import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


UpperCAmelCase_    		=				{0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
UpperCAmelCase_    		=				set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_=0							,       lowerCAmelCase_=None )		->    Dict:




       '''simple docstring'''






       return [" ".join(x.split(' ' )[begin:end] ) for x in answers if len(x.split(' ' ) ) > 1]

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_=False )		->    str:




       '''simple docstring'''






       if make_sub_answers:
              # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
              # *e.g.* if the correct answer contains a prefix such as "the", or "a"
              _A=						(
                  given_answers + get_sub_answers(lowerCAmelCase_							,       begin=1 ) + get_sub_answers(lowerCAmelCase_							,       end=-1 )
              )
       _A=						[]
       for answer in given_answers:
              _A=						answer.replace('_'							,       ' ' ).lower()
              _A=						''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias )
              answers.append(' '.join(alias.split() ).strip() )
       return set(lowerCAmelCase_ )

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_=1							,       lowerCAmelCase_=1_00 )		->    List[str]:




       '''simple docstring'''






       _A,      _A=						jax.lax.top_k(lowerCAmelCase_							,       lowerCAmelCase_ )
       _A,      _A=						jax.lax.top_k(lowerCAmelCase_							,       lowerCAmelCase_ )

       _A=						best_end_idx[:, None] - best_start_idx[None, :]
       _A=						jnp.logical_or(widths < 0							,       widths > max_size )
       _A=						(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
       _A=						jnp.argmax(lowerCAmelCase_ ).item()

       return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    Tuple:




       '''simple docstring'''






       _A=						sample['question']['text']
       _A=						sample['document']['tokens']['token']
       _A=						sample['document']['tokens']['is_html']
       _A=						sample['annotations']['long_answer']
       _A=						sample['annotations']['short_answers']

       _A=						' '.join([context[i] for i in range(len(lowerCAmelCase_ ) ) if not is_html[i]] )

       # 0 - No ; 1 - Yes
       for answer in sample["annotations"]["yes_no_answer"]:
              if answer == 0 or answer == 1:
                     return {
                         "question": question,
                         "context": context_string,
                         "short": [],
                         "long": [],
                         "category": "no" if answer == 0 else "yes",
                     }

       _A=						[]
       for s in short_answers:
              short_targets.extend(s['text'] )
       _A=						list(set(lowerCAmelCase_ ) )

       _A=						[]
       for s in long_answers:
              if s["start_token"] == -1:
                     continue
              _A=						context[s['start_token'] : s['end_token']]
              _A=						is_html[s['start_token'] : s['end_token']]
              _A=						' '.join([answer[i] for i in range(len(lowerCAmelCase_ ) ) if not html[i]] )
              if new_answer not in long_targets:
                     long_targets.append(lowerCAmelCase_ )

       _A=						'long_short' if len(short_targets + long_targets ) > 0 else 'null'

       return {
           "question": question,
           "context": context_string,
           "short": short_targets,
           "long": long_targets,
           "category": category,
       }

def 				UpperCamelCase					(     )		->    List[str]:




       '''simple docstring'''






       _A=						load_from_disk('natural-questions-validation' )
       _A=						dataset.map(lowerCAmelCase_ ).remove_columns(['annotations', 'document', 'id'] )
       print(lowerCAmelCase_ )

       _A=						dataset.filter(lambda lowerCAmelCase_ : (len(x['question'] ) + len(x['context'] )) < 4 * 40_96 )
       _A=						short_validation_dataset.filter(lambda lowerCAmelCase_ : x["category"] != "null" )
       short_validation_dataset

       _A=						'vasudevgupta/flax-bigbird-natural-questions'
       _A=						FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCAmelCase_ )
       _A=						BigBirdTokenizerFast.from_pretrained(lowerCAmelCase_ )

       @jax.jit
       def forward(*lowerCAmelCase_							,       **lowerCAmelCase_ ):
              _A,      _A,      _A=						model(*lowerCAmelCase_							,       **lowerCAmelCase_ )
              return start_logits, end_logits, jnp.argmax(lowerCAmelCase_							,       axis=-1 )

       def evaluate(lowerCAmelCase_ ):
              # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
              _A=						tokenizer(
                  example['question']							,       example['context']							,       return_tensors='np'							,       max_length=40_96							,       padding='max_length'							,       truncation=lowerCAmelCase_							,       )

              _A,      _A,      _A=						forward(**lowerCAmelCase_ )

              _A=						CATEGORY_MAPPING[category.item()]

              _A=						example['long'] + example['short']
              if example["category"] in ["yes", "no", "null"]:
                     _A=						[example['category']]
              _A=						example['category'] != 'null'
              # Now target can be: "yes", "no", "null", "list of long & short answers"

              if predicted_category in ["yes", "no", "null"]:
                     _A=						[predicted_category]
                     _A=						example['output'] == example['targets']
                     _A=						predicted_category != 'null'
                     return example

              _A=						38 if predicted_category == 'short' else 10_24
              _A,      _A=						get_best_valid_start_end_idx(
                  start_scores[0]							,       end_scores[0]							,       top_k=8							,       max_size=lowerCAmelCase_ )

              _A=						inputs['input_ids'][0].tolist()
              _A=						[tokenizer.decode(input_ids[start_score : end_score + 1] )]

              _A=						expand_to_aliases(example['targets']							,       make_sub_answers=lowerCAmelCase_ )
              _A=						expand_to_aliases(example['output'] )

              # some preprocessing to both prediction and answer
              _A=						{''.join(a.split() ) for a in answers}
              _A=						{''.join(p.split() ) for p in predictions}
              _A=						{s for s in predictions if s not in ['``', '\'\'', '`', '\'']}

              # if there is a common element, it's a exact match
              _A=						len(list(answers & predictions ) ) > 0
              _A=						predicted_category != 'null' and len(lowerCAmelCase_ ) > 0

              return example

       _A=						short_validation_dataset.map(lowerCAmelCase_ )

       _A=						len(lowerCAmelCase_ )
       _A=						len(short_validation_dataset.filter(lambda lowerCAmelCase_ : x["match"] == 1 ) )
       print('EM score:'							,       (matched / total) * 1_00							,       '%' )


if __name__ == "__main__":
      main()
import numpy as np
from PIL import Image

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A,      _A,      _A=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       return (gray > 1_27) & (gray <= 2_55)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A=						np.zeros_like(lowerCAmelCase_ )
       _A=						np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )

       # Copy image to padded image
       _A=						image

       # Iterate over image & apply kernel
       for x in range(image.shape[1] ):
              for y in range(image.shape[0] ):
                     _A=						(
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     _A=						int(summation == 5 )
       return output


# kernel to be applied
UpperCAmelCase_    		=				np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      UpperCAmelCase_    		=				np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      UpperCAmelCase_    		=				erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      UpperCAmelCase_    		=				Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')