




"""simple docstring"""

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

from transformers import BigBirdTokenizerFast


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



def   UpperCamelCase__     (		lowercase__  :     List[str]       ,     lowercase__  :     List[str]=0       ,     lowercase__  :     Union[str, Any]=None   ):
		return [" ".join(x.split(" "   )[begin:end]   ) for x in answers if len(x.split(" "   )   ) > 1]



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



def   UpperCamelCase__     (		lowercase__  :     List[Any]       ,     lowercase__  :     Tuple       ,     lowercase__  :     List[Any]=1       ,     lowercase__  :     Any=100   ):
		snake_case			,				snake_case		:						str								=				jax.lax.top_k(lowercase__       ,     lowercase__   )
		snake_case			,				snake_case		:						Any								=				jax.lax.top_k(lowercase__       ,     lowercase__   )

		snake_case		:						List[str]								=				best_end_idx[:, None] - best_start_idx[None, :]
		snake_case		:						Union[str, Any]								=				jnp.logical_or(widths < 0       ,     widths > max_size   )
		snake_case		:						List[str]								=				(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
		snake_case		:						str								=				jnp.argmax(lowercase__   ).item()

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



def   UpperCamelCase__     (		lowercase__  :     str   ):
		snake_case		:						Optional[Any]								=				sample["question"]["text"]
		snake_case		:						Any								=				sample["document"]["tokens"]["token"]
		snake_case		:						Optional[Any]								=				sample["document"]["tokens"]["is_html"]
		snake_case		:						int								=				sample["annotations"]["long_answer"]
		snake_case		:						List[Any]								=				sample["annotations"]["short_answers"]

		snake_case		:						Optional[int]								=				" ".join([context[i] for i in range(len(lowercase__   )   ) 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",
						}

		snake_case		:						Union[str, Any]								=				[]
		for s in short_answers:
				short_targets.extend(s["text"]   )
		snake_case		:						Optional[int]								=				list(set(lowercase__   )   )

		snake_case		:						Any								=				[]
		for s in long_answers:
				if s["start_token"] == -1:
						continue
				snake_case		:						Union[str, Any]								=				context[s["start_token"] : s["end_token"]]
				snake_case		:						Optional[int]								=				is_html[s["start_token"] : s["end_token"]]
				snake_case		:						Optional[Any]								=				" ".join([answer[i] for i in range(len(lowercase__   )   ) if not html[i]]   )
				if new_answer not in long_targets:
						long_targets.append(lowercase__   )

		snake_case		:						Any								=				"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__     (		):
		snake_case		:						List[str]								=				load_from_disk("natural-questions-validation"   )
		snake_case		:						List[str]								=				dataset.map(lowercase__   ).remove_columns(["annotations", "document", "id"]   )
		print(lowercase__   )

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

		snake_case		:						Dict								=				"vasudevgupta/flax-bigbird-natural-questions"
		snake_case		:						int								=				FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase__   )
		snake_case		:						Tuple								=				BigBirdTokenizerFast.from_pretrained(lowercase__   )

		@jax.jit
		def forward(*lowercase__  :     Union[str, Any]       ,     **lowercase__  :     int   ):
				snake_case			,				snake_case			,				snake_case		:						Tuple								=				model(*lowercase__       ,     **lowercase__   )
				return start_logits, end_logits, jnp.argmax(lowercase__       ,     axis=-1   )

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

				snake_case			,				snake_case			,				snake_case		:						List[str]								=				forward(**lowercase__   )

				snake_case		:						int								=				CATEGORY_MAPPING[category.item()]

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

				if predicted_category in ["yes", "no", "null"]:
						snake_case		:						Dict								=				[predicted_category]
						snake_case		:						Tuple								=				example["output"] == example["targets"]
						snake_case		:						List[str]								=				predicted_category != "null"
						return example

				snake_case		:						Optional[int]								=				38 if predicted_category == "short" else 1024
				snake_case			,				snake_case		:						int								=				get_best_valid_start_end_idx(
				    start_scores[0]       ,     end_scores[0]       ,     top_k=8       ,     max_size=lowercase__   )

				snake_case		:						List[Any]								=				inputs["input_ids"][0].tolist()
				snake_case		:						int								=				[tokenizer.decode(input_ids[start_score : end_score + 1]   )]

				snake_case		:						Tuple								=				expand_to_aliases(example["targets"]       ,     make_sub_answers=lowercase__   )
				snake_case		:						Optional[Any]								=				expand_to_aliases(example["output"]   )

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

				# if there is a common element, it's a exact match
				snake_case		:						Union[str, Any]								=				len(list(answers & predictions   )   ) > 0
				snake_case		:						Optional[int]								=				predicted_category != "null" and len(lowercase__   ) > 0

				return example

		snake_case		:						Optional[int]								=				short_validation_dataset.map(lowercase__   )

		snake_case		:						List[Any]								=				len(lowercase__   )
		snake_case		:						Any								=				len(short_validation_dataset.filter(lambda lowercase__   : x["match"] == 1   )   )
		print("EM score:"       ,     (matched / total) * 100       ,     "%"   )


if __name__ == "__main__":
							main()







"""simple docstring"""

import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class 			lowerCamelCase__    :


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						int								=				0
					# max greyscale value for #FFFFFF
					snake_case		:						List[str]								=				int(self.get_greyscale(255				, 255				, 255							)							)

					if not self.min_threshold < threshold < self.max_threshold:
							snake_case		:						Union[str, Any]								=				F'''Factor value should be from 0 to {self.max_threshold}'''
							raise ValueError(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				input_img
					snake_case		:						Optional[Any]								=				threshold
					snake_case			,				snake_case		:						List[str]								=				self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					snake_case		:						Union[str, Any]								=				[
					    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
					]
					snake_case		:						Tuple								=				np.ones((self.width, self.height, 3)				, np.uinta							) * 255


			@classmethod
			def 							lowerCamelCase_				(     cls				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					for y in range(self.height							):
							for x in range(self.width							):
									snake_case		:						Dict								=				int(self.get_greyscale(*self.input_img[y][x]							)							)
									if self.threshold > greyscale + self.error_table[y][x]:
											snake_case		:						List[str]								=				(0, 0, 0)
											snake_case		:						List[Any]								=				greyscale + self.error_table[x][y]
									else:
											snake_case		:						int								=				(255, 255, 255)
											snake_case		:						Tuple								=				greyscale + self.error_table[x][y] - 255
									self.error_table[y][x + 1] += int(8 / 32 * current_error							)
									self.error_table[y][x + 2] += int(4 / 32 * current_error							)
									self.error_table[y + 1][x] += int(8 / 32 * current_error							)
									self.error_table[y + 1][x + 1] += int(4 / 32 * current_error							)
									self.error_table[y + 1][x + 2] += int(2 / 32 * current_error							)
									self.error_table[y + 1][x - 1] += int(4 / 32 * current_error							)
									self.error_table[y + 1][x - 2] += int(2 / 32 * current_error							)


if __name__ == "__main__":
							# create Burke's instances with original images in greyscale
							__A 		=			[
							    Burkes(imread("image_data/lena.jpg", 1), threshold)
							    for threshold in (1, 126, 130, 140)
							]

							for burkes in burkes_instances:
														burkes.process()

							for burkes in burkes_instances:
														imshow(
														    f'Original image with dithering threshold: {burkes.threshold}',
														    burkes.output_img,
														)

							waitKey(0)
							destroyAllWindows()

