



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)
_lowercase:				Optional[Any]       =	"\\n"

_lowercase:				List[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"

_lowercase:				Dict       =	"\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"

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

try:
				from jiwer import transforms as tr

				_lowercase:				Any       =	True
except ImportError:
				_lowercase:				Optional[int]       =	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	, lowerCamelCase_ = " "	):
										"""simple docstring"""

										a        =     sentence_delimiter




					def   UpperCamelCase_ (self	, lowerCamelCase_	):
										"""simple docstring"""

										return list(lowerCamelCase_	)




					def   UpperCamelCase_ (self	, lowerCamelCase_	):
										"""simple docstring"""

										a        =     []
										for sent_idx, sentence in enumerate(lowerCamelCase_	):
															chars.extend(self.process_string(lowerCamelCase_	)	)
															if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCamelCase_	) - 1:
																				chars.append(self.sentence_delimiter	)
										return chars




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






def 					a(   A :					List[Any]				,  A :					int )	-> Union[str, Any]:
					"""simple docstring"""





					return float((preds == labels).mean() )






def 					a(   A :					int				,  A :					List[str] )	-> str:
					"""simple docstring"""





					return {
					    "f1": float(fa_score(y_true=A				,  y_pred=A				,  average="macro" ) ),
					    "accuracy": simple_accuracy(A				,  A ),
					}






def 					a(   A :					Optional[Any]				,  A :					Dict				,  A :					int="exp"				,  A :					int=None				,  A :					List[Any]=False				,  A :					Tuple=False				,  A :					Tuple=None				,  A :					Tuple=False				,  )	-> Tuple:
					"""simple docstring"""





					a        =     [[label] for label in labels]
					a        =     list(A )
					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(A ) != 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(A )]
					a        =     scb.corpus_bleu(
					    A				,  A				,  smooth_method=A				,  smooth_value=A				,  force=A				,  lowercase=A				,  use_effective_order=A				,  **({"tokenize": tokenize} if tokenize else {})				,  )
					return {"bleu": output.score}






def 					a(   A :					str				,  A :					Tuple				,  A :					Optional[Any]				,  A :					str )	-> int:
					"""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:
										a        =     compute_measures(A				,  A )["wer"]

										a        =     compute_measures(A				,  A				,  truth_transform=A				,  hypothesis_transform=A )["wer"]
										return {"wer": wer, "cer": cer}
					else:

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

										return {"wer": compute_score(A				,  A				,  "wer" ), "cer": compute_score(A				,  A				,  "cer" )}







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





	"""simple docstring"""




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						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(lowerCamelCase_	), "references": datasets.Value(lowerCamelCase_	)}	)	, codebase_urls=[]	, reference_urls=[]	, format="numpy"	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_=None	, lowerCamelCase_=None	):
						"""simple docstring"""

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





import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							None






def 					a(   A :					Dict				,  A :					Optional[int]=0.999				,  A :					Optional[int]="cosine"				,  )	-> List[str]:
					"""simple docstring"""





					if alpha_transform_type == "cosine":

										def alpha_bar_fn(A :					Dict ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(A :					int ):
															return math.exp(t * -12.0 )

					else:
										raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

					a        =     []
					for i in range(A ):
										a        =     i / num_diffusion_timesteps
										a        =     (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(A ) / alpha_bar_fn(A )				,  A ) )
					return torch.tensor(A				,  dtype=torch.floataa )







class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""
	__A					=							[e.name for e in KarrasDiffusionSchedulers]
	__A					=							1




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 1000	, lowerCamelCase_ = 0.0001	, lowerCamelCase_ = 0.02	, lowerCamelCase_ = "linear"	, lowerCamelCase_ = None	, lowerCamelCase_ = False	, lowerCamelCase_ = "epsilon"	, lowerCamelCase_ = "linspace"	, lowerCamelCase_ = 0	, ):
						"""simple docstring"""

						if trained_betas is not None:
											a        =     torch.tensor(lowerCamelCase_	, dtype=torch.floataa	)
						elif beta_schedule == "linear":
											a        =     torch.linspace(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, dtype=torch.floataa	)
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											a        =     (
											    torch.linspace(beta_start**0.5	, beta_end**0.5	, lowerCamelCase_	, dtype=torch.floataa	) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											a        =     betas_for_alpha_bar(lowerCamelCase_	)
						else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

						a        =     1.0 - self.betas
						a        =     torch.cumprod(self.alphas	, dim=0	)

						a        =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
						a        =     np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
						a        =     torch.from_numpy(lowerCamelCase_	)

						# setable values
						a        =     None
						a        =     use_karras_sigmas
						self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	)
						a        =     []
						a        =     False




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						if isinstance(lowerCamelCase_	, torch.Tensor	):
											a        =     timestep.to(self.timesteps.device	)
						a        =     (self.timesteps == timestep).nonzero().item()
						a        =     self.sigmas[step_index]
						a        =     sample / ((sigma**2 + 1) ** 0.5)
						a        =     True
						return sample




