


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

import numpy as np
import torch

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


lowerCamelCase						 =	logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class A  (       UpperCamelCase_					):
      UpperCamelCase__							:  torch.FloatTensor
      UpperCamelCase__							:  Optional[torch.FloatTensor]				=None




def      a_				(   SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Dict=0.9_99		,      SCREAMING_SNAKE_CASE__ :				Optional[Any]="cosine"		,      ):
       '''simple docstring'''





       if alpha_transform_type == "cosine":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__ :				Dict					):
                     return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(SCREAMING_SNAKE_CASE__ :				Tuple					):
                     return math.exp(t * -12.0					)

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

       _lowerCamelCase	:		Optional[int]          =[]
       for i in range(SCREAMING_SNAKE_CASE__					):
              _lowerCamelCase	:		Tuple          =i / num_diffusion_timesteps
              _lowerCamelCase	:		Optional[Any]          =(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__					) / alpha_bar_fn(SCREAMING_SNAKE_CASE__					)		,      SCREAMING_SNAKE_CASE__					)					)
       return torch.tensor(SCREAMING_SNAKE_CASE__		,      dtype=torch.floataa					)




class A  (       UpperCamelCase_   ,							UpperCamelCase_					):
      UpperCamelCase__							:  List[Any]				=[e.name for e in KarrasDiffusionSchedulers]
      UpperCamelCase__							:  Dict				=1






      @register_to_config
      def __init__( self						:					str      ,       lowercase_						:					int = 1000      ,       lowercase_						:					float = 0.0001      ,       lowercase_						:					float = 0.02      ,       lowercase_						:					str = "linear"      ,       lowercase_						:					Optional[Union[np.ndarray, List[float]]] = None      ,       lowercase_						:					str = "epsilon"      ,       lowercase_						:					str = "linspace"      ,       lowercase_						:					int = 0      ,       ) ->						List[Any]:
             """simple docstring"""




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

             _lowerCamelCase	:		List[Any]          =1.0 - self.betas
             _lowerCamelCase	:		List[str]          =torch.cumprod(self.alphas      ,       dim=0			)

             _lowerCamelCase	:		str          =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
             _lowerCamelCase	:		Optional[int]          =np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
             _lowerCamelCase	:		Optional[Any]          =torch.from_numpy(lowercase_			)

             # setable values
             _lowerCamelCase	:		Optional[int]          =None
             _lowerCamelCase	:		int          =np.linspace(0      ,       num_train_timesteps - 1      ,       lowercase_      ,       dtype=lowercase_			)[::-1].copy()
             _lowerCamelCase	:		Optional[Any]          =torch.from_numpy(lowercase_			)
             _lowerCamelCase	:		Tuple          =False






      @property
      def 	lowerCamelCase	( self						:					int			) ->						List[str]:
             """simple docstring"""




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

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






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					Union[float, torch.FloatTensor]			) ->						torch.FloatTensor:
             """simple docstring"""




             if isinstance(lowercase_      ,       torch.Tensor			):
                    _lowerCamelCase	:		Union[str, Any]          =timestep.to(self.timesteps.device			)
             _lowerCamelCase	:		Optional[int]          =(self.timesteps == timestep).nonzero().item()
             _lowerCamelCase	:		List[Any]          =self.sigmas[step_index]
             _lowerCamelCase	:		Dict          =sample / ((sigma**2 + 1) ** 0.5)
             _lowerCamelCase	:		Optional[Any]          =True
             return sample






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					int      ,       lowercase_						:					Union[str, torch.device] = None			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =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":
                    _lowerCamelCase	:		Tuple          =np.linspace(0      ,       self.config.num_train_timesteps - 1      ,       lowercase_      ,       dtype=lowercase_			)[
                        ::-1
                    ].copy()
             elif self.config.timestep_spacing == "leading":
                    _lowerCamelCase	:		int          =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
                    _lowerCamelCase	:		List[str]          =(np.arange(0      ,       lowercase_			) * step_ratio).round()[::-1].copy().astype(lowercase_			)
                    timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                    _lowerCamelCase	:		Union[str, Any]          =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
                    _lowerCamelCase	:		List[Any]          =(np.arange(self.config.num_train_timesteps      ,       0      ,       -step_ratio			)).round().copy().astype(lowercase_			)
                    timesteps -= 1
             else:
                    raise ValueError(
                        F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

             _lowerCamelCase	:		List[str]          =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
             _lowerCamelCase	:		int          =np.interp(lowercase_      ,       np.arange(0      ,       len(lowercase_			)			)      ,       lowercase_			)
             _lowerCamelCase	:		Optional[int]          =np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
             _lowerCamelCase	:		Dict          =torch.from_numpy(lowercase_			).to(device=lowercase_			)
             if str(lowercase_			).startswith('mps'			):
                    # mps does not support float64
                    _lowerCamelCase	:		Union[str, Any]          =torch.from_numpy(lowercase_			).to(lowercase_      ,       dtype=torch.floataa			)
             else:
                    _lowerCamelCase	:		Any          =torch.from_numpy(lowercase_			).to(device=lowercase_			)






