


"""simple docstring"""






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)
SCREAMING_SNAKE_CASE__				:  int						= "\\n"

SCREAMING_SNAKE_CASE__				:  List[str]						= "\\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"

SCREAMING_SNAKE_CASE__				:  Any						= "\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"

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

try:
 from jiwer import transforms as tr

 SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= True
except ImportError:
 SCREAMING_SNAKE_CASE__				:  Any						= False

if _jiwer_available and version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):


 class 	A_       (     tr.AbstractTransform   ):





   """simple docstring"""

   def __init__(						self  ,					__UpperCAmelCase = " "						)				->					Tuple:
      a   :	Dict						=    sentence_delimiter
   def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Tuple:
      return list(__UpperCAmelCase						)






   def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[str]:
      a   :	List[Any]						=    []
      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



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




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[str]:
   return float((preds == labels).mean()				)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Optional[Any]:
   return {
       "f1": float(fa_score(y_true=UpperCAmelCase__       ,   y_pred=UpperCAmelCase__       ,   average='macro'				)				),
       "accuracy": simple_accuracy(UpperCAmelCase__       ,   UpperCAmelCase__				),
   }




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




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     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   :	Tuple						=    compute_measures(UpperCAmelCase__       ,   UpperCAmelCase__				)['wer']

      a   :	Optional[Any]						=    compute_measures(UpperCAmelCase__       ,   UpperCAmelCase__       ,   truth_transform=UpperCAmelCase__       ,   hypothesis_transform=UpperCAmelCase__				)['wer']
      return {"wer": wer, "cer": cer}
   else:

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

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


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





  """simple docstring"""

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

     a   :	List[str]						=    '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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None						)				->					List[Any]:
     a   :	Any						=    bleu_kwargs if bleu_kwargs is not None else {}
     a   :	Tuple						=    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   :	Optional[Any]						=    bleu_kwargs.pop('smooth_method'  ,					'exp'						)
        a   :	List[str]						=    bleu_kwargs.pop('smooth_value'  ,					__UpperCAmelCase						)
        a   :	List[Any]						=    bleu_kwargs.pop('force'  ,					__UpperCAmelCase						)
        a   :	List[Any]						=    bleu_kwargs.pop('lowercase'  ,					__UpperCAmelCase						)
        a   :	str						=    bleu_kwargs.pop('tokenize'  ,					__UpperCAmelCase						)
        a   :	Tuple						=    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   :	Dict						=    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}'						)









"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class 	A_       :





  """simple docstring"""

  lowercase		:	CommonSchedulerState

  # setable values
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	Optional[int]   =						None

  # running values
  lowercase		:	Optional[jnp.ndarray]   =						None
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     return cls(common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase						)



@dataclass
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	LMSDiscreteSchedulerState



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	int   =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase		:	jnp.dtype
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     return True
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = jnp.floataa  ,					)				->					Union[str, Any]:
     a   :	Dict						=    dtype
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					LMSDiscreteSchedulerState:
     if common is None:
        a   :	Dict						=    CommonSchedulerState.create(self						)

     a   :	List[Any]						=    jnp.arange(0  ,					self.config.num_train_timesteps						).round()[::-1]
     a   :	Dict						=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     a   :	Union[str, Any]						=    sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					jnp.ndarray:
     ((a)	, )   :	Optional[int]						=    jnp.where(state.timesteps == timestep  ,					size=1						)
     a   :	Optional[Any]						=    step_index[0]

     a   :	Union[str, Any]						=    state.sigmas[step_index]
     a   :	Optional[int]						=    sample / ((sigma**2 + 1) ** 0.5)
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:

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

     a   :	Union[str, Any]						=    integrate.quad(__UpperCAmelCase  ,					state.sigmas[t]  ,					state.sigmas[t + 1]  ,					epsrel=1E-4						)[0]

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = ()						)				->					LMSDiscreteSchedulerState:
     a   :	Optional[int]						=    jnp.linspace(self.config.num_train_timesteps - 1  ,					0  ,					__UpperCAmelCase  ,					dtype=self.dtype						)

     a   :	Dict						=    jnp.floor(__UpperCAmelCase						).astype(jnp.intaa						)
     a   :	str						=    jnp.ceil(__UpperCAmelCase						).astype(jnp.intaa						)

     a   :	List[Any]						=    jnp.mod(__UpperCAmelCase  ,					1.0						)

     a   :	Optional[int]						=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     a   :	int						=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     a   :	Optional[int]						=    jnp.concatenate([sigmas, jnp.array([0.0]  ,					dtype=self.dtype						)]						)

     a   :	Any						=    timesteps.astype(jnp.intaa						)

     # initial running values
     a   :	Optional[int]						=    jnp.zeros((0,) + shape  ,					dtype=self.dtype						)

     return state.replace(
         timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					num_inference_steps=__UpperCAmelCase  ,					derivatives=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 4  ,					__UpperCAmelCase = True  ,					)				->					Union[FlaxLMSSchedulerOutput, Tuple]:
     if state.num_inference_steps is None:
        raise ValueError(
            'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

     a   :	Dict						=    state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        a   :	Any						=    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a   :	str						=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     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   :	str						=    (sample - pred_original_sample) / sigma
     a   :	int						=    state.replace(derivatives=jnp.append(state.derivatives  ,					__UpperCAmelCase						)						)
     if len(state.derivatives						) > order:
        a   :	Any						=    state.replace(derivatives=jnp.delete(state.derivatives  ,					0						)						)

     # 3. Compute linear multistep coefficients
     a   :	Any						=    min(timestep + 1  ,					__UpperCAmelCase						)
     a   :	Dict						=    [self.get_lms_coefficient(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

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

     if not return_dict:
        return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase  ,					state=__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					jnp.ndarray:
     a   :	Dict						=    state.sigmas[timesteps].flatten()
     a   :	Union[str, Any]						=    broadcast_to_shape_from_left(__UpperCAmelCase  ,					noise.shape						)

     a   :	List[Any]						=    original_samples + noise * sigma

     return noisy_samples






  def __len__(						self						)				->					List[Any]:
     return self.config.num_train_timesteps





