

"""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"""




  UpperCAmelCase__ :	CommonSchedulerState

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

  # running values
  UpperCAmelCase__ :	Optional[jnp.ndarray]         =None


  @classmethod
  def 	_lowercase (     cls      :	List[Any]			,			UpperCAmelCase__      :	CommonSchedulerState			,			UpperCAmelCase__      :	jnp.ndarray			,			UpperCAmelCase__      :	jnp.ndarray			,			UpperCAmelCase__      :	jnp.ndarray						)   ->Union[str, Any]:
        """simple docstring"""







        return cls(common=UpperCAmelCase__			,			init_noise_sigma=UpperCAmelCase__			,			timesteps=UpperCAmelCase__			,			sigmas=UpperCAmelCase__						)







@dataclass
class     a__    (				UpperCAmelCase__    ):







  """simple docstring"""




  UpperCAmelCase__ :	LMSDiscreteSchedulerState







class     a__    (				UpperCAmelCase__		,     UpperCAmelCase__    ):







  """simple docstring"""




  UpperCAmelCase__ :	List[Any]         =[e.name for e in FlaxKarrasDiffusionSchedulers]

  UpperCAmelCase__ :	jnp.dtype


  @property
  def 	_lowercase (     self      :	List[Any]						)   ->Any:
        """simple docstring"""







        return True


  @register_to_config
  def __init__(     self      :	Dict			,			UpperCAmelCase__      :	int = 1_0_0_0			,			UpperCAmelCase__      :	float = 0.00_01			,			UpperCAmelCase__      :	float = 0.02			,			UpperCAmelCase__      :	str = "linear"			,			UpperCAmelCase__      :	Optional[jnp.ndarray] = None			,			UpperCAmelCase__      :	str = "epsilon"			,			UpperCAmelCase__      :	jnp.dtype = jnp.floataa			,			)   ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: int					       =				dtype


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	Optional[CommonSchedulerState] = None						)   ->LMSDiscreteSchedulerState:
        """simple docstring"""







        if common is None:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				CommonSchedulerState.create(self						)

        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				jnp.arange(0			,			self.config.num_train_timesteps						).round()[::-1]
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        SCREAMING_SNAKE_CASE	: List[Any]					       =				sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=UpperCAmelCase__			,			init_noise_sigma=UpperCAmelCase__			,			timesteps=UpperCAmelCase__			,			sigmas=UpperCAmelCase__			,			)


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	LMSDiscreteSchedulerState			,			UpperCAmelCase__      :	jnp.ndarray			,			UpperCAmelCase__      :	int						)   ->jnp.ndarray:
        """simple docstring"""







        (SCREAMING_SNAKE_CASE						)	: Optional[Any]					       =				jnp.where(state.timesteps == timestep			,			size=1						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				step_index[0]

        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				state.sigmas[step_index]
        SCREAMING_SNAKE_CASE	: int					       =				sample / ((sigma**2 + 1) ** 0.5)
        return sample


  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	LMSDiscreteSchedulerState			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[int]			,			UpperCAmelCase__      :	Dict						)   ->int:
        """simple docstring"""







        def lms_derivative(UpperCAmelCase__      :	str						):
              SCREAMING_SNAKE_CASE	: Union[str, 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

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				integrate.quad(UpperCAmelCase__			,			state.sigmas[t]			,			state.sigmas[t + 1]			,			epsrel=1e-4						)[0]

        return integrated_coeff


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	LMSDiscreteSchedulerState			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	Tuple = ()						)   ->LMSDiscreteSchedulerState:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				jnp.linspace(self.config.num_train_timesteps - 1			,			0			,			UpperCAmelCase__			,			dtype=self.dtype						)

        SCREAMING_SNAKE_CASE	: List[str]					       =				jnp.floor(UpperCAmelCase__						).astype(jnp.intaa						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				jnp.ceil(UpperCAmelCase__						).astype(jnp.intaa						)

        SCREAMING_SNAKE_CASE	: int					       =				jnp.mod(UpperCAmelCase__			,			1.0						)

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

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				timesteps.astype(jnp.intaa						)

