"""simple docstring"""




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__(      lowercase_			):
						lowerCAmelCase__	:    Any    				=		42
						lowerCAmelCase__	:    Tuple    				=		None






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__=0.9_9_9							,     UpperCamelCase__="cosine"							,     ):






							"""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__						  =				[]
							for i in range(snake_case_					):
														A__						  =				i / num_diffusion_timesteps
														A__						  =				(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__(      lowercase_		,    lowercase_			):
						lowerCAmelCase__	:    str    				=		[e.name for e in KarrasDiffusionSchedulers]
						lowerCAmelCase__	:    Optional[Any]    				=		1





						@register_to_config
						def __init__(							self		,__UpperCAmelCase = 10_00		,__UpperCAmelCase = 0.0_0_0_1		,__UpperCAmelCase = 0.0_2		,__UpperCAmelCase = "linear"		,__UpperCAmelCase = None		,__UpperCAmelCase = "epsilon"		,__UpperCAmelCase = "linspace"		,__UpperCAmelCase = 0		,)       ->					int:




													if trained_betas is not None:
																				A__						  =				torch.tensor(UpperCAmelCase_		,dtype=torch.floataa  )
													elif beta_schedule == "linear":
																				A__						  =				torch.linspace(UpperCAmelCase_		,UpperCAmelCase_		,UpperCAmelCase_		,dtype=torch.floataa  )
													elif beta_schedule == "scaled_linear":
																				# this schedule is very specific to the latent diffusion model.
																				A__						  =				(
																				    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__						  =				betas_for_alpha_bar(UpperCAmelCase_  )
													else:
																				raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''  )

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

													A__						  =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
													A__						  =				np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
													A__						  =				torch.from_numpy(UpperCAmelCase_  )

													# setable values
													A__						  =				None
													A__						  =				np.linspace(0		,num_train_timesteps - 1		,UpperCAmelCase_		,dtype=UpperCAmelCase_  )[::-1].copy()
													A__						  =				torch.from_numpy(UpperCAmelCase_  )
													A__						  =				False





						@property
						def 					snake_case__						(							self  )       ->					Dict:




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

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





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					torch.FloatTensor:




													if isinstance(UpperCAmelCase_		,torch.Tensor  ):
																				A__						  =				timestep.to(self.timesteps.device  )
													A__						  =				(self.timesteps == timestep).nonzero().item()
													A__						  =				self.sigmas[step_index]
													A__						  =				sample / ((sigma**2 + 1) ** 0.5)
													A__						  =				True
													return sample





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





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = True		,)       ->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:




													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__						  =				timestep.to(self.timesteps.device  )

													A__						  =				(self.timesteps == timestep).nonzero().item()
													A__						  =				self.sigmas[step_index]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																				A__						  =				sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																				# * c_out + input * c_skip
																				A__						  =				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__						  =				self.sigmas[step_index]
													A__						  =				self.sigmas[step_index + 1]
													A__						  =				(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
													A__						  =				(sigma_to**2 - sigma_up**2) ** 0.5

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

													A__						  =				sigma_down - sigma

													A__						  =				sample + derivative * dt

													A__						  =				model_output.device
													A__						  =				randn_tensor(model_output.shape		,dtype=model_output.dtype		,device=UpperCAmelCase_		,generator=UpperCAmelCase_  )

													A__						  =				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		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,)       ->					torch.FloatTensor:
													A__						  =				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__						  =				self.timesteps.to(original_samples.device		,dtype=torch.floataa  )
																				A__						  =				timesteps.to(original_samples.device		,dtype=torch.floataa  )
													else:
																				A__						  =				self.timesteps.to(original_samples.device  )
																				A__						  =				timesteps.to(original_samples.device  )

													A__						  =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

													A__						  =				original_samples + noise * sigma
													return noisy_samples





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













"""simple docstring"""





import argparse
import json
import re
import string
import sys
from collections import Counter






def 							A(    snake_case_						):
       """simple docstring"""





       def remove_articles(snake_case_						):
              return re.sub(r"\b(a|an|the)\b"		,  " "		,  snake_case_						)

       def white_space_fix(snake_case_						):
              return " ".join(text.split()						)

       def remove_punc(snake_case_						):
              lowercase__:							str         =     set(string.punctuation						)
              return "".join(ch for ch in text if ch not in exclude						)

       def lower(snake_case_						):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(snake_case_						)						)						)						)






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Dict         =     normalize_answer(snake_case_						).split()
       lowercase__:							List[str]         =     normalize_answer(snake_case_						).split()
       lowercase__:							Optional[int]         =     Counter(snake_case_						) & Counter(snake_case_						)
       lowercase__:							Any         =     sum(common.values()						)
       if num_same == 0:
              return 0
       lowercase__:							Any         =     1.0 * num_same / len(snake_case_						)
       lowercase__:							List[Any]         =     1.0 * num_same / len(snake_case_						)
       lowercase__:							int         =     (2 * precision * recall) / (precision + recall)
       return fa






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       return normalize_answer(snake_case_						) == normalize_answer(snake_case_						)






def 							A(    snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							int         =     []
       for ground_truth in ground_truths:
              lowercase__:							str         =     metric_fn(snake_case_		,  snake_case_						)
              scores_for_ground_truths.append(snake_case_						)
       return max(snake_case_						)






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							Optional[int]         =     0
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   lowercase__:							Union[str, Any]         =     "Unanswered question " + qa["id"] + " will receive score 0."
                                   print(snake_case_		,  file=sys.stderr						)
                                   continue
                            lowercase__:							List[Any]         =     [x["text"] for x in qa["answers"]]
                            lowercase__:							Optional[Any]         =     predictions[qa["id"]]
                            exact_match += metric_max_over_ground_truths(snake_case_		,  snake_case_		,  snake_case_						)
                            fa += metric_max_over_ground_truths(snake_case_		,  snake_case_		,  snake_case_						)

       lowercase__:							Union[str, Any]         =     1_00.0 * exact_match / total
       lowercase__:							str         =     1_00.0 * fa / total

       return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
  UpperCamelCase					=     """1.1"""
  UpperCamelCase					=     argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
  parser.add_argument("""dataset_file""", help="""Dataset file""")
  parser.add_argument("""prediction_file""", help="""Prediction File""")
  UpperCamelCase					=     parser.parse_args()
  with open(args.dataset_file) as dataset_file:
    UpperCamelCase					=     json.load(dataset_file)
    if dataset_json["version"] != expected_version:
      print(
          """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
          file=sys.stderr,
      )
    UpperCamelCase					=     dataset_json["""data"""]
  with open(args.prediction_file) as prediction_file:
    UpperCamelCase					=     json.load(prediction_file)
  print(json.dumps(evaluate(dataset, predictions)))