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						def lms_derivative(lowerCamelCase_	):
											a        =     1.0
											for k in range(lowerCamelCase_	):
																if current_order == k:
																					continue
																prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
											return prod

						a        =     integrate.quad(lowerCamelCase_	, self.sigmas[t]	, self.sigmas[t + 1]	, epsrel=1E-4	)[0]

						return integrated_coeff




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     num_inference_steps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
											a        =     np.linspace(0	, self.config.num_train_timesteps - 1	, lowerCamelCase_	, dtype=lowerCamelCase_	)[
											    ::-1
											].copy()
						elif self.config.timestep_spacing == "leading":
											a        =     self.config.num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											a        =     (np.arange(0	, lowerCamelCase_	) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_	)
											timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
											a        =     self.config.num_train_timesteps / self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											a        =     (np.arange(self.config.num_train_timesteps	, 0	, -step_ratio	)).round().copy().astype(lowerCamelCase_	)
											timesteps -= 1
						else:
											raise ValueError(
											    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

						a        =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
						a        =     np.log(lowerCamelCase_	)
						a        =     np.interp(lowerCamelCase_	, np.arange(0	, len(lowerCamelCase_	)	)	, lowerCamelCase_	)

						if self.use_karras_sigmas:
											a        =     self._convert_to_karras(in_sigmas=lowerCamelCase_	)
											a        =     np.array([self._sigma_to_t(lowerCamelCase_	, lowerCamelCase_	) for sigma in sigmas]	)

						a        =     np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)

						a        =     torch.from_numpy(lowerCamelCase_	).to(device=lowerCamelCase_	)
						if str(lowerCamelCase_	).startswith("mps"	):
											# mps does not support float64
											a        =     torch.from_numpy(lowerCamelCase_	).to(lowerCamelCase_	, dtype=torch.floataa	)
						else:
											a        =     torch.from_numpy(lowerCamelCase_	).to(device=lowerCamelCase_	)

						a        =     []




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     np.log(lowerCamelCase_	)

						# get distribution
						a        =     log_sigma - log_sigmas[:, np.newaxis]

						# get sigmas range
						a        =     np.cumsum((dists >= 0)	, axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
						a        =     low_idx + 1

						a        =     log_sigmas[low_idx]
						a        =     log_sigmas[high_idx]

						# interpolate sigmas
						a        =     (low - log_sigma) / (low - high)
						a        =     np.clip(lowerCamelCase_	, 0	, 1	)

						# transform interpolation to time range
						a        =     (1 - w) * low_idx + w * high_idx
						a        =     t.reshape(sigma.shape	)
						return t




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     in_sigmas[-1].item()
						a        =     in_sigmas[0].item()

						a        =     7.0  # 7.0 is the value used in the paper
						a        =     np.linspace(0	, 1	, self.num_inference_steps	)
						a        =     sigma_min ** (1 / rho)
						a        =     sigma_max ** (1 / rho)
						a        =     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = 4	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if not self.is_scale_input_called:
											warnings.warn(
											    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
											    "See `StableDiffusionPipeline` for a usage example."	)

						if isinstance(lowerCamelCase_	, torch.Tensor	):
											a        =     timestep.to(self.timesteps.device	)
						a        =     (self.timesteps == timestep).nonzero().item()
						a        =     self.sigmas[step_index]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
											a        =     sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											a        =     model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						elif self.config.prediction_type == "sample":
											a        =     model_output
						else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''	)

						# 2. Convert to an ODE derivative
						a        =     (sample - pred_original_sample) / sigma
						self.derivatives.append(lowerCamelCase_	)
						if len(self.derivatives	) > order:
											self.derivatives.pop(0	)

						# 3. Compute linear multistep coefficients
						a        =     min(step_index + 1	, lowerCamelCase_	)
						a        =     [self.get_lms_coefficient(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	) for curr_order in range(lowerCamelCase_	)]

						# 4. Compute previous sample based on the derivatives path
						a        =     sample + sum(
						    coeff * derivative for coeff, derivative in zip(lowerCamelCase_	, reversed(self.derivatives	)	)	)

						if not return_dict:
											return (prev_sample,)

						return LMSDiscreteSchedulerOutput(prev_sample=lowerCamelCase_	, pred_original_sample=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, ):
						"""simple docstring"""

						a        =     self.sigmas.to(device=original_samples.device	, dtype=original_samples.dtype	)
						if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_	):
											# mps does not support float64
											a        =     self.timesteps.to(original_samples.device	, dtype=torch.floataa	)
											a        =     timesteps.to(original_samples.device	, dtype=torch.floataa	)
						else:
											a        =     self.timesteps.to(original_samples.device	)
											a        =     timesteps.to(original_samples.device	)

						a        =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

						a        =     sigmas[step_indices].flatten()
						while len(sigma.shape	) < len(original_samples.shape	):
											a        =     sigma.unsqueeze(-1	)

						a        =     original_samples + noise * sigma
						return noisy_samples




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
