






"""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


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							None







def 							A(    snake_case_		,  snake_case_=0.9_99		,  snake_case_="cosine"		,  ):
       """simple docstring"""





       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       lowercase__:							Optional[int]         =     []
       for i in range(snake_case_						):
              lowercase__:							Any         =     i / num_diffusion_timesteps
              lowercase__:							Optional[Any]         =     (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 						_a  (		lowercase_							,	lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							[e.name for e in KarrasDiffusionSchedulers]
   UpperCamelCase__     	=							1





   @register_to_config
   def __init__(       self					, UpperCAmelCase_ = 1_000					, UpperCAmelCase_ = 0.00_01					, UpperCAmelCase_ = 0.02					, UpperCAmelCase_ = "linear"					, UpperCAmelCase_ = None					, UpperCAmelCase_ = "epsilon"					, UpperCAmelCase_ = "linspace"					, UpperCAmelCase_ = 0					, )			->   int:




          '''simple docstring'''




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

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

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

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





   @property
   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




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

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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   torch.FloatTensor:




          '''simple docstring'''




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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_ = None)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							int         =     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":
                 lowercase__:							Optional[Any]         =     np.linspace(0					, self.config.num_train_timesteps - 1					, UpperCAmelCase_					, dtype=UpperCAmelCase_)[
                     ::-1
                 ].copy()
          elif self.config.timestep_spacing == "leading":
                 lowercase__:							List[str]         =     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
                 lowercase__:							List[Any]         =     (np.arange(0					, UpperCAmelCase_) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_)
                 timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                 lowercase__:							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
                 lowercase__:							Optional[int]         =     (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'.""")

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





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




          '''simple docstring'''




          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):
                 lowercase__:							Tuple         =     timestep.to(self.timesteps.device)

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

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

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

          # 2. Convert to an ODE derivative
          lowercase__:							List[str]         =     (sample - pred_original_sample) / sigma

          lowercase__:							Union[str, Any]         =     sigma_down - sigma

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

          lowercase__:							Tuple         =     model_output.device
          lowercase__:							Tuple         =     randn_tensor(model_output.shape					, dtype=model_output.dtype					, device=UpperCAmelCase_					, generator=UpperCAmelCase_)

          lowercase__:							Tuple         =     prev_sample + noise * sigma_up

          if not return_dict:
                 return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=UpperCAmelCase_					, pred_original_sample=UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, )			->   torch.FloatTensor:




          '''simple docstring'''




          lowercase__:							List[Any]         =     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
                 lowercase__:							str         =     self.timesteps.to(original_samples.device					, dtype=torch.floataa)
                 lowercase__:							Any         =     timesteps.to(original_samples.device					, dtype=torch.floataa)
          else:
                 lowercase__:							List[Any]         =     self.timesteps.to(original_samples.device)
                 lowercase__:							Optional[Any]         =     timesteps.to(original_samples.device)

          lowercase__:							List[str]         =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

          lowercase__:							List[Any]         =     original_samples + noise * sigma
          return noisy_samples





   def __len__(       self)			->   Dict:




          '''simple docstring'''




          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)))






