

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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=0		,				SCREAMING_SNAKE_CASE=None					)							->   List[Any]:

     """simple docstring"""







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







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False					)							->   List[Any]:

     """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"
          _lowerCAmelCase :       Optional[Any]	   =     (
              given_answers + get_sub_answers(SCREAMING_SNAKE_CASE		,				begin=1					) + get_sub_answers(SCREAMING_SNAKE_CASE		,				end=-1					)
          )
     _lowerCAmelCase :       Optional[int]	   =     []
     for answer in given_answers:
          _lowerCAmelCase :       Dict	   =     answer.replace("_"		,				" "					).lower()
          _lowerCAmelCase :       Union[str, Any]	   =     ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias					)
          answers.append(" ".join(alias.split()					).strip()					)
     return set(SCREAMING_SNAKE_CASE					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1		,				SCREAMING_SNAKE_CASE=100					)							->   str:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     jax.lax.top_k(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Dict	   =     jax.lax.top_k(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       Dict	   =     best_end_idx[:, None] - best_start_idx[None, :]
     _lowerCAmelCase :       List[str]	   =     jnp.logical_or(widths < 0		,				widths > max_size					)
     _lowerCAmelCase :       int	   =     (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
     _lowerCAmelCase :       List[Any]	   =     jnp.argmax(SCREAMING_SNAKE_CASE					).item()

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







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       int	   =     sample['question']['text']
     _lowerCAmelCase :       Union[str, Any]	   =     sample['document']['tokens']['token']
     _lowerCAmelCase :       Union[str, Any]	   =     sample['document']['tokens']['is_html']
     _lowerCAmelCase :       Optional[Any]	   =     sample['annotations']['long_answer']
     _lowerCAmelCase :       List[str]	   =     sample['annotations']['short_answers']

     _lowerCAmelCase :       Any	   =     ' '.join([context[i] for i in range(len(SCREAMING_SNAKE_CASE					)					) 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",
               }

     _lowerCAmelCase :       Dict	   =     []
     for s in short_answers:
          short_targets.extend(s["text"]					)
     _lowerCAmelCase :       Optional[Any]	   =     list(set(SCREAMING_SNAKE_CASE					)					)

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

     _lowerCAmelCase :       str	   =     '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 						_snake_case		(     )							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[Any]	   =     load_from_disk("natural-questions-validation"					)
     _lowerCAmelCase :       int	   =     dataset.map(SCREAMING_SNAKE_CASE					).remove_columns(["annotations", "document", "id"]					)
     print(SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       List[str]	   =     dataset.filter(lambda SCREAMING_SNAKE_CASE					: (len(x["question"]					) + len(x["context"]					)) < 4 * 4096					)
     _lowerCAmelCase :       Any	   =     short_validation_dataset.filter(lambda SCREAMING_SNAKE_CASE					: x["category"] != "null"					)
     short_validation_dataset

     _lowerCAmelCase :       str	   =     'vasudevgupta/flax-bigbird-natural-questions'
     _lowerCAmelCase :       Any	   =     FlaxBigBirdForNaturalQuestions.from_pretrained(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Tuple	   =     BigBirdTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE					)

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

     def evaluate(SCREAMING_SNAKE_CASE					):
          # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
          _lowerCAmelCase :       Tuple	   =     tokenizer(
              example["question"]		,				example["context"]		,				return_tensors="np"		,				max_length=4096		,				padding="max_length"		,				truncation=SCREAMING_SNAKE_CASE		,				)

          _lowerCAmelCase :       Union[str, Any]	   =     forward(**SCREAMING_SNAKE_CASE					)

          _lowerCAmelCase :       Optional[Any]	   =     CATEGORY_MAPPING[category.item()]

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

          if predicted_category in ["yes", "no", "null"]:
               _lowerCAmelCase :       List[str]	   =     [predicted_category]
               _lowerCAmelCase :       List[Any]	   =     example['output'] == example['targets']
               _lowerCAmelCase :       List[str]	   =     predicted_category != 'null'
               return example

          _lowerCAmelCase :       int	   =     38 if predicted_category == 'short' else 1024
          _lowerCAmelCase :       Optional[Any]	   =     get_best_valid_start_end_idx(
              start_scores[0]		,				end_scores[0]		,				top_k=8		,				max_size=SCREAMING_SNAKE_CASE					)

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

          _lowerCAmelCase :       Optional[int]	   =     expand_to_aliases(example["targets"]		,				make_sub_answers=SCREAMING_SNAKE_CASE					)
          _lowerCAmelCase :       List[str]	   =     expand_to_aliases(example["output"]					)

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

          # if there is a common element, it's a exact match
          _lowerCAmelCase :       str	   =     len(list(answers & predictions					)					) > 0
          _lowerCAmelCase :       List[Any]	   =     predicted_category != 'null' and len(SCREAMING_SNAKE_CASE					) > 0

          return example

     _lowerCAmelCase :       int	   =     short_validation_dataset.map(SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       Tuple	   =     len(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       List[str]	   =     len(short_validation_dataset.filter(lambda SCREAMING_SNAKE_CASE					: x["match"] == 1					)					)
     print("EM score:"		,				(matched / total) * 100		,				"%"					)


if __name__ == "__main__":
  main()










import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def   snake_case_	(    snake_case	)    ->							List[str]:
       lowercase__:  Any         =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]	)
       lowercase__:  Optional[Any]         =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]	)

       lowercase__:  Optional[int]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       lowercase__:  List[Any]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       # modify the pix within [0, 255]
       lowercase__:  Optional[Any]         =  dst_x * 2_55 / np.max(snake_case	)
       lowercase__:  Dict         =  dst_y * 2_55 / np.max(snake_case	)

       lowercase__:  Tuple         =  np.sqrt((np.square(snake_case	)) + (np.square(snake_case	))	)
       lowercase__:  str         =  dst_xy * 2_55 / np.max(snake_case	)
       lowercase__:  Tuple         =  dst_xy.astype(np.uinta	)

       lowercase__:  List[Any]         =  np.arctana(snake_case     ,				snake_case	)
       return dst, theta


if __name__ == "__main__":
 # read original image
 __lowerCAmelCase									=				imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 __lowerCAmelCase									=				cvtColor(img, COLOR_BGR2GRAY)

 __lowerCAmelCase			,__lowerCAmelCase									=				sobel_filter(gray)

 # show result images
 imshow('''sobel filter''', sobel_grad)
 imshow('''sobel theta''', sobel_theta)
 waitKey(0)


