





"""simple docstring"""




import os

import jsonlines
import numpy as np
from tqdm import tqdm


a						: Dict     	=			2048
a						: int     	=			4096
a						: List[str]     	=			42
a						: Dict     	=			os.environ.pop('''PROCESS_TRAIN''', '''false''')
a						: Dict     	=			{'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[int]					) ->List[Any]:






	'''simple docstring'''






	def choose_first(_lowercase						:    Tuple    ,       _lowercase						:    Any=False					):
		assert isinstance(_lowercase    ,       _lowercase					)
		if len(_lowercase					) == 1:
			a      :		Union[str, Any]	=				answer[0]
			return {k: [answer[k]] for k in answer} if is_long_answer else answer
		for a in answer:
			if is_long_answer:
				a      :		int	=				{k: [a[k]] for k in a}
			if len(a["start_token"]					) > 0:
				break
		return a

	a      :		str	=				{"id": example["id"]}
	a      :		Tuple	=				example["annotations"]
	a      :		Union[str, Any]	=				annotation["yes_no_answer"]
	if 0 in yes_no_answer or 1 in yes_no_answer:
		a      :		List[str]	=				["yes"] if 1 in yes_no_answer else ["no"]
		a      :		List[str]	=				[]
		a      :		str	=				[]
		a      :		Any	=				["<cls>"]
	else:
		a      :		Optional[int]	=				["short"]
		a      :		List[Any]	=				choose_first(annotation["short_answers"]					)
		if len(out["start_token"]					) == 0:
			# answer will be long if short is not available
			a      :		List[str]	=				["long"]
			a      :		List[str]	=				choose_first(annotation["long_answer"]    ,       is_long_answer=_lowercase					)
			a      :		Optional[int]	=				[]
		answer.update(_lowercase					)

	# disregard some samples
	if len(answer["start_token"]					) > 1 or answer["start_token"] == answer["end_token"]:
		a      :		int	=				True
	else:
		a      :		Dict	=				False

	a      :		Optional[Any]	=				["start_token", "end_token", "start_byte", "end_byte", "text"]
	if not all(isinstance(answer[k]    ,       _lowercase					) for k in cols					):
		raise ValueError("Issue in ID"    ,       example["id"]					)

	return answer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]    ,       _lowercase						:    Optional[int]=False					) ->int:






	'''simple docstring'''






	a      :		Optional[int]	=				_get_single_answer(_lowercase					)
	# bytes are of no use
	del answer["start_byte"]
	del answer["end_byte"]

	# handle yes_no answers explicitly
	if answer["category"][0] in ["yes", "no"]:  # category is list with one element
		a      :		Tuple	=				example["document"]["tokens"]
		a      :		List[str]	=				[]
		for i in range(len(doc["token"]					)					):
			if not doc["is_html"][i]:
				context.append(doc["token"][i]					)
		return {
		    "context": " ".join(_lowercase					),
		    "answer": {
		        "start_token": -100,  # ignore index in cross-entropy
		        "end_token": -100,  # ignore index in cross-entropy
		        "category": answer["category"],
		        "span": answer["category"],  # extra
		    },
		}

	# later, help in removing all no answers
	if answer["start_token"] == [-1]:
		return {
		    "context": "None",
		    "answer": {
		        "start_token": -1,
		        "end_token": -1,
		        "category": "null",
		        "span": "None",  # extra
		    },
		}

	# handling normal samples

	a      :		str	=				["start_token", "end_token"]
	answer.update({k: answer[k][0] if len(answer[k]					) > 0 else answer[k] for k in cols}					)  # e.g. [10] == 10

	a      :		List[Any]	=				example["document"]["tokens"]
	a      :		List[str]	=				answer["start_token"]
	a      :		Any	=				answer["end_token"]

	a      :		Union[str, Any]	=				[]
	for i in range(len(doc["token"]					)					):
		if not doc["is_html"][i]:
			context.append(doc["token"][i]					)
		else:
			if answer["start_token"] > i:
				start_token -= 1
			if answer["end_token"] > i:
				end_token -= 1
	a      :		Union[str, Any]	=				" ".join(context[start_token:end_token]					)

	# checking above code
	if assertion:
		a      :		int	=				doc["is_html"][answer["start_token"] : answer["end_token"]]
		a      :		Union[str, Any]	=				doc["token"][answer["start_token"] : answer["end_token"]]
		a      :		List[Any]	=				" ".join([old[i] for i in range(len(_lowercase					)					) if not is_html[i]]					)
		if new != old:
			print("ID:"    ,       example["id"]					)
			print("New:"    ,       _lowercase    ,       end="\n"					)
			print("Old:"    ,       _lowercase    ,       end="\n\n"					)

	return {
	    "context": " ".join(_lowercase					),
	    "answer": {
	        "start_token": start_token,
	        "end_token": end_token - 1,  # this makes it inclusive
	        "category": answer["category"],  # either long or short
	        "span": new,  # extra
	    },
	}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Tuple    ,       _lowercase						:    Tuple    ,       _lowercase						:    Any=2048    ,       _lowercase						:    Optional[Any]=4096    ,       _lowercase						:    Union[str, Any]=True					) ->Tuple:






