'''simple docstring'''




import argparse
import json
import re
import string
import sys

import numpy as np


__SCREAMING_SNAKE_CASE :  str  =						0.5

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   str					)					->					Optional[int]:


       """simple docstring"""







       _UpperCAmelCase  :						int							   =					['''.''', ''',''', ''';''', ''':''']

       for token in remove_tokens:
              _UpperCAmelCase  :						int							   =					ground_truth.replace(lowercase_    ,			""					)
              _UpperCAmelCase  :						Tuple							   =					prediction.replace(lowercase_    ,			""					)

       _UpperCAmelCase  :						List[str]							   =					ground_truth.lower(), prediction.lower()
       _UpperCAmelCase  :						List[Any]							   =					ground_truth.replace("/"    ,			" "					), prediction.replace("/"    ,			" "					)
       _UpperCAmelCase  :						Tuple							   =					set(ground_truth.split(" "					)					), set(prediction.split(" "					)					)

       _UpperCAmelCase  :						Union[str, Any]							   =					ground_truth.intersection(lowercase_					)
       _UpperCAmelCase  :						int							   =					ground_truth.union(lowercase_					)
       _UpperCAmelCase  :						int							   =					len(lowercase_					) / len(lowercase_					)
       return jaccard

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[str]					)					->					Tuple:


       """simple docstring"""







       def remove_articles(_UpperCAmelCase						:   Optional[Any]					):
              return re.sub(R"\b(a|an|the)\b"    ,			" "    ,			lowercase_					)

       def white_space_fix(_UpperCAmelCase						:   str					):
              return " ".join(text.split()					)

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

       def lower(_UpperCAmelCase						:   List[str]					):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(lowercase_					)					)					)					)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   Any    ,			_UpperCAmelCase						:   Dict					)					->					List[str]:


       """simple docstring"""







       _UpperCAmelCase  :						Tuple							   =					0, 0, 0

       _UpperCAmelCase  :						Optional[int]							   =					'''Parties''' in qa_id

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

                     if not match_found:
                            fp += 1

       _UpperCAmelCase  :						Dict							   =					tp / (tp + fp) if tp + fp > 0 else np.nan
       _UpperCAmelCase  :						str							   =					tp / (tp + fn) if tp + fn > 0 else np.nan

       return precision, recall

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[int]					)					->					str:


       """simple docstring"""







       _UpperCAmelCase  :						Optional[Any]							   =					precisions[::-1]
       for i in range(1    ,			len(lowercase_					)					):
              _UpperCAmelCase  :						Any							   =					max(precision_best[i - 1]    ,			precision_best[i]					)
       _UpperCAmelCase  :						Tuple							   =					precision_best[::-1]
       return precisions

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   Tuple					)					->					List[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					process_precisions(lowercase_					)
       _UpperCAmelCase  :						Tuple							   =					np.trapz(lowercase_    ,			lowercase_					)
       if np.isnan(lowercase_					):
              return 0
       return aupr

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[int]    ,			_UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   Optional[int]					)					->					List[str]:


       """simple docstring"""







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

def 			UpperCamelCase_							(      _UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   Optional[int]					)					->					int:


       """simple docstring"""







       return normalize_answer(lowercase_					) == normalize_answer(lowercase_					)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   List[Any]					)					->					Any:


       """simple docstring"""







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

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[int]    ,			_UpperCAmelCase						:   List[Any]					)					->					List[Any]:


       """simple docstring"""







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

                            precisions.append(lowercase_					)
                            recalls.append(lowercase_					)

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

                            exact_match += metric_max_over_ground_truths(lowercase_    ,			lowercase_    ,			lowercase_					)

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

       _UpperCAmelCase  :						Dict							   =					100.0 * fa / total
       _UpperCAmelCase  :						str							   =					100.0 * exact_match / total
       _UpperCAmelCase  :						Tuple							   =					get_aupr(lowercase_    ,			lowercase_					)

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


'''simple docstring'''




import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  __SCREAMING_SNAKE_CASE :  Any  =						False


if is_vision_available():
  from PIL import Image
else:

  class 			lowerCamelCase_			:
        '''simple docstring'''

