpperCAmelCase      ,						position=UpperCAmelCase      ,						color=UpperCAmelCase      ,						)



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase="g"      ,						UpperCAmelCase="left"      ,						) ->    List[str]:
								__a							=   0
								__a							=   self.font_size
								__a							=   np.maximum(list(mplc.to_rgb(UpperCAmelCase    )    )      ,						0.2    )
								__a							=   max(0.8      ,						np.max(UpperCAmelCase    )    )
								__a							=   {
								    'facecolor': 'black',
								    'alpha': self.alpha,
								    'pad': self.pad,
								    'edgecolor': 'none',
								}
								__a					,      __a							=   position
								self.ax.text(
								    UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						size=font_size * self.scale      ,						family='sans-serif'      ,						bbox=UpperCAmelCase      ,						verticalalignment='top'      ,						horizontalalignment=UpperCAmelCase      ,						color=UpperCAmelCase      ,						zorder=1_0      ,						rotation=UpperCAmelCase      ,						)



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase=None    ) ->    List[Any]:
								if saveas is None:
													__a							=   self.saveas
								if saveas.lower().endswith('.jpg'    ) or saveas.lower().endswith('.png'    ):
													cva.imwrite(
													    UpperCAmelCase      ,						self._get_buffer()[:, :, ::-1]      ,						)
								else:
													self.fig.savefig(UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Tuple:
								__a							=   [self.idaobj[i] for i in classes]
								__a							=   [self.idaattr[i] for i in attr_classes]
								__a							=   [
								    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
								    for label, score, attr, attr_score in zip(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
								]
								return labels



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    List[Any]:
								__a							=   [self.idaobj[i] for i in classes]
								if scores is not None:
													if labels is None:
																		__a							=   ['{:.0f}%'.format(s * 1_0_0    ) for s in scores]
													else:
																		__a							=   ['{} {:.0f}%'.format(UpperCAmelCase      ,						s * 1_0_0    ) for li, s in zip(UpperCAmelCase      ,						UpperCAmelCase    )]
								return labels



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase=2_5_5    ) ->    int:
								__a							=   np.random.randint(0      ,						len(_COLORS    )    )
								__a							=   _COLORS[idx] * maximum
								if not self.rgb:
													__a							=   ret[::-1]
								return ret



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								if not self.pynb:
													__a					,      __a							=   self.canvas.print_to_buffer()
													if (width, height) != (self.width, self.height):
																		__a							=   cva.resize(self.img      ,						(width, height)    )
													else:
																		__a							=   self.img
								else:
													__a							=   io.BytesIO()  # works for cairo backend
													self.canvas.print_rgba(UpperCAmelCase    )
													__a					,      __a							=   self.width, self.height
													__a							=   buf.getvalue()
													__a							=   self.img

								__a							=   np.frombuffer(UpperCAmelCase      ,						dtype='uint8'    )
								__a							=   buffer.reshape(UpperCAmelCase      ,						UpperCAmelCase      ,						4    )
								__a					,      __a							=   np.split(UpperCAmelCase      ,						[3]      ,						axis=2    )

								try:
													import numexpr as ne  # fuse them with numexpr

													__a							=   ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'    )
								except ImportError:
													__a							=   alpha.astype('float32'    ) / 255.0
													__a							=   img * (1 - alpha) + rgb * alpha

								return visualized_image.astype('uint8'    )







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Dict:
								assert brightness_factor >= -1.0 and brightness_factor <= 1.0
								__a							=   mplc.to_rgb(UpperCAmelCase    )
								__a							=   colorsys.rgb_to_hls(*mplc.to_rgb(UpperCAmelCase    )    )
								__a							=   polygon_color[1] + (brightness_factor * polygon_color[1])
								__a							=   0.0 if modified_lightness < 0.0 else modified_lightness
								__a							=   1.0 if modified_lightness > 1.0 else modified_lightness
								__a							=   colorsys.hls_to_rgb(polygon_color[0]      ,						UpperCAmelCase      ,						polygon_color[2]    )
								return modified_color






lowerCamelCase_				:		Optional[int]       						=       (
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)





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}'''    )




