

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

import numpy as np
import torch

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 							A_ (		_a					):
     _lowercase			:     Optional[Any]								=	4_2
     _lowercase			:     Tuple								=	None







def _a (       SCREAMING_SNAKE_CASE		:			List[Any]    ,					SCREAMING_SNAKE_CASE		:			Tuple=0.9_9_9    ,					SCREAMING_SNAKE_CASE		:			Tuple="cosine"    ,					)    ->    Tuple:

 """simple docstring"""






 if alpha_transform_type == "cosine":

  def alpha_bar_fn(SCREAMING_SNAKE_CASE		:			List[Any]		):
   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(SCREAMING_SNAKE_CASE		:			Optional[Any]		):
   return math.exp(t * -1_2.0		)

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

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






class 							A_ (		_a							,	_a					):


     @register_to_config
     def __init__(							self			:		List[str]			,  UpperCAmelCase			:		int = 1_0_0_0			,  UpperCAmelCase			:		str = "fixed_small_log"			,  UpperCAmelCase			:		bool = True			,  UpperCAmelCase			:		Optional[float] = 1.0			,  UpperCAmelCase			:		str = "epsilon"			,  UpperCAmelCase			:		str = "squaredcos_cap_v2"			,  )							->	int:







      if beta_schedule != "squaredcos_cap_v2":
       raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''					)

      __lowerCAmelCase:   Dict   	=      betas_for_alpha_bar(UpperCamelCase__					)

      __lowerCAmelCase:   int   	=      1.0 - self.betas
      __lowerCAmelCase:   Dict   	=      torch.cumprod(self.alphas			,  dim=0					)
      __lowerCAmelCase:   List[Any]   	=      torch.tensor(1.0					)

      # standard deviation of the initial noise distribution
      __lowerCAmelCase:   List[str]   	=      1.0

      # setable values
      __lowerCAmelCase:   Any   	=      None
      __lowerCAmelCase:   int   	=      torch.from_numpy(np.arange(0			,  UpperCamelCase__					)[::-1].copy()					)

      __lowerCAmelCase:   Dict   	=      variance_type


     def   UpperCAmelCase  (							self			:		Dict			,  UpperCAmelCase			:		torch.FloatTensor			,  UpperCAmelCase			:		Optional[int] = None					)							->	Tuple:
      return sample


     def   UpperCAmelCase  (							self			:		List[str]			,  UpperCAmelCase			:		int			,  UpperCAmelCase			:		Union[str, torch.device] = None					)							->	int:
      __lowerCAmelCase:   Optional[Any]   	=      num_inference_steps
      __lowerCAmelCase:   str   	=      (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      __lowerCAmelCase:   int   	=      (np.arange(0			,  UpperCamelCase__					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
      __lowerCAmelCase:   Tuple   	=      torch.from_numpy(UpperCamelCase__					).to(UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		Dict			,  UpperCAmelCase			:		Union[str, Any]			,  UpperCAmelCase			:		Union[str, Any]=None			,  UpperCAmelCase			:		Optional[int]=None			,  UpperCAmelCase			:		Tuple=None					)							->	List[Any]:







      if prev_timestep is None:
       __lowerCAmelCase:   Union[str, Any]   	=      t - 1

      __lowerCAmelCase:   str   	=      self.alphas_cumprod[t]
      __lowerCAmelCase:   List[str]   	=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      __lowerCAmelCase:   Any   	=      1 - alpha_prod_t
      __lowerCAmelCase:   List[Any]   	=      1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
       __lowerCAmelCase:   Union[str, Any]   	=      self.betas[t]
      else:
       __lowerCAmelCase:   str   	=      1 - alpha_prod_t / alpha_prod_t_prev

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      __lowerCAmelCase:   Optional[int]   	=      beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
       __lowerCAmelCase:   Union[str, Any]   	=      self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
       __lowerCAmelCase:   str   	=      torch.log(torch.clamp(UpperCamelCase__			,  min=1E-20					)					)
       __lowerCAmelCase:   Optional[Any]   	=      torch.exp(0.5 * variance					)
      elif variance_type == "learned_range":
       # NOTE difference with DDPM scheduler
       __lowerCAmelCase:   List[Any]   	=      variance.log()
       __lowerCAmelCase:   List[str]   	=      beta.log()

       __lowerCAmelCase:   List[str]   	=      (predicted_variance + 1) / 2
       __lowerCAmelCase:   Union[str, Any]   	=      frac * max_log + (1 - frac) * min_log

      return variance


     def   UpperCAmelCase  (							self			:		int			,  UpperCAmelCase			:		torch.FloatTensor			,  UpperCAmelCase			:		int			,  UpperCAmelCase			:		torch.FloatTensor			,  UpperCAmelCase			:		Optional[int] = None			,  UpperCAmelCase			:		str=None			,  UpperCAmelCase			:		bool = True			,  )							->	List[str]:
      __lowerCAmelCase:   List[Any]   	=      timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
       __lowerCAmelCase	,							__lowerCAmelCase:   List[Any]   	=      torch.split(UpperCamelCase__			,  sample.shape[1]			,  dim=1					)
      else:
       __lowerCAmelCase:   List[Any]   	=      None

