




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

from transformers import BigBirdTokenizerFast


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





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=0						,			UpperCamelCase__=None   )     ->     Optional[int]:







				'''simple docstring'''


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





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=False   )     ->     Optional[int]:







				'''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					=  (
								    given_answers + get_sub_answers(_SCREAMING_SNAKE_CASE						,			begin=1   ) + get_sub_answers(_SCREAMING_SNAKE_CASE						,			end=-1   )
								)
				__lowerCAmelCase					=  []
				for answer in given_answers:
								__lowerCAmelCase					=  answer.replace("""_"""						,			""" """   ).lower()
								__lowerCAmelCase					=  """""".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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=1						,			UpperCamelCase__=1_00   )     ->     str:







				'''simple docstring'''


				__lowerCAmelCase	,				__lowerCAmelCase					=  jax.lax.top_k(_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE   )
				__lowerCAmelCase	,				__lowerCAmelCase					=  jax.lax.top_k(_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE   )

				__lowerCAmelCase					=  best_end_idx[:, None] - best_start_idx[None, :]
				__lowerCAmelCase					=  jnp.logical_or(widths < 0						,			widths > max_size   )
				__lowerCAmelCase					=  (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
				__lowerCAmelCase					=  jnp.argmax(_SCREAMING_SNAKE_CASE   ).item()

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





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  sample["""question"""]["""text"""]
				__lowerCAmelCase					=  sample["""document"""]["""tokens"""]["""token"""]
				__lowerCAmelCase					=  sample["""document"""]["""tokens"""]["""is_html"""]
				__lowerCAmelCase					=  sample["""annotations"""]["""long_answer"""]
				__lowerCAmelCase					=  sample["""annotations"""]["""short_answers"""]

				__lowerCAmelCase					=  """ """.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					=  []
				for s in short_answers:
								short_targets.extend(s["""text"""]   )
				__lowerCAmelCase					=  list(set(_SCREAMING_SNAKE_CASE   )   )

				__lowerCAmelCase					=  []
				for s in long_answers:
								if s["start_token"] == -1:
												continue
								__lowerCAmelCase					=  context[s["""start_token"""] : s["""end_token"""]]
								__lowerCAmelCase					=  is_html[s["""start_token"""] : s["""end_token"""]]
								__lowerCAmelCase					=  """ """.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					=  """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  (						)     ->     int:







				'''simple docstring'''


				__lowerCAmelCase					=  load_from_disk("""natural-questions-validation"""   )
				__lowerCAmelCase					=  dataset.map(_SCREAMING_SNAKE_CASE   ).remove_columns(["""annotations""", """document""", """id"""]   )
				print(_SCREAMING_SNAKE_CASE   )

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

				__lowerCAmelCase					=  """vasudevgupta/flax-bigbird-natural-questions"""
				__lowerCAmelCase					=  FlaxBigBirdForNaturalQuestions.from_pretrained(_SCREAMING_SNAKE_CASE   )
				__lowerCAmelCase					=  BigBirdTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE   )

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

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

								__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  forward(**_SCREAMING_SNAKE_CASE   )

								__lowerCAmelCase					=  CATEGORY_MAPPING[category.item()]

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

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

								__lowerCAmelCase					=  38 if predicted_category == """short""" else 10_24
								__lowerCAmelCase	,				__lowerCAmelCase					=  get_best_valid_start_end_idx(
								    start_scores[0]						,			end_scores[0]						,			top_k=8						,			max_size=_SCREAMING_SNAKE_CASE   )

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

								__lowerCAmelCase					=  expand_to_aliases(example["""targets"""]						,			make_sub_answers=_SCREAMING_SNAKE_CASE   )
								__lowerCAmelCase					=  expand_to_aliases(example["""output"""]   )

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

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

								return example

				__lowerCAmelCase					=  short_validation_dataset.map(_SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
	main()







'''simple docstring'''







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_snake_case					: Union[str, Any]     			=					180



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			k_size // 2
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_SCREAMING_SNAKE_CASE					=			(
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE	) + np.square(_SCREAMING_SNAKE_CASE	)) / (2 * np.square(_SCREAMING_SNAKE_CASE	))	)
						)
						return g



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		_SCREAMING_SNAKE_CASE					=			gradient_direction[row, col]

																		if (
																		    0 <= direction < PI / 8
																		    or 15 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    PI / 8 <= direction < 3 * PI / 8
																		    or 9 * PI / 8 <= direction < 11 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col + 1]
																								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    3 * PI / 8 <= direction < 5 * PI / 8
																		    or 11 * PI / 8 <= direction < 13 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col]
																								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

																		elif (
																		    5 * PI / 8 <= direction < 7 * PI / 8
																		    or 13 * PI / 8 <= direction < 15 * PI / 8
																		):
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row - 1, col - 1]
																								_SCREAMING_SNAKE_CASE					=			sobel_grad[row + 1, col + 1]
																								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																														_SCREAMING_SNAKE_CASE					=			sobel_grad[row, col]

						return destination



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		if destination[row, col] >= threshold_high:
																								_SCREAMING_SNAKE_CASE					=			strong
																		elif destination[row, col] <= threshold_low:
																								_SCREAMING_SNAKE_CASE					=			0
																		else:
																								_SCREAMING_SNAKE_CASE					=			weak



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Any	):
						for row in range(1						,      image_shape[0]	):
												for col in range(1						,      image_shape[1]	):
																		if destination[row, col] == weak:
																								if 255 in (
																								    destination[row, col + 1],
																								    destination[row, col - 1],
																								    destination[row - 1, col],
																								    destination[row + 1, col],
																								    destination[row - 1, col - 1],
																								    destination[row + 1, col - 1],
																								    destination[row - 1, col + 1],
																								    destination[row + 1, col + 1],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: List[str]=15						,      _SCREAMING_SNAKE_CASE		: List[Any]=30						,      _SCREAMING_SNAKE_CASE		: Optional[int]=128						,      _SCREAMING_SNAKE_CASE		: List[Any]=255	):
						# gaussian_filter
						_SCREAMING_SNAKE_CASE					=			img_convolve(_SCREAMING_SNAKE_CASE						,      gen_gaussian_kernel(9						,      sigma=1.4	)	)
						# get the gradient and degree by sobel_filter
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			sobel_filter(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			PI + np.radadeg(_SCREAMING_SNAKE_CASE	)

						_SCREAMING_SNAKE_CASE					=			suppress_non_maximum(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						detect_high_low_threshold(
						    image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						track_edge(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						return destination


if __name__ == "__main__":
						# read original image in gray mode
						_snake_case					: Any     			=					cva.imread(R"""../image_data/lena.jpg""", 0)
						# canny edge detection
						_snake_case					: int     			=					canny(lena)
						cva.imshow("""canny""", canny_destination)
						cva.waitKey(0)