





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Union[str, Any]=0.999    ,      UpperCamelCase__     :    List[str]="cosine"    ,      )     ->	Optional[int]:



					'''simple docstring'''




					if alpha_transform_type == "cosine":

										def alpha_bar_fn(UpperCamelCase__     :    int  ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(UpperCamelCase__     :    Optional[Any]  ):
															return math.exp(t * -12.0  )

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

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




class      UpperCamelCase_						(					__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE   ):
					lowerCAmelCase_							=		[e.name for e in KarrasDiffusionSchedulers]
					lowerCAmelCase_							=		2




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 1000			,  lowerCAmelCase_ = 0.0_00_85			,  lowerCAmelCase_ = 0.0_12			,  lowerCAmelCase_ = "linear"			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = "epsilon"			,  lowerCAmelCase_ = "linspace"			,  lowerCAmelCase_ = 0			,  )						->							Optional[Any]:
										if trained_betas is not None:
															_snake_case													=			torch.tensor(_a			,  dtype=torch.floataa					)
										elif beta_schedule == "linear":
															_snake_case													=			torch.linspace(_a			,  _a			,  _a			,  dtype=torch.floataa					)
										elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															_snake_case													=			(
															    torch.linspace(beta_start**0.5			,  beta_end**0.5			,  _a			,  dtype=torch.floataa					) ** 2
															)
										elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															_snake_case													=			betas_for_alpha_bar(_a					)
										else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

										_snake_case													=			1.0 - self.betas
										_snake_case													=			torch.cumprod(self.alphas			,  dim=0					)

										#  set all values
										self.set_timesteps(_a			,  _a			,  _a					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							Tuple:
										if schedule_timesteps is None:
															_snake_case													=			self.timesteps

										_snake_case													=			(schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter					) == 0:
															_snake_case													=			1 if len(_a					) > 1 else 0
										else:
															_snake_case													=			timestep.cpu().item() if torch.is_tensor(_a					) else timestep
															_snake_case													=			self._index_counter[timestep_int]

										return indices[pos].item()




					@property
					def 		lowerCAmelCase     (						self					)						->							Dict:
										# 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 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  )						->							str:
										_snake_case													=			self.index_for_timestep(_a					)

										if self.state_in_first_order:
															_snake_case													=			self.sigmas[step_index]
										else:
															_snake_case													=			self.sigmas_interpol[step_index]

										_snake_case													=			sample / ((sigma**2 + 1) ** 0.5)
										return sample




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  )						->							Optional[Any]:
										_snake_case													=			num_inference_steps

										_snake_case													=			num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
															_snake_case													=			np.linspace(0			,  num_train_timesteps - 1			,  _a			,  dtype=_a					)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
															_snake_case													=			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
															_snake_case													=			(np.arange(0			,  _a					) * step_ratio).round()[::-1].copy().astype(_a					)
															timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
															_snake_case													=			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
															_snake_case													=			(np.arange(_a			,  0			,  -step_ratio					)).round().copy().astype(_a					)
															timesteps -= 1
										else:
															raise ValueError(
															    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

										_snake_case													=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
										_snake_case													=			torch.from_numpy(np.log(_a					)					).to(_a					)

										_snake_case													=			np.interp(_a			,  np.arange(0			,  len(_a					)					)			,  _a					)
										_snake_case													=			np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
										_snake_case													=			torch.from_numpy(_a					).to(device=_a					)

										# interpolate sigmas
										_snake_case													=			sigmas.log().lerp(sigmas.roll(1					).log()			,  0.5					).exp()

										_snake_case													=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
										_snake_case													=			torch.cat(
										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)

										if str(_a					).startswith('mps'					):
															# mps does not support float64
															_snake_case													=			torch.from_numpy(_a					).to(_a			,  dtype=torch.floataa					)
										else:
															_snake_case													=			torch.from_numpy(_a					).to(_a					)

