




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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class        lowerCamelCase__:
      UpperCAmelCase__ :      CommonSchedulerState

      # setable values
      UpperCAmelCase__ :      jnp.ndarray
      UpperCAmelCase__ :      jnp.ndarray
      UpperCAmelCase__ :      jnp.ndarray
      UpperCAmelCase__ :      Optional[int]       						=  None

      # running values
      UpperCAmelCase__ :      Optional[jnp.ndarray]       						=  None
      @classmethod
      def      lowerCAmelCase__		(							cls:					Optional[Any]       ,			UpperCamelCase_:					CommonSchedulerState       ,			UpperCamelCase_:					jnp.ndarray       ,			UpperCamelCase_:					jnp.ndarray       ,			UpperCamelCase_:					jnp.ndarray  ):
             return cls(common=UpperCamelCase_       ,			init_noise_sigma=UpperCamelCase_       ,			timesteps=UpperCamelCase_       ,			sigmas=UpperCamelCase_  )





@dataclass
class        lowerCamelCase__(   __lowerCamelCase):
      UpperCAmelCase__ :      LMSDiscreteSchedulerState





class        lowerCamelCase__(   __lowerCamelCase					,	__lowerCamelCase):
      UpperCAmelCase__ :      str       						=  [e.name for e in FlaxKarrasDiffusionSchedulers]

      UpperCAmelCase__ :      jnp.dtype
      @property
      def      lowerCAmelCase__		(							self:					Dict  ):
             return True
      @register_to_config
      def __init__(							self:					str       ,			UpperCamelCase_:					int = 10_00       ,			UpperCamelCase_:					float = 0.0001       ,			UpperCamelCase_:					float = 0.02       ,			UpperCamelCase_:					str = "linear"       ,			UpperCamelCase_:					Optional[jnp.ndarray] = None       ,			UpperCamelCase_:					str = "epsilon"       ,			UpperCamelCase_:					jnp.dtype = jnp.floataa       ,			):
             __lowerCamelCase  =		dtype
      def      lowerCAmelCase__		(							self:					List[Any]       ,			UpperCamelCase_:					Optional[CommonSchedulerState] = None  ):
             if common is None:
                    __lowerCamelCase  =		CommonSchedulerState.create(self  )

             __lowerCamelCase  =		jnp.arange(0       ,			self.config.num_train_timesteps  ).round()[::-1]
             __lowerCamelCase  =		((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

             # standard deviation of the initial noise distribution
             __lowerCamelCase  =		sigmas.max()

             return LMSDiscreteSchedulerState.create(
                 common=UpperCamelCase_       ,			init_noise_sigma=UpperCamelCase_       ,			timesteps=UpperCamelCase_       ,			sigmas=UpperCamelCase_       ,			)
      def      lowerCAmelCase__		(							self:					Any       ,			UpperCamelCase_:					LMSDiscreteSchedulerState       ,			UpperCamelCase_:					jnp.ndarray       ,			UpperCamelCase_:					int  ):
             ((__lowerCamelCase),  )  =		jnp.where(state.timesteps == timestep       ,			size=1  )
             __lowerCamelCase  =		step_index[0]

             __lowerCamelCase  =		state.sigmas[step_index]
             __lowerCamelCase  =		sample / ((sigma**2 + 1) ** 0.5)
             return sample
      def      lowerCAmelCase__		(							self:					int       ,			UpperCamelCase_:					LMSDiscreteSchedulerState       ,			UpperCamelCase_:					str       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					List[str]  ):

             def lms_derivative(UpperCamelCase_:					Dict  ):
                    __lowerCamelCase  =		1.0
                    for k in range(UpperCamelCase_  ):
                           if current_order == k:
                                  continue
                           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
                    return prod

             __lowerCamelCase  =		integrate.quad(UpperCamelCase_       ,			state.sigmas[t]       ,			state.sigmas[t + 1]       ,			epsrel=1E-4  )[0]

             return integrated_coeff
      def      lowerCAmelCase__		(							self:					str       ,			UpperCamelCase_:					LMSDiscreteSchedulerState       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					Tuple = ()  ):
             __lowerCamelCase  =		jnp.linspace(self.config.num_train_timesteps - 1       ,			0       ,			UpperCamelCase_       ,			dtype=self.dtype  )

             __lowerCamelCase  =		jnp.floor(UpperCamelCase_  ).astype(jnp.intaa  )
             __lowerCamelCase  =		jnp.ceil(UpperCamelCase_  ).astype(jnp.intaa  )

             __lowerCamelCase  =		jnp.mod(UpperCamelCase_       ,			1.0  )

             __lowerCamelCase  =		((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
             __lowerCamelCase  =		(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
             __lowerCamelCase  =		jnp.concatenate([sigmas, jnp.array([0.0]       ,			dtype=self.dtype  )]  )