        @staticmethod
        def   _A			( *A		:       str		,      **A		:       Optional[Any] ):
               pass

@is_pipeline_test
@require_vision
class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Union[str, Any]      			=		MODEL_FOR_VISION_2_SEQ_MAPPING
      __UpperCamelCase:						int      			=		TF_MODEL_FOR_VISION_2_SEQ_MAPPING




      def   _A			( self		:       Any		,      A		:       Any		,      A		:       str		,      A		:       Optional[int] ):
             _UpperCAmelCase  :						Dict							   =					pipeline("image-to-text"		,      model=A		,      tokenizer=A		,      image_processor=A )
             _UpperCAmelCase  :						Union[str, Any]							   =					[
                 Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]
             return pipe, examples




      def   _A			( self		:       Tuple		,      A		:       Union[str, Any]		,      A		:       Optional[Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": ANY(A )}],
                     [{"generated_text": ANY(A )}],
                 ]		,      )




      @require_tf
      def   _A			( self		:       Any ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-vit-gpt2"		,      framework="tf" )
             _UpperCAmelCase  :						int							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     },
                 ]		,      )

             _UpperCAmelCase  :						Tuple							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                 ]		,      )

             _UpperCAmelCase  :						Dict							   =					pipe(A		,      max_new_tokens=1 )
             self.assertEqual(
                 A		,      [{"generated_text": "growth"}]		,      )




      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-vit-gpt2" )
             _UpperCAmelCase  :						Optional[int]							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						Optional[Any]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     },
                 ]		,      )

             _UpperCAmelCase  :						str							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                 ]		,      )




      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-BlipForConditionalGeneration" )
             _UpperCAmelCase  :						Any							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"
             _UpperCAmelCase  :						int							   =					"a photo of"

             _UpperCAmelCase  :						str							   =					pipe(A		,      prompt=A )
             self.assertTrue(outputs[0]["generated_text"].startswith(A ) )




      @slow
      @require_torch
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						Dict							   =					pipeline("image-to-text"		,      model="ydshieh/vit-gpt2-coco-en" )
             _UpperCAmelCase  :						int							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}] )

             _UpperCAmelCase  :						int							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                 ]		,      )




      @slow
      @require_torch
      def   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						Tuple							   =					pipeline("image-to-text"		,      model="Salesforce/blip-image-captioning-base" )
             _UpperCAmelCase  :						int							   =					"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
             _UpperCAmelCase  :						Optional[int]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Any							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}] )




      @slow
      @require_torch
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipeline("image-to-text"		,      model="microsoft/git-base-coco" )
             _UpperCAmelCase  :						Tuple							   =					"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
             _UpperCAmelCase  :						Dict							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cartoon of a purple character."}] )




      @slow
      @require_torch
      def   _A			( self		:       int ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="Salesforce/blip-image-captioning-base" )
             _UpperCAmelCase  :						List[str]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						int							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Dict							   =					"a photography of"

             _UpperCAmelCase  :						Optional[int]							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "a photography of a volcano"}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[Any]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )




      @slow
      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						Optional[int]							   =					pipeline("image-to-text"		,      model="microsoft/git-base-coco" )
             _UpperCAmelCase  :						Optional[Any]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						List[str]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						List[Any]							   =					"a photo of a"

             _UpperCAmelCase  :						Tuple							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "a photo of a tent with a tent and a tent in the background."}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[str]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )




      @unittest.skipIf(
          not is_torch_greater_or_equal_than_1_11		,      reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`." )
      @slow
      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						str							   =					pipeline("image-to-text"		,      model="google/pix2struct-ai2d-base" )
             _UpperCAmelCase  :						List[Any]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						List[Any]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Optional[int]							   =					"What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

             _UpperCAmelCase  :						str							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "ash cloud"}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[str]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )





      @slow
      @require_tf
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						str							   =					pipeline("image-to-text"		,      model="ydshieh/vit-gpt2-coco-en"		,      framework="tf" )
             _UpperCAmelCase  :						str							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						Any							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}] )

             _UpperCAmelCase  :						Optional[Any]							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                     [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                 ]		,      )

