





'''simple docstring'''

import argparse
import json
import re
import string
import sys

import numpy as np


UpperCAmelCase       :		str		=				0.5




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[str]         =  [".", ",", ";", ":"]

      for token in remove_tokens:
            _snake_case		:						Optional[Any]         =  ground_truth.replace(lowerCAmelCase_      ,   ''''''						)
            _snake_case		:						str         =  prediction.replace(lowerCAmelCase_      ,   ''''''						)

      _snake_case		:						List[Any]         =  ground_truth.lower(), prediction.lower()
      _snake_case		:						Optional[Any]         =  ground_truth.replace('''/'''      ,   ''' '''						), prediction.replace('''/'''      ,   ''' '''						)
      _snake_case		:						str         =  set(ground_truth.split(''' '''						)						), set(prediction.split(''' '''						)						)

      _snake_case		:						Optional[int]         =  ground_truth.intersection(lowerCAmelCase_						)
      _snake_case		:						Optional[Any]         =  ground_truth.union(lowerCAmelCase_						)
      _snake_case		:						Optional[int]         =  len(lowerCAmelCase_						) / len(lowerCAmelCase_						)
      return jaccard




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      def remove_articles(lowerCAmelCase_						):
            return re.sub(R'''\b(a|an|the)\b'''      ,   ''' '''      ,   lowerCAmelCase_						)

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

      def remove_punc(lowerCAmelCase_						):
            _snake_case		:						Optional[int]         =  set(string.punctuation						)
            return "".join(ch for ch in text if ch not in exclude						)

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

      return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_						)						)						)						)




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Optional[int]         =  0, 0, 0

      _snake_case		:						Dict         =  "Parties" in qa_id

      # first check if ground truth is empty
      if len(lowerCAmelCase_						) == 0:
            if len(lowerCAmelCase_						) > 0:
                  fp += len(lowerCAmelCase_						)  # false positive for each one
      else:
            for ground_truth in ground_truths:
                  assert len(lowerCAmelCase_						) > 0
                  # check if there is a match
                  _snake_case		:						Tuple         =  False
                  for pred in predictions:
                        if substr_ok:
                              _snake_case		:						List[str]         =  get_jaccard(lowerCAmelCase_      ,   lowerCAmelCase_						) >= IOU_THRESH or ground_truth in pred
                        else:
                              _snake_case		:						List[Any]         =  get_jaccard(lowerCAmelCase_      ,   lowerCAmelCase_						) >= IOU_THRESH
                        if is_match:
                              _snake_case		:						List[str]         =  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.)
                  _snake_case		:						Tuple         =  False
                  for ground_truth in ground_truths:
                        assert len(lowerCAmelCase_						) > 0
                        if substr_ok:
                              _snake_case		:						Any         =  get_jaccard(lowerCAmelCase_      ,   lowerCAmelCase_						) >= IOU_THRESH or ground_truth in pred
                        else:
                              _snake_case		:						Optional[Any]         =  get_jaccard(lowerCAmelCase_      ,   lowerCAmelCase_						) >= IOU_THRESH
                        if is_match:
                              _snake_case		:						Dict         =  True

                  if not match_found:
                        fp += 1

      _snake_case		:						Union[str, Any]         =  tp / (tp + fp) if tp + fp > 0 else np.nan
      _snake_case		:						List[str]         =  tp / (tp + fn) if tp + fn > 0 else np.nan

      return precision, recall




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Optional[Any]         =  precisions[::-1]
      for i in range(1      ,   len(lowerCAmelCase_						)						):
            _snake_case		:						List[Any]         =  max(precision_best[i - 1]      ,   precision_best[i]						)
      _snake_case		:						Any         =  precision_best[::-1]
      return precisions




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Optional[Any]         =  process_precisions(lowerCAmelCase_						)
      _snake_case		:						int         =  np.trapz(lowerCAmelCase_      ,   lowerCAmelCase_						)
      if np.isnan(lowerCAmelCase_						):
            return 0
      return aupr