										# interpolate timesteps
										_snake_case													=			self.sigma_to_t(_a					).to(_a			,  dtype=timesteps.dtype					)
										_snake_case													=			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])			,  dim=-1					).flatten()

										_snake_case													=			torch.cat([timesteps[:1], interleaved_timesteps]					)

										_snake_case													=			None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										_snake_case													=			defaultdict(_a					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							List[str]:
										# get log sigma
										_snake_case													=			sigma.log()

										# get distribution
										_snake_case													=			log_sigma - self.log_sigmas[:, None]

										# get sigmas range
										_snake_case													=			dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
										_snake_case													=			low_idx + 1

										_snake_case													=			self.log_sigmas[low_idx]
										_snake_case													=			self.log_sigmas[high_idx]

										# interpolate sigmas
										_snake_case													=			(low - log_sigma) / (low - high)
										_snake_case													=			w.clamp(0			,  1					)

										# transform interpolation to time range
										_snake_case													=			(1 - w) * low_idx + w * high_idx
										_snake_case													=			t.view(sigma.shape					)
										return t




					@property
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										return self.sample is None




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = True			,  )						->							str:
										_snake_case													=			self.index_for_timestep(_a					)

										# advance index counter by 1
										_snake_case													=			timestep.cpu().item() if torch.is_tensor(_a					) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
															_snake_case													=			self.sigmas[step_index]
															_snake_case													=			self.sigmas_interpol[step_index + 1]
															_snake_case													=			self.sigmas[step_index + 1]
										else:
															# 2nd order / KDPM2's method
															_snake_case													=			self.sigmas[step_index - 1]
															_snake_case													=			self.sigmas_interpol[step_index]
															_snake_case													=			self.sigmas[step_index]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										_snake_case													=			0
										_snake_case													=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															_snake_case													=			sigma_hat if self.state_in_first_order else sigma_interpol
															_snake_case													=			sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
															_snake_case													=			sigma_hat if self.state_in_first_order else sigma_interpol
															_snake_case													=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
															    sample / (sigma_input**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`'''					)

										if self.state_in_first_order:
															# 2. Convert to an ODE derivative for 1st order
															_snake_case													=			(sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															_snake_case													=			sigma_interpol - sigma_hat

															# store for 2nd order step
															_snake_case													=			sample
										else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															_snake_case													=			(sample - pred_original_sample) / sigma_interpol

															# 3. delta timestep
															_snake_case													=			sigma_next - sigma_hat

															_snake_case													=			self.sample
															_snake_case													=			None

										_snake_case													=			sample + derivative * dt

										if not return_dict:
															return (prev_sample,)

										return SchedulerOutput(prev_sample=_a					)




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

										_snake_case													=			[self.index_for_timestep(_a			,  _a					) for t in timesteps]

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

										_snake_case													=			original_samples + noise * sigma
										return noisy_samples






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










from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


UpperCAmelCase_ 							=       """\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

UpperCAmelCase_ 							=       """\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

UpperCAmelCase_ 							=       """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

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): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,					_KWARGS_DESCRIPTION   )
class      UpperCamelCase_						(					datasets.Metric   ):




					def 		lowerCAmelCase     (						self					)						->							MetricInfo:
										return datasets.MetricInfo(
										    description=_DESCRIPTION			,  citation=_CITATION			,  inputs_description=_KWARGS_DESCRIPTION			,  features=datasets.Features(
										        {
										            'predictions': datasets.Sequence(datasets.Value('string'			,  id='token'					)			,  id='sequence'					),
										            'references': datasets.Sequence(
										                datasets.Sequence(datasets.Value('string'			,  id='token'					)			,  id='sequence'					)			,  id='references'					),
										        }					)			,  )






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 1			,  lowerCAmelCase_ = 4			,  )						->							Dict[str, float]:
										return {
										    "google_bleu": gleu_score.corpus_gleu(
										        list_of_references=lowerCAmelCase_			,  hypotheses=lowerCAmelCase_			,  min_len=lowerCAmelCase_			,  max_len=lowerCAmelCase_					)
										}