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

        return state.replace(
            timesteps=UpperCAmelCase__			,			sigmas=UpperCAmelCase__			,			num_inference_steps=UpperCAmelCase__			,			derivatives=UpperCAmelCase__			,			)


  def 	_lowercase (     self      :	int			,			UpperCAmelCase__      :	LMSDiscreteSchedulerState			,			UpperCAmelCase__      :	jnp.ndarray			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	jnp.ndarray			,			UpperCAmelCase__      :	int = 4			,			UpperCAmelCase__      :	bool = 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"""						)

        SCREAMING_SNAKE_CASE	: Tuple					       =				state.sigmas[timestep]

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

        # 3. Compute linear multistep coefficients
        SCREAMING_SNAKE_CASE	: List[Any]					       =				min(timestep + 1			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				[self.get_lms_coefficient(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						) for curr_order in range(UpperCAmelCase__						)]

        # 4. Compute previous sample based on the derivatives path
        SCREAMING_SNAKE_CASE	: 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      :	List[Any]			,			UpperCAmelCase__      :	LMSDiscreteSchedulerState			,			UpperCAmelCase__      :	jnp.ndarray			,			UpperCAmelCase__      :	jnp.ndarray			,			UpperCAmelCase__      :	jnp.ndarray			,			)   ->jnp.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				state.sigmas[timesteps].flatten()
        SCREAMING_SNAKE_CASE	: List[Any]					       =				broadcast_to_shape_from_left(UpperCAmelCase__			,			noise.shape						)

        SCREAMING_SNAKE_CASE	: str					       =				original_samples + noise * sigma

        return noisy_samples




  def __len__(     self      :	str						)   ->Optional[Any]:
        """simple docstring"""







        return self.config.num_train_timesteps







import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__	:       Tuple			      =      """\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
"""

UpperCAmelCase__	:       Union[str, Any]			      =      """\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
"""


UpperCAmelCase__	:       str			      =      """
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric(\"frugalscore\")
    >>> results = frugalscore.compute(predictions=['hello there', 'huggingface'], references=['hello world', 'hugging face'])
    >>> print([round(s, 3) for s in results[\"scores\"]])
    [0.631, 0.645]
"""







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







  """simple docstring"""




  def 	_lowercase (     self      :	Any						)   ->Optional[int]:
        """simple docstring"""







        return datasets.MetricInfo(
            description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""						),
                    """references""": datasets.Value("""string"""						),
                }						)			,			homepage="""https://github.com/moussaKam/FrugalScore"""			,			)


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	Tuple						)   ->Any:
        """simple docstring"""







        if self.config_name == "default":
              SCREAMING_SNAKE_CASE	: int					       =				"""moussaKam/frugalscore_tiny_bert-base_bert-score"""
        else:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.config_name
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				AutoTokenizer.from_pretrained(UpperCAmelCase__						)




  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	str=3_2			,			UpperCAmelCase__      :	Optional[int]=1_2_8			,			UpperCAmelCase__      :	Optional[int]=None			,			)   ->Any:
        """simple docstring"""







        assert len(UpperCAmelCase__						) == len(
            UpperCAmelCase__						), "predictions and references should have the same number of sentences."
        if device is not None:
              assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
        else:
              SCREAMING_SNAKE_CASE	: List[str]					       =				"""gpu""" if torch.cuda.is_available() else """cpu"""
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				TrainingArguments(
            """trainer"""			,			fpaa=(device == """gpu""")			,			per_device_eval_batch_size=UpperCAmelCase__			,			report_to="""all"""			,			no_cuda=(device == """cpu""")			,			log_level="""warning"""			,			)
        SCREAMING_SNAKE_CASE	: int					       =				{"""sentence1""": predictions, """sentence2""": references}
        SCREAMING_SNAKE_CASE	: List[str]					       =				datasets.Dataset.from_dict(UpperCAmelCase__						)

        def tokenize_function(UpperCAmelCase__      :	Any						):
              return self.tokenizer(
                  data["""sentence1"""]			,			data["""sentence2"""]			,			max_length=UpperCAmelCase__			,			truncation=UpperCAmelCase__			,			padding=UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Dict					       =				raw_datasets.map(UpperCAmelCase__			,			batched=UpperCAmelCase__						)
        tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				Trainer(self.model			,			UpperCAmelCase__			,			tokenizer=self.tokenizer						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				trainer.predict(UpperCAmelCase__						)
        return {"scores": list(predictions.predictions.squeeze(-1						)						)}