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					Union[float, torch.FloatTensor]      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					Optional[torch.Generator] = None      ,       lowercase_						:					bool = True      ,       ) ->						Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:
             """simple docstring"""




             if (
                 isinstance(lowercase_      ,       lowercase_			)
                 or isinstance(lowercase_      ,       torch.IntTensor			)
                 or isinstance(lowercase_      ,       torch.LongTensor			)
             ):
                    raise ValueError(
                        (
                            'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                            ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                            ' one of the `scheduler.timesteps` as a timestep.'
                        )      ,       )

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

             if isinstance(lowercase_      ,       torch.Tensor			):
                    _lowerCamelCase	:		Tuple          =timestep.to(self.timesteps.device			)

             _lowerCamelCase	:		Optional[Any]          =(self.timesteps == timestep).nonzero().item()
             _lowerCamelCase	:		Optional[int]          =self.sigmas[step_index]

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                    _lowerCamelCase	:		Any          =sample - sigma * model_output
             elif self.config.prediction_type == "v_prediction":
                    # * c_out + input * c_skip
                    _lowerCamelCase	:		List[Any]          =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
             elif self.config.prediction_type == "sample":
                    raise NotImplementedError('prediction_type not implemented yet: sample'			)
             else:
                    raise ValueError(
                        F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

             _lowerCamelCase	:		int          =self.sigmas[step_index]
             _lowerCamelCase	:		Optional[Any]          =self.sigmas[step_index + 1]
             _lowerCamelCase	:		List[Any]          =(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
             _lowerCamelCase	:		Optional[int]          =(sigma_to**2 - sigma_up**2) ** 0.5

             # 2. Convert to an ODE derivative
             _lowerCamelCase	:		Dict          =(sample - pred_original_sample) / sigma

             _lowerCamelCase	:		Optional[int]          =sigma_down - sigma

             _lowerCamelCase	:		List[str]          =sample + derivative * dt

             _lowerCamelCase	:		Any          =model_output.device
             _lowerCamelCase	:		List[Any]          =randn_tensor(model_output.shape      ,       dtype=model_output.dtype      ,       device=lowercase_      ,       generator=lowercase_			)

             _lowerCamelCase	:		List[Any]          =prev_sample + noise * sigma_up

             if not return_dict:
                    return (prev_sample,)

             return EulerAncestralDiscreteSchedulerOutput(
                 prev_sample=lowercase_      ,       pred_original_sample=lowercase_			)






      def 	lowerCamelCase	( self						:					Dict      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					torch.FloatTensor      ,       lowercase_						:					torch.FloatTensor      ,       ) ->						torch.FloatTensor:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =self.sigmas.to(device=original_samples.device      ,       dtype=original_samples.dtype			)
             if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_			):
                    # mps does not support float64
                    _lowerCamelCase	:		str          =self.timesteps.to(original_samples.device      ,       dtype=torch.floataa			)
                    _lowerCamelCase	:		Union[str, Any]          =timesteps.to(original_samples.device      ,       dtype=torch.floataa			)
             else:
                    _lowerCamelCase	:		Any          =self.timesteps.to(original_samples.device			)
                    _lowerCamelCase	:		Tuple          =timesteps.to(original_samples.device			)

             _lowerCamelCase	:		Union[str, Any]          =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

             _lowerCamelCase	:		int          =original_samples + noise * sigma
             return noisy_samples






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




             return self.config.num_train_timesteps






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

import datasets


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

lowerCamelCase						 =	'\\nFrugalScore 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.\n'


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




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






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """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 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




             if self.config_name == "default":
                    _lowerCamelCase	:		Optional[int]          ='moussaKam/frugalscore_tiny_bert-base_bert-score'
             else:
                    _lowerCamelCase	:		List[Any]          =self.config_name
             _lowerCamelCase	:		str          =AutoModelForSequenceClassification.from_pretrained(lowercase_			)
             _lowerCamelCase	:		str          =AutoTokenizer.from_pretrained(lowercase_			)






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]=32      ,       lowercase_						:					int=128      ,       lowercase_						:					Tuple=None      ,       ) ->						str:
             """simple docstring"""




             assert len(lowercase_			) == len(
                 lowercase_			), "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:
                    _lowerCamelCase	:		List[str]          ='gpu' if torch.cuda.is_available() else 'cpu'
             _lowerCamelCase	:		List[str]          =TrainingArguments(
                 'trainer'      ,       fpaa=(device == 'gpu')      ,       per_device_eval_batch_size=lowercase_      ,       report_to='all'      ,       no_cuda=(device == 'cpu')      ,       log_level='warning'      ,       )
             _lowerCamelCase	:		str          ={'sentence1': predictions, 'sentence2': references}
             _lowerCamelCase	:		Tuple          =datasets.Dataset.from_dict(lowercase_			)

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

             _lowerCamelCase	:		Dict          =raw_datasets.map(lowercase_      ,       batched=lowercase_			)
             tokenized_datasets.remove_columns(['sentence1', 'sentence2']			)
             _lowerCamelCase	:		Union[str, Any]          =Trainer(self.model      ,       lowercase_      ,       tokenizer=self.tokenizer			)
             _lowerCamelCase	:		List[str]          =trainer.predict(lowercase_			)
             return {"scores": list(predictions.predictions.squeeze(-1			)			)}


