






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      UpperCamelCase_							(				__UpperCAmelCase       ):
						lowercase									=  42
						lowercase									=  None






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase=0.9_9_9    ,	UpperCAmelCase="cosine"    ,	)     ->		Union[str, Any]:







							"""simple docstring"""





							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCAmelCase     ):
																					return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2     ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(UpperCAmelCase     ):
																					return math.exp(t * -1_2.0     )

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

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







class      UpperCamelCase_							(				__UpperCAmelCase ,       __UpperCAmelCase       ):
						lowercase									=  [e.name for e in KarrasDiffusionSchedulers]
						lowercase									=  1




						@register_to_config
						def __init__(    self		,	lowercase = 1_000		,	lowercase = 0.0001		,	lowercase = 0.02		,	lowercase = "linear"		,	lowercase = None		,	lowercase = "epsilon"		,	lowercase = "linspace"		,	lowercase = 0		,	)			->						List[Any]:
													if trained_betas is not None:
																				_a  :    Union[str, Any]			=						torch.tensor(UpperCAmelCase_		,	dtype=torch.floataa       )
													elif beta_schedule == "linear":
																				_a  :    int			=						torch.linspace(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	dtype=torch.floataa       )
													elif beta_schedule == "scaled_linear":
																				# this schedule is very specific to the latent diffusion model.
																				_a  :    Tuple			=						(
																				    torch.linspace(beta_start**0.5		,	beta_end**0.5		,	UpperCAmelCase_		,	dtype=torch.floataa       ) ** 2
																				)
													elif beta_schedule == "squaredcos_cap_v2":
																				# Glide cosine schedule
																				_a  :    Tuple			=						betas_for_alpha_bar(UpperCAmelCase_       )
													else:
																				raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'       )

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

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

													# setable values
													_a  :    Dict			=						None
													_a  :    str			=						np.linspace(0		,	num_train_timesteps - 1		,	UpperCAmelCase_		,	dtype=UpperCAmelCase_       )[::-1].copy()
													_a  :    Any			=						torch.from_numpy(UpperCAmelCase_       )
													_a  :    Optional[int]			=						False




						@property
						def      snake_case__(    self       )			->						List[Any]:
													# standard deviation of the initial noise distribution
													if self.config.timestep_spacing in ["linspace", "trailing"]:
																				return self.sigmas.max()

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




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						str:
													if isinstance(UpperCAmelCase_		,	torch.Tensor       ):
																				_a  :    int			=						timestep.to(self.timesteps.device       )
													_a  :    Optional[int]			=						(self.timesteps == timestep).nonzero().item()
													_a  :    Any			=						self.sigmas[step_index]
													_a  :    Dict			=						sample / ((sigma**2 + 1) ** 0.5)
													_a  :    Optional[Any]			=						True
													return sample




						def      snake_case__(    self		,	lowercase		,	lowercase = None       )			->						str:
													_a  :    List[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":
																				_a  :    Tuple			=						np.linspace(0		,	self.config.num_train_timesteps - 1		,	UpperCAmelCase_		,	dtype=UpperCAmelCase_       )[
																				    ::-1
																				].copy()
													elif self.config.timestep_spacing == "leading":
																				_a  :    Optional[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
																				_a  :    List[Any]			=						(np.arange(0		,	UpperCAmelCase_       ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_       )
																				timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																				_a  :    List[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
																				_a  :    Union[str, Any]			=						(np.arange(self.config.num_train_timesteps		,	0		,	-step_ratio       )).round().copy().astype(UpperCAmelCase_       )
																				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  :    Optional[Any]			=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
													_a  :    int			=						np.interp(UpperCAmelCase_		,	np.arange(0		,	len(UpperCAmelCase_       )       )		,	UpperCAmelCase_       )
													_a  :    List[str]			=						np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
													_a  :    Dict			=						torch.from_numpy(UpperCAmelCase_       ).to(device=UpperCAmelCase_       )
													if str(UpperCAmelCase_       ).startswith('''mps'''       ):
																				# mps does not support float64
																				_a  :    Any			=						torch.from_numpy(UpperCAmelCase_       ).to(UpperCAmelCase_		,	dtype=torch.floataa       )
													else:
																				_a  :    str			=						torch.from_numpy(UpperCAmelCase_       ).to(device=UpperCAmelCase_       )




						def      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase = None		,	lowercase = True		,	)			->						int:

													if (
													    isinstance(UpperCAmelCase_		,	UpperCAmelCase_       )
													    or isinstance(UpperCAmelCase_		,	torch.IntTensor       )
													    or isinstance(UpperCAmelCase_		,	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(UpperCAmelCase_		,	torch.Tensor       ):
																				_a  :    List[str]			=						timestep.to(self.timesteps.device       )