             __lowerCamelCase  =		timesteps.astype(jnp.intaa  )

             # initial running values
             __lowerCamelCase  =		jnp.zeros((0,) + shape       ,			dtype=self.dtype  )

             return state.replace(
                 timesteps=UpperCamelCase_       ,			sigmas=UpperCamelCase_       ,			num_inference_steps=UpperCamelCase_       ,			derivatives=UpperCamelCase_       ,			)
      def      lowerCAmelCase__		(							self:					List[str]       ,			UpperCamelCase_:					LMSDiscreteSchedulerState       ,			UpperCamelCase_:					jnp.ndarray       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					jnp.ndarray       ,			UpperCamelCase_:					int = 4       ,			UpperCamelCase_:					bool = True       ,			):
             if state.num_inference_steps is None:
                    raise ValueError(
                        """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

             __lowerCamelCase  =		state.sigmas[timestep]

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                    __lowerCamelCase  =		sample - sigma * model_output
             elif self.config.prediction_type == "v_prediction":
                    # * c_out + input * c_skip
                    __lowerCamelCase  =		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
             else:
                    raise ValueError(
                        F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'  )

             # 2. Convert to an ODE derivative
             __lowerCamelCase  =		(sample - pred_original_sample) / sigma
             __lowerCamelCase  =		state.replace(derivatives=jnp.append(state.derivatives       ,			UpperCamelCase_  )  )
             if len(state.derivatives  ) > order:
                    __lowerCamelCase  =		state.replace(derivatives=jnp.delete(state.derivatives       ,			0  )  )

             # 3. Compute linear multistep coefficients
             __lowerCamelCase  =		min(timestep + 1       ,			UpperCamelCase_  )
             __lowerCamelCase  =		[self.get_lms_coefficient(UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_  ) for curr_order in range(UpperCamelCase_  )]

             # 4. Compute previous sample based on the derivatives path
             __lowerCamelCase  =		sample + sum(
                 coeff * derivative for coeff, derivative in zip(UpperCamelCase_       ,			reversed(state.derivatives  )  )  )

             if not return_dict:
                    return (prev_sample, state)

             return FlaxLMSSchedulerOutput(prev_sample=UpperCamelCase_       ,			state=UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					List[Any]       ,			UpperCamelCase_:					LMSDiscreteSchedulerState       ,			UpperCamelCase_:					jnp.ndarray       ,			UpperCamelCase_:					jnp.ndarray       ,			UpperCamelCase_:					jnp.ndarray       ,			):
             __lowerCamelCase  =		state.sigmas[timesteps].flatten()
             __lowerCamelCase  =		broadcast_to_shape_from_left(UpperCamelCase_       ,			noise.shape  )

             __lowerCamelCase  =		original_samples + noise * sigma

             return noisy_samples




      def __len__(							self:					List[Any]  ):
             return self.config.num_train_timesteps







import argparse
import json
import re
import string
import sys

import numpy as np


UpperCAmelCase_   							=      0.5




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Union[str, Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		[""".""", """,""", """;""", """:"""]

       for token in remove_tokens:
              __lowerCamelCase  =		ground_truth.replace(A__    ,     """"""      )
              __lowerCamelCase  =		prediction.replace(A__    ,     """"""      )

       __lowerCamelCase,  __lowerCamelCase  =		ground_truth.lower(), prediction.lower()
       __lowerCamelCase,  __lowerCamelCase  =		ground_truth.replace("""/"""    ,     """ """      ), prediction.replace("""/"""    ,     """ """      )
       __lowerCamelCase,  __lowerCamelCase  =		set(ground_truth.split(""" """      )      ), set(prediction.split(""" """      )      )

       __lowerCamelCase  =		ground_truth.intersection(A__      )
       __lowerCamelCase  =		ground_truth.union(A__      )
       __lowerCamelCase  =		len(A__      ) / len(A__      )
       return jaccard




def    lowerCamelCase__			(		A__			:   Optional[Any]      ):





       '''simple docstring'''



       def remove_articles(A__			:   Tuple      ):
              return re.sub(R"""\b(a|an|the)\b"""    ,     """ """    ,     A__      )

       def white_space_fix(A__			:   Optional[Any]      ):
              return " ".join(text.split()      )

       def remove_punc(A__			:   Optional[Any]      ):
              __lowerCamelCase  =		set(string.punctuation      )
              return "".join(ch for ch in text if ch not in exclude      )

       def lower(A__			:   Tuple      ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(A__      )      )      )      )




def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Optional[Any]    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		0, 0, 0