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Dict         =  process_precisions(lowerCAmelCase_						)
      _snake_case		:						Optional[int]         =  0
      for prec, recall in zip(lowerCAmelCase_      ,   lowerCAmelCase_						):
            if recall >= recall_thresh:
                  _snake_case		:						Any         =  prec
                  break
      return prec_at_recall




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      return normalize_answer(lowerCAmelCase_						) == normalize_answer(lowerCAmelCase_						)




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[Any]         =  0
      for pred in predictions:
            for ground_truth in ground_truths:
                  _snake_case		:						Optional[Any]         =  metric_fn(lowerCAmelCase_      ,   lowerCAmelCase_						)
                  if score == 1:  # break the loop when one prediction matches the ground truth
                        break
            if score == 1:
                  break
      return score




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



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

                        precisions.append(lowerCAmelCase_						)
                        recalls.append(lowerCAmelCase_						)

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

                        exact_match += metric_max_over_ground_truths(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						)

      _snake_case		:						int         =  [x for _, x in sorted(zip(lowerCAmelCase_      ,   lowerCAmelCase_						)						)]
      recalls.sort()

      _snake_case		:						Union[str, Any]         =  100.0 * fa / total
      _snake_case		:						Tuple         =  100.0 * exact_match / total
      _snake_case		:						Optional[int]         =  get_aupr(lowerCAmelCase_      ,   lowerCAmelCase_						)

      _snake_case		:						List[Any]         =  get_prec_at_recall(lowerCAmelCase_      ,   lowerCAmelCase_      ,   recall_thresh=0.9						)
      _snake_case		:						Optional[int]         =  get_prec_at_recall(lowerCAmelCase_      ,   lowerCAmelCase_      ,   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       :		Any		=				argparse.ArgumentParser(description='Evaluation for CUAD')
   parser.add_argument('dataset_file', help='Dataset file')
   parser.add_argument('prediction_file', help='Prediction File')
   UpperCAmelCase       :		Optional[Any]		=				parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      UpperCAmelCase       :		int		=				json.load(dataset_file)
      UpperCAmelCase       :		int		=				dataset_json["""data"""]
   with open(args.prediction_file) as prediction_file:
      UpperCAmelCase       :		List[str]		=				json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))




"""simple docstring"""




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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				42







class 	_lowercase    (							lowerCAmelCase_						, lowerCAmelCase_					):


     '''simple docstring'''





     @register_to_config
     def __init__(   self			,      __UpperCamelCase = None			,      __UpperCamelCase = 3			,      __UpperCamelCase = 3			,      __UpperCamelCase = False			,      __UpperCamelCase = "positional"			,      __UpperCamelCase = 0			,      __UpperCamelCase = True			,      __UpperCamelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")			,      __UpperCamelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")			,      __UpperCamelCase = (2_24, 4_48, 6_72, 8_96)			,      __UpperCamelCase = 2			,      __UpperCamelCase = 1			,      __UpperCamelCase = 1			,      __UpperCamelCase = "conv"			,      __UpperCamelCase = "conv"			,      __UpperCamelCase = "silu"			,      __UpperCamelCase = 8			,      __UpperCamelCase = 32			,      __UpperCamelCase = 1E-5			,      __UpperCamelCase = "default"			,      __UpperCamelCase = True			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      )->       Any:
          super().__init__()

          UpperCAmelCase__				:			Optional[int]							=    sample_size
          UpperCAmelCase__				:			Any							=    block_out_channels[0] * 4

          # Check inputs
          if len(__UpperCamelCase	) != len(__UpperCamelCase	):
               raise ValueError(
                   F"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."	)

          if len(__UpperCamelCase	) != len(__UpperCamelCase	):
               raise ValueError(
                   F"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."	)

          # input
          UpperCAmelCase__				:			List[Any]							=    nn.Convad(__UpperCamelCase			,      block_out_channels[0]			,      kernel_size=3			,      padding=(1, 1)	)

          # time
          if time_embedding_type == "fourier":
               UpperCAmelCase__				:			int							=    GaussianFourierProjection(embedding_size=block_out_channels[0]			,      scale=16	)
               UpperCAmelCase__				:			Dict							=    2 * block_out_channels[0]
          elif time_embedding_type == "positional":
               UpperCAmelCase__				:			List[str]							=    Timesteps(block_out_channels[0]			,      __UpperCamelCase			,      __UpperCamelCase	)
               UpperCAmelCase__				:			Any							=    block_out_channels[0]