      # 1. compute alphas, betas
      if prev_timestep is None:
       __lowerCAmelCase:   Optional[Any]   	=      t - 1

      __lowerCAmelCase:   List[str]   	=      self.alphas_cumprod[t]
      __lowerCAmelCase:   Any   	=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      __lowerCAmelCase:   Union[str, Any]   	=      1 - alpha_prod_t
      __lowerCAmelCase:   Optional[Any]   	=      1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
       __lowerCAmelCase:   Any   	=      self.betas[t]
       __lowerCAmelCase:   List[str]   	=      self.alphas[t]
      else:
       __lowerCAmelCase:   Any   	=      1 - alpha_prod_t / alpha_prod_t_prev
       __lowerCAmelCase:   str   	=      1 - beta

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
       __lowerCAmelCase:   Tuple   	=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
       __lowerCAmelCase:   int   	=      model_output
      else:
       raise ValueError(
           F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
           ' for the UnCLIPScheduler.'					)

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
       __lowerCAmelCase:   Union[str, Any]   	=      torch.clamp(
           UpperCamelCase__			,  -self.config.clip_sample_range			,  self.config.clip_sample_range					)

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      __lowerCAmelCase:   List[str]   	=      (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      __lowerCAmelCase:   Tuple   	=      alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      __lowerCAmelCase:   Optional[Any]   	=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      __lowerCAmelCase:   List[str]   	=      0
      if t > 0:
       __lowerCAmelCase:   Optional[Any]   	=      randn_tensor(
           model_output.shape			,  dtype=model_output.dtype			,  generator=UpperCamelCase__			,  device=model_output.device					)

       __lowerCAmelCase:   Optional[Any]   	=      self._get_variance(
           UpperCamelCase__			,  predicted_variance=UpperCamelCase__			,  prev_timestep=UpperCamelCase__			,  )

       if self.variance_type == "fixed_small_log":
        __lowerCAmelCase:   Any   	=      variance
       elif self.variance_type == "learned_range":
        __lowerCAmelCase:   Optional[Any]   	=      (0.5 * variance).exp()
       else:
        raise ValueError(
            F'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
            ' for the UnCLIPScheduler.'					)

       __lowerCAmelCase:   int   	=      variance * variance_noise

      __lowerCAmelCase:   str   	=      pred_prev_sample + variance

      if not return_dict:
       return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__			,  pred_original_sample=UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		int			,  UpperCAmelCase			:		torch.FloatTensor			,  UpperCAmelCase			:		torch.FloatTensor			,  UpperCAmelCase			:		torch.IntTensor			,  )							->	Optional[int]:
      __lowerCAmelCase:   Union[str, Any]   	=      self.alphas_cumprod.to(device=original_samples.device			,  dtype=original_samples.dtype					)
      __lowerCAmelCase:   List[str]   	=      timesteps.to(original_samples.device					)

      __lowerCAmelCase:   Tuple   	=      alphas_cumprod[timesteps] ** 0.5
      __lowerCAmelCase:   Any   	=      sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape					) < len(original_samples.shape					):
       __lowerCAmelCase:   str   	=      sqrt_alpha_prod.unsqueeze(-1					)

      __lowerCAmelCase:   Any   	=      (1 - alphas_cumprod[timesteps]) ** 0.5
      __lowerCAmelCase:   Optional[Any]   	=      sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape					) < len(original_samples.shape					):
       __lowerCAmelCase:   int   	=      sqrt_one_minus_alpha_prod.unsqueeze(-1					)

      __lowerCAmelCase:   Optional[Any]   	=      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples










'''simple docstring'''


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCamelCase	:       List[str]      	=	"\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

_lowerCamelCase	:       Optional[int]      	=	"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

_lowerCamelCase	:       str      	=	"\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"

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






    """simple docstring"""





    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
               {
                   'predictions': datasets.Value('string'  ,						id='sequence' ),
                   'references': datasets.Value('string'  ,						id='sequence' ),
               } )  ,						codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']  ,						reference_urls=[
               'https://en.wikipedia.org/wiki/ROUGE_(metric)',
               'https://github.com/google-research/google-research/tree/master/rouge',
           ]  ,						)






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=False ):


       """simple docstring"""







       if rouge_types is None:
          UpperCamelCase					=  ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

       UpperCamelCase					=  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase__  ,						use_stemmer=UpperCamelCase__ )
       if use_aggregator:
          UpperCamelCase					=  scoring.BootstrapAggregator()
       else:
          UpperCamelCase					=  []

       for ref, pred in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
          UpperCamelCase					=  scorer.score(UpperCamelCase__  ,						UpperCamelCase__ )
          if use_aggregator:
             aggregator.add_scores(UpperCamelCase__ )
          else:
             scores.append(UpperCamelCase__ )

       if use_aggregator:
          UpperCamelCase					=  aggregator.aggregate()
       else:
          UpperCamelCase					=  {}
          for key in scores[0]:
             UpperCamelCase					=  [score[key] for score in scores]

       return result