       __lowerCamelCase  =		"""Parties""" in qa_id

       # first check if ground truth is empty
       if len(A__      ) == 0:
              if len(A__      ) > 0:
                     fp += len(A__      )  # false positive for each one
       else:
              for ground_truth in ground_truths:
                     assert len(A__      ) > 0
                     # check if there is a match
                     __lowerCamelCase  =		False
                     for pred in predictions:
                            if substr_ok:
                                   __lowerCamelCase  =		get_jaccard(A__    ,     A__      ) >= IOU_THRESH or ground_truth in pred
                            else:
                                   __lowerCamelCase  =		get_jaccard(A__    ,     A__      ) >= IOU_THRESH
                            if is_match:
                                   __lowerCamelCase  =		True

                     if match_found:
                            tp += 1
                     else:
                            fn += 1

        # now also get any fps by looping through preds
              for pred in predictions:
                     # Check if there's a match. if so, don't count (don't want to double count based on the above)
                     # but if there's no match, then this is a false positive.
                     # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
                     # multiple predictions that are matched with the same answer.)
                     __lowerCamelCase  =		False
                     for ground_truth in ground_truths:
                            assert len(A__      ) > 0
                            if substr_ok:
                                   __lowerCamelCase  =		get_jaccard(A__    ,     A__      ) >= IOU_THRESH or ground_truth in pred
                            else:
                                   __lowerCamelCase  =		get_jaccard(A__    ,     A__      ) >= IOU_THRESH
                            if is_match:
                                   __lowerCamelCase  =		True

                     if not match_found:
                            fp += 1

       __lowerCamelCase  =		tp / (tp + fp) if tp + fp > 0 else np.nan
       __lowerCamelCase  =		tp / (tp + fn) if tp + fn > 0 else np.nan

       return precision, recall




def    lowerCamelCase__			(		A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		precisions[::-1]
       for i in range(1    ,     len(A__      )      ):
              __lowerCamelCase  =		max(precision_best[i - 1]    ,     precision_best[i]      )
       __lowerCamelCase  =		precision_best[::-1]
       return precisions




def    lowerCamelCase__			(		A__			:   str    ,     A__			:   Union[str, Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		process_precisions(A__      )
       __lowerCamelCase  =		np.trapz(A__    ,     A__      )
       if np.isnan(A__      ):
              return 0
       return aupr




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   List[str]    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		process_precisions(A__      )
       __lowerCamelCase  =		0
       for prec, recall in zip(A__    ,     A__      ):
              if recall >= recall_thresh:
                     __lowerCamelCase  =		prec
                     break
       return prec_at_recall




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       return normalize_answer(A__      ) == normalize_answer(A__      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   int    ,     A__			:   Dict      ):





       '''simple docstring'''



       __lowerCamelCase  =		0
       for pred in predictions:
              for ground_truth in ground_truths:
                     __lowerCamelCase  =		metric_fn(A__    ,     A__      )
                     if score == 1:  # break the loop when one prediction matches the ground truth
                            break
              if score == 1:
                     break
       return score




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		__lowerCamelCase  =		__lowerCamelCase  =		0
       __lowerCamelCase  =		[]
       __lowerCamelCase  =		[]
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   __lowerCamelCase  =		"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
                                   print(A__    ,     file=sys.stderr      )
                                   continue
                            __lowerCamelCase  =		[x["""text"""] for x in qa["""answers"""]]
                            __lowerCamelCase  =		predictions[qa["""id"""]]
                            __lowerCamelCase,  __lowerCamelCase  =		compute_precision_recall(A__    ,     A__    ,     qa["""id"""]      )

                            precisions.append(A__      )
                            recalls.append(A__      )

                            if precision == 0 and recall == 0:
                                   fa += 0
                            else:
                                   fa += 2 * (precision * recall) / (precision + recall)

                            exact_match += metric_max_over_ground_truths(A__    ,     A__    ,     A__      )

       __lowerCamelCase  =		[x for _, x in sorted(zip(A__    ,     A__      )      )]
       recalls.sort()

       __lowerCamelCase  =		100.0 * fa / total
       __lowerCamelCase  =		100.0 * exact_match / total
       __lowerCamelCase  =		get_aupr(A__    ,     A__      )

       __lowerCamelCase  =		get_prec_at_recall(A__    ,     A__    ,     recall_thresh=0.9      )
       __lowerCamelCase  =		get_prec_at_recall(A__    ,     A__    ,     recall_thresh=0.8      )

       return {
           "exact_match": exact_match,
           "f1": fa,
           "aupr": aupr,
           "prec_at_80_recall": prec_at_aa_recall,
           "prec_at_90_recall": prec_at_aa_recall,
       }


if __name__ == "__main__":
       UpperCAmelCase_   							=      argparse.ArgumentParser(description='Evaluation for CUAD')
       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)
              UpperCAmelCase_   							=      dataset_json['data']
       with open(args.prediction_file) as prediction_file:
              UpperCAmelCase_   							=      json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))