          UpperCAmelCase__				:			List[str]							=    TimestepEmbedding(__UpperCamelCase			,      __UpperCamelCase	)

          # class embedding
          if class_embed_type is None and num_class_embeds is not None:
               UpperCAmelCase__				:			Optional[Any]							=    nn.Embedding(__UpperCamelCase			,      __UpperCamelCase	)
          elif class_embed_type == "timestep":
               UpperCAmelCase__				:			str							=    TimestepEmbedding(__UpperCamelCase			,      __UpperCamelCase	)
          elif class_embed_type == "identity":
               UpperCAmelCase__				:			Union[str, Any]							=    nn.Identity(__UpperCamelCase			,      __UpperCamelCase	)
          else:
               UpperCAmelCase__				:			Optional[int]							=    None

          UpperCAmelCase__				:			str							=    nn.ModuleList([]	)
          UpperCAmelCase__				:			Any							=    None
          UpperCAmelCase__				:			Tuple							=    nn.ModuleList([]	)

          # down
          UpperCAmelCase__				:			Any							=    block_out_channels[0]
          for i, down_block_type in enumerate(__UpperCamelCase	):
               UpperCAmelCase__				:			List[Any]							=    output_channel
               UpperCAmelCase__				:			Optional[int]							=    block_out_channels[i]
               UpperCAmelCase__				:			Any							=    i == len(__UpperCamelCase	) - 1

               UpperCAmelCase__				:			List[Any]							=    get_down_block(
                   __UpperCamelCase			,      num_layers=__UpperCamelCase			,      in_channels=__UpperCamelCase			,      out_channels=__UpperCamelCase			,      temb_channels=__UpperCamelCase			,      add_downsample=not is_final_block			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      resnet_groups=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,      downsample_padding=__UpperCamelCase			,      resnet_time_scale_shift=__UpperCamelCase			,      downsample_type=__UpperCamelCase			,      )
               self.down_blocks.append(__UpperCamelCase	)

          # mid
          UpperCAmelCase__				:			List[str]							=    UNetMidBlockaD(
              in_channels=block_out_channels[-1]			,      temb_channels=__UpperCamelCase			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      output_scale_factor=__UpperCamelCase			,      resnet_time_scale_shift=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]			,      resnet_groups=__UpperCamelCase			,      add_attention=__UpperCamelCase			,      )

          # up
          UpperCAmelCase__				:			Tuple							=    list(reversed(__UpperCamelCase	)	)
          UpperCAmelCase__				:			Optional[Any]							=    reversed_block_out_channels[0]
          for i, up_block_type in enumerate(__UpperCamelCase	):
               UpperCAmelCase__				:			int							=    output_channel
               UpperCAmelCase__				:			List[Any]							=    reversed_block_out_channels[i]
               UpperCAmelCase__				:			Dict							=    reversed_block_out_channels[min(i + 1			,      len(__UpperCamelCase	) - 1	)]

               UpperCAmelCase__				:			int							=    i == len(__UpperCamelCase	) - 1

               UpperCAmelCase__				:			Dict							=    get_up_block(
                   __UpperCamelCase			,      num_layers=layers_per_block + 1			,      in_channels=__UpperCamelCase			,      out_channels=__UpperCamelCase			,      prev_output_channel=__UpperCamelCase			,      temb_channels=__UpperCamelCase			,      add_upsample=not is_final_block			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      resnet_groups=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,      resnet_time_scale_shift=__UpperCamelCase			,      upsample_type=__UpperCamelCase			,      )
               self.up_blocks.append(__UpperCamelCase	)
               UpperCAmelCase__				:			Dict							=    output_channel

