






"""simple docstring"""





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left







@flax.struct.dataclass
class 						_a  :





   '''simple docstring'''






   UpperCamelCase__     	=							None
   UpperCamelCase__     	=							None
   UpperCamelCase__     	=							None





   @classmethod
   def __lowercase							(       cls)			->   Union[str, Any]:




          '''simple docstring'''




          return cls()







@dataclass
class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							None







class 						_a  (		lowercase_							,	lowercase_       ):





   '''simple docstring'''






   @property
   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          return True





   @register_to_config
   def __init__(       self					, UpperCAmelCase_ = 2_000					, UpperCAmelCase_ = 0.15					, UpperCAmelCase_ = 0.01					, UpperCAmelCase_ = 13_48.0					, UpperCAmelCase_ = 1E-5					, UpperCAmelCase_ = 1					, )			->   Dict:




          '''simple docstring'''




          pass





   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          lowercase__:							List[Any]         =     ScoreSdeVeSchedulerState.create()
          return self.set_sigmas(
              UpperCAmelCase_					, self.config.num_train_timesteps					, self.config.sigma_min					, self.config.sigma_max					, self.config.sampling_eps					, )





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = ()					, UpperCAmelCase_ = None)			->   ScoreSdeVeSchedulerState:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

          lowercase__:							Union[str, Any]         =     jnp.linspace(1					, UpperCAmelCase_					, UpperCAmelCase_)
          return state.replace(timesteps=UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, )			->   ScoreSdeVeSchedulerState:




          '''simple docstring'''




          lowercase__:							Optional[int]         =     sigma_min if sigma_min is not None else self.config.sigma_min
          lowercase__:							Any         =     sigma_max if sigma_max is not None else self.config.sigma_max
          lowercase__:							Dict         =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if state.timesteps is None:
                 lowercase__:							int         =     self.set_timesteps(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)

          lowercase__:							Union[str, Any]         =     jnp.exp(jnp.linspace(jnp.log(UpperCAmelCase_)					, jnp.log(UpperCAmelCase_)					, UpperCAmelCase_))
          lowercase__:							List[Any]         =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])

          return state.replace(discrete_sigmas=UpperCAmelCase_					, sigmas=UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   Tuple:




          '''simple docstring'''




          return jnp.where(timesteps == 0					, jnp.zeros_like(UpperCAmelCase_)					, state.discrete_sigmas[timesteps - 1])





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = True					, )			->   Union[FlaxSdeVeOutput, Tuple]:




          '''simple docstring'''




          if state.timesteps is None:
                 raise ValueError(
                     "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

          lowercase__:							List[str]         =     timestep * jnp.ones(
              sample.shape[0]					, )
          lowercase__:							Optional[Any]         =     (timestep * (len(state.timesteps) - 1)).long()

          lowercase__:							List[str]         =     state.discrete_sigmas[timesteps]
          lowercase__:							Optional[int]         =     self.get_adjacent_sigma(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)
          lowercase__:							List[Any]         =     jnp.zeros_like(UpperCAmelCase_)
          lowercase__:							str         =     (sigma**2 - adjacent_sigma**2) ** 0.5

          # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
          # also equation 47 shows the analog from SDE models to ancestral sampling methods
          lowercase__:							Optional[int]         =     diffusion.flatten()
          lowercase__:							List[Any]         =     broadcast_to_shape_from_left(UpperCAmelCase_					, sample.shape)
          lowercase__:							List[Any]         =     drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          lowercase__:							Dict         =     random.split(UpperCAmelCase_					, num=1)
          lowercase__:							Tuple         =     random.normal(key=UpperCAmelCase_					, shape=sample.shape)
          lowercase__:							str         =     sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          lowercase__:							List[Any]         =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
                 return (prev_sample, prev_sample_mean, state)

          return FlaxSdeVeOutput(prev_sample=UpperCAmelCase_					, prev_sample_mean=UpperCAmelCase_					, state=UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_ = True					, )			->   Union[FlaxSdeVeOutput, Tuple]:




          '''simple docstring'''




          if state.timesteps is None:
                 raise ValueError(
                     "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

          # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
          # sample noise for correction
          lowercase__:							Optional[int]         =     random.split(UpperCAmelCase_					, num=1)
          lowercase__:							Optional[Any]         =     random.normal(key=UpperCAmelCase_					, shape=sample.shape)

          # compute step size from the model_output, the noise, and the snr
          lowercase__:							Union[str, Any]         =     jnp.linalg.norm(UpperCAmelCase_)
          lowercase__:							int         =     jnp.linalg.norm(UpperCAmelCase_)
          lowercase__:							Tuple         =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          lowercase__:							List[Any]         =     step_size * jnp.ones(sample.shape[0])

          # compute corrected sample: model_output term and noise term
          lowercase__:							List[Any]         =     step_size.flatten()
          lowercase__:							Dict         =     broadcast_to_shape_from_left(UpperCAmelCase_					, sample.shape)
          lowercase__:							Optional[int]         =     sample + step_size * model_output
          lowercase__:							Optional[Any]         =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
                 return (prev_sample, state)

          return FlaxSdeVeOutput(prev_sample=UpperCAmelCase_					, state=UpperCAmelCase_)





   def __len__(       self)			->   Tuple:




          '''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)))






