

'''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     lowercase_		:

  '''simple docstring'''





  __lowerCAmelCase   :	CommonSchedulerState

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

  # running values
  __lowerCAmelCase   :	Optional[jnp.ndarray]      					=				None
  @classmethod
  def   snake_case_     (       cls				,	a_				,	a_				,	a_				,	a_       )							->			Tuple:


    """simple docstring"""
    return cls(common=a_				,	init_noise_sigma=a_				,	timesteps=a_				,	sigmas=a_       )




@dataclass
class     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	LMSDiscreteSchedulerState




class     lowercase_		(						a	,    a	):

  '''simple docstring'''





  __lowerCAmelCase   :	Dict      					=				[e.name for e in FlaxKarrasDiffusionSchedulers]

  __lowerCAmelCase   :	jnp.dtype
  @property
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    return True
  @register_to_config
  def __init__(       self				,	a_ = 1_0_0_0				,	a_ = 0.0001				,	a_ = 0.02				,	a_ = "linear"				,	a_ = None				,	a_ = "epsilon"				,	a_ = jnp.floataa				,	)							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		dtype
  def   snake_case_     (       self				,	a_ = None       )							->			LMSDiscreteSchedulerState:


    """simple docstring"""
    if common is None:
      UpperCAmelCase  							=		CommonSchedulerState.create(self       )

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

    # standard deviation of the initial noise distribution
    UpperCAmelCase  							=		sigmas.max()

    return LMSDiscreteSchedulerState.create(
        common=a_				,	init_noise_sigma=a_				,	timesteps=a_				,	sigmas=a_				,	)
  def   snake_case_     (       self				,	a_				,	a_				,	a_       )							->			jnp.ndarray:


    """simple docstring"""
    ((UpperCAmelCase )							,  )  							=		jnp.where(state.timesteps == timestep				,	size=1       )
    UpperCAmelCase  							=		step_index[0]

    UpperCAmelCase  							=		state.sigmas[step_index]
    UpperCAmelCase  							=		sample / ((sigma**2 + 1) ** 0.5)
    return sample
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_       )							->			Tuple:


    """simple docstring"""

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

    UpperCAmelCase  							=		integrate.quad(a_				,	state.sigmas[t]				,	state.sigmas[t + 1]				,	epsrel=1E-4       )[0]

    return integrated_coeff
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = ()       )							->			LMSDiscreteSchedulerState:


    """simple docstring"""
    UpperCAmelCase  							=		jnp.linspace(self.config.num_train_timesteps - 1				,	0				,	a_				,	dtype=self.dtype       )

    UpperCAmelCase  							=		jnp.floor(a_       ).astype(jnp.intaa       )
    UpperCAmelCase  							=		jnp.ceil(a_       ).astype(jnp.intaa       )

    UpperCAmelCase  							=		jnp.mod(a_				,	1.0       )

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

    UpperCAmelCase  							=		timesteps.astype(jnp.intaa       )

    # initial running values
    UpperCAmelCase  							=		jnp.zeros((0,) + shape				,	dtype=self.dtype       )

    return state.replace(
        timesteps=a_				,	sigmas=a_				,	num_inference_steps=a_				,	derivatives=a_				,	)
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_				,	a_ = 4				,	a_ = True				,	)							->			Union[FlaxLMSSchedulerOutput, Tuple]:


    """simple docstring"""
    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'       )

    UpperCAmelCase  							=		state.sigmas[timestep]

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

    # 3. Compute linear multistep coefficients
    UpperCAmelCase  							=		min(timestep + 1				,	a_       )
    UpperCAmelCase  							=		[self.get_lms_coefficient(a_				,	a_				,	a_				,	a_       ) for curr_order in range(a_       )]

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

    if not return_dict:
      return (prev_sample, state)

    return FlaxLMSSchedulerOutput(prev_sample=a_				,	state=a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_				,	)							->			jnp.ndarray:


    """simple docstring"""
    UpperCAmelCase  							=		state.sigmas[timesteps].flatten()
    UpperCAmelCase  							=		broadcast_to_shape_from_left(a_				,	noise.shape       )

    UpperCAmelCase  							=		original_samples + noise * sigma

    return noisy_samples





  def __len__(       self       )							->			Dict:


    """simple docstring"""
    return self.config.num_train_timesteps





'''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)
_a			:	List[Any]								=  '\\n'

_a			:	Optional[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'

_a			:	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'

_a			:	Dict								=  ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
_a			:	Dict								=  ''

try:
  from jiwer import transforms as tr

  _a			:	Optional[Any]								=  True
except ImportError:
  _a			:	List[Any]								=  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				,	a_ = " "       )							->			str:


      """simple docstring"""
      UpperCAmelCase  							=		sentence_delimiter
    def   snake_case_     (       self				,	a_       )							->			Optional[int]:


      """simple docstring"""
      return list(a_       )





    def   snake_case_     (       self				,	a_       )							->			List[str]:


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




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


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				Union[str, Any]  ):
  return float((preds == labels).mean()  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				Optional[Any]  ):
  return {
      "f1": float(fa_score(y_true=SCREAMING_SNAKE_CASE    ,				y_pred=SCREAMING_SNAKE_CASE    ,				average='macro'  )  ),
      "accuracy": simple_accuracy(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ),
  }


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				str="exp"    ,				SCREAMING_SNAKE_CASE :				Union[str, Any]=None    ,				SCREAMING_SNAKE_CASE :				Any=False    ,				SCREAMING_SNAKE_CASE :				Optional[Any]=False    ,				SCREAMING_SNAKE_CASE :				Optional[int]=None    ,				SCREAMING_SNAKE_CASE :				List[Any]=False    ,				):
  # xtreme-s can only have one label
  UpperCAmelCase  							=		[[label] for label in labels]
  UpperCAmelCase  							=		list(SCREAMING_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"`.'  )

  UpperCAmelCase  							=		len(labels[0]  )
  if any(len(SCREAMING_SNAKE_CASE  ) != references_per_prediction for refs in labels  ):
    raise ValueError('Sacrebleu requires the same number of references for each prediction'  )
  UpperCAmelCase  							=		[[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE  )]
  UpperCAmelCase  							=		scb.corpus_bleu(
      SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				smooth_method=SCREAMING_SNAKE_CASE    ,				smooth_value=SCREAMING_SNAKE_CASE    ,				force=SCREAMING_SNAKE_CASE    ,				lowercase=SCREAMING_SNAKE_CASE    ,				use_effective_order=SCREAMING_SNAKE_CASE    ,				**({'tokenize': tokenize} if tokenize else {})    ,				)
  return {"bleu": output.score}


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Optional[int]  ):
  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:
    UpperCAmelCase  							=		compute_measures(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )['wer']

    UpperCAmelCase  							=		compute_measures(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				truth_transform=SCREAMING_SNAKE_CASE    ,				hypothesis_transform=SCREAMING_SNAKE_CASE  )['wer']
    return {"wer": wer, "cer": cer}
  else:

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

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



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

  '''simple docstring'''





  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    if self.config_name not in _CONFIG_NAMES:
      raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''       )

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





  def   snake_case_     (       self				,	a_				,	a_				,	a_=None				,	a_=None       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		bleu_kwargs if bleu_kwargs is not None else {}
    UpperCAmelCase  							=		wer_kwargs if wer_kwargs is not None else {}

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