          # out
          UpperCAmelCase__				:			str							=    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,      32	)
          UpperCAmelCase__				:			Tuple							=    nn.GroupNorm(num_channels=block_out_channels[0]			,      num_groups=__UpperCamelCase			,      eps=__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    nn.SiLU()
          UpperCAmelCase__				:			Optional[int]							=    nn.Convad(block_out_channels[0]			,      __UpperCamelCase			,      kernel_size=3			,      padding=1	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = None			,      __UpperCamelCase = True			,      )->       Union[UNetaDOutput, Tuple]:
          # 0. center input if necessary
          if self.config.center_input_sample:
               UpperCAmelCase__				:			Any							=    2 * sample - 1.0

          # 1. time
          UpperCAmelCase__				:			Tuple							=    timestep
          if not torch.is_tensor(__UpperCamelCase	):
               UpperCAmelCase__				:			Optional[int]							=    torch.tensor([timesteps]			,      dtype=torch.long			,      device=sample.device	)
          elif torch.is_tensor(__UpperCamelCase	) and len(timesteps.shape	) == 0:
               UpperCAmelCase__				:			List[Any]							=    timesteps[None].to(sample.device	)

          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          UpperCAmelCase__				:			Dict							=    timesteps * torch.ones(sample.shape[0]			,      dtype=timesteps.dtype			,      device=timesteps.device	)

          UpperCAmelCase__				:			List[str]							=    self.time_proj(__UpperCamelCase	)

          # timesteps does not contain any weights and will always return f32 tensors
          # but time_embedding might actually be running in fp16. so we need to cast here.
          # there might be better ways to encapsulate this.
          UpperCAmelCase__				:			Dict							=    t_emb.to(dtype=self.dtype	)
          UpperCAmelCase__				:			Optional[int]							=    self.time_embedding(__UpperCamelCase	)

          if self.class_embedding is not None:
               if class_labels is None:
                    raise ValueError("class_labels should be provided when doing class conditioning"	)

               if self.config.class_embed_type == "timestep":
                    UpperCAmelCase__				:			List[Any]							=    self.time_proj(__UpperCamelCase	)

               UpperCAmelCase__				:			List[str]							=    self.class_embedding(__UpperCamelCase	).to(dtype=self.dtype	)
               UpperCAmelCase__				:			Optional[int]							=    emb + class_emb

          # 2. pre-process
          UpperCAmelCase__				:			List[str]							=    sample
          UpperCAmelCase__				:			str							=    self.conv_in(__UpperCamelCase	)

          # 3. down
          UpperCAmelCase__				:			List[str]							=    (sample,)
          for downsample_block in self.down_blocks:
               if hasattr(__UpperCamelCase			,      "skip_conv"	):
                    UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[Any]							=    downsample_block(
                        hidden_states=__UpperCamelCase			,      temb=__UpperCamelCase			,      skip_sample=__UpperCamelCase	)
               else:
                    UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[Any]							=    downsample_block(hidden_states=__UpperCamelCase			,      temb=__UpperCamelCase	)

               down_block_res_samples += res_samples

          # 4. mid
          UpperCAmelCase__				:			Union[str, Any]							=    self.mid_block(__UpperCamelCase			,      __UpperCamelCase	)

          # 5. up
          UpperCAmelCase__				:			Dict							=    None
          for upsample_block in self.up_blocks:
               UpperCAmelCase__				:			Union[str, Any]							=    down_block_res_samples[-len(upsample_block.resnets	) :]
               UpperCAmelCase__				:			Optional[Any]							=    down_block_res_samples[: -len(upsample_block.resnets	)]

               if hasattr(__UpperCamelCase			,      "skip_conv"	):
                    UpperCAmelCase__				,						UpperCAmelCase__				:			str							=    upsample_block(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)
               else:
                    UpperCAmelCase__				:			Tuple							=    upsample_block(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)

        # 6. post-process
          UpperCAmelCase__				:			List[Any]							=    self.conv_norm_out(__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    self.conv_act(__UpperCamelCase	)
          UpperCAmelCase__				:			Optional[Any]							=    self.conv_out(__UpperCamelCase	)

          if skip_sample is not None:
               sample += skip_sample

          if self.config.time_embedding_type == "fourier":
               UpperCAmelCase__				:			str							=    timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]	)))	)
               UpperCAmelCase__				:			int							=    sample / timesteps

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=__UpperCamelCase	)