													_a  :    Any			=						(self.timesteps == timestep).nonzero().item()
													_a  :    Dict			=						self.sigmas[step_index]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																				_a  :    List[str]			=						sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																				# * c_out + input * c_skip
																				_a  :    int			=						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`'       )

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

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

													_a  :    Dict			=						sigma_down - sigma

													_a  :    Optional[Any]			=						sample + derivative * dt

													_a  :    List[str]			=						model_output.device
													_a  :    Tuple			=						randn_tensor(model_output.shape		,	dtype=model_output.dtype		,	device=UpperCAmelCase_		,	generator=UpperCAmelCase_       )

													_a  :    Tuple			=						prev_sample + noise * sigma_up

													if not return_dict:
																				return (prev_sample,)

													return EulerAncestralDiscreteSchedulerOutput(
													    prev_sample=UpperCAmelCase_		,	pred_original_sample=UpperCAmelCase_       )




						def      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase		,	)			->						Tuple:
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													_a  :    List[str]			=						self.sigmas.to(device=original_samples.device		,	dtype=original_samples.dtype       )
													if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_       ):
																				# mps does not support float64
																				_a  :    Union[str, Any]			=						self.timesteps.to(original_samples.device		,	dtype=torch.floataa       )
																				_a  :    List[str]			=						timesteps.to(original_samples.device		,	dtype=torch.floataa       )
													else:
																				_a  :    Union[str, Any]			=						self.timesteps.to(original_samples.device       )
																				_a  :    Any			=						timesteps.to(original_samples.device       )

													_a  :    Optional[Any]			=						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

													_a  :    Optional[int]			=						original_samples + noise * sigma
													return noisy_samples







						def __len__(    self       )			->						str:
													return self.config.num_train_timesteps





import sacrebleu as scb
from packaging import version

import datasets


UpperCAmelCase_    ="""\
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

UpperCAmelCase_    ="""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

UpperCAmelCase_    ="""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


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





				'''simple docstring'''



				def    __snake_case		(			self    ):
											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"`.'''    )
											return datasets.MetricInfo(
											    description=_DESCRIPTION							,			citation=_CITATION							,			homepage='''https://github.com/mjpost/sacreBLEU'''							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
											        {
											            '''predictions''': datasets.Value('''string'''							,			id='''sequence'''    ),
											            '''references''': datasets.Sequence(datasets.Value('''string'''							,			id='''sequence'''    )							,			id='''references'''    ),
											        }    )							,			codebase_urls=['''https://github.com/mjpost/sacreBLEU''']							,			reference_urls=[
											        '''https://github.com/mjpost/sacreBLEU''',
											        '''https://en.wikipedia.org/wiki/BLEU''',
											        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
											    ]							,			)






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_="exp"							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			UpperCAmelCase_=False							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			):
											lowerCAmelCase =		len(references[0]    )
											if any(len(UpperCAmelCase_    ) != references_per_prediction for refs in references    ):
																		raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
											lowerCAmelCase =		[[refs[i] for refs in references] for i in range(UpperCAmelCase_    )]
											lowerCAmelCase =		scb.corpus_bleu(
											    UpperCAmelCase_							,			UpperCAmelCase_							,			smooth_method=UpperCAmelCase_							,			smooth_value=UpperCAmelCase_							,			force=UpperCAmelCase_							,			lowercase=UpperCAmelCase_							,			use_effective_order=UpperCAmelCase_							,			**({'''tokenize''': tokenize} if tokenize else {})							,			)
											lowerCAmelCase =		{
											    '''score''': output.score,
											    '''counts''': output.counts,
											    '''totals''': output.totals,
											    '''precisions''': output.precisions,
											    '''bp''': output.bp,
											    '''sys_len''': output.sys_len,
											    '''ref_len''': output.ref_len,
											}
											return output_dict