	'''simple docstring'''






	a      :		List[Any]	=				get_context_and_ans(_lowercase    ,       assertion=_lowercase					)
	a      :		List[Any]	=				out["answer"]

	# later, removing these samples
	if answer["start_token"] == -1:
		return {
		    "example_id": example["id"],
		    "input_ids": [[-1]],
		    "labels": {
		        "start_token": [-1],
		        "end_token": [-1],
		        "category": ["null"],
		    },
		}

	a      :		Dict	=				tokenizer(example["question"]["text"]    ,       out["context"]					).input_ids
	a      :		Any	=				input_ids.index(tokenizer.sep_token_id					) + 1

	# return yes/no
	if answer["category"][0] in ["yes", "no"]:  # category is list with one element
		a      :		Union[str, Any]	=				[]
		a      :		str	=				[]
		a      :		Any	=				input_ids[:q_len]
		a      :		Optional[int]	=				range(_lowercase    ,       len(_lowercase					)    ,       max_length - doc_stride					)
		for i in doc_start_indices:
			a      :		int	=				i + max_length - q_len
			a      :		Optional[int]	=				input_ids[i:end_index]
			inputs.append(q_indices + slice					)
			category.append(answer["category"][0]					)
			if slice[-1] == tokenizer.sep_token_id:
				break

		return {
		    "example_id": example["id"],
		    "input_ids": inputs,
		    "labels": {
		        "start_token": [-100] * len(_lowercase					),
		        "end_token": [-100] * len(_lowercase					),
		        "category": category,
		    },
		}

	a      :		Dict	=				out["context"].split()
	a      :		Optional[Any]	=				splitted_context[answer["end_token"]]
	a      :		Optional[Any]	=				len(
	    tokenizer(
	        " ".join(splitted_context[: answer["start_token"]]					)    ,       add_special_tokens=_lowercase    ,       ).input_ids					)
	a      :		List[Any]	=				len(
	    tokenizer(" ".join(splitted_context[: answer["end_token"]]					)    ,       add_special_tokens=_lowercase					).input_ids					)

	answer["start_token"] += q_len
	answer["end_token"] += q_len

	# fixing end token
	a      :		Optional[int]	=				len(tokenizer(_lowercase    ,       add_special_tokens=_lowercase					).input_ids					)
	if num_sub_tokens > 1:
		answer["end_token"] += num_sub_tokens - 1

	a      :		List[str]	=				input_ids[answer["start_token"] : answer["end_token"] + 1]  # right & left are inclusive
	a      :		Optional[Any]	=				answer["start_token"]
	a      :		Dict	=				answer["end_token"]

	if assertion:
		a      :		Optional[int]	=				tokenizer.decode(_lowercase					)
		if answer["span"] != new:
			print("ISSUE IN TOKENIZATION"					)
			print("OLD:"    ,       answer["span"]					)
			print("NEW:"    ,       _lowercase    ,       end="\n\n"					)

	if len(_lowercase					) <= max_length:
		return {
		    "example_id": example["id"],
		    "input_ids": [input_ids],
		    "labels": {
		        "start_token": [answer["start_token"]],
		        "end_token": [answer["end_token"]],
		        "category": answer["category"],
		    },
		}

	a      :		Optional[Any]	=				input_ids[:q_len]
	a      :		Union[str, Any]	=				range(_lowercase    ,       len(_lowercase					)    ,       max_length - doc_stride					)

	a      :		str	=				[]
	a      :		List[Any]	=				[]
	a      :		Union[str, Any]	=				[]
	a      :		Optional[int]	=				[]  # null, yes, no, long, short
	for i in doc_start_indices:
		a      :		Dict	=				i + max_length - q_len
		a      :		List[str]	=				input_ids[i:end_index]
		inputs.append(q_indices + slice					)
		assert len(inputs[-1]					) <= max_length, "Issue in truncating length"

		if start_token >= i and end_token <= end_index - 1:
			a      :		Optional[int]	=				start_token - i + q_len
			a      :		int	=				end_token - i + q_len
			answers_category.append(answer["category"][0]					)  # ["short"] -> "short"
		else:
			a      :		List[Any]	=				-100
			a      :		Optional[int]	=				-100
			answers_category.append("null"					)
		a      :		Optional[int]	=				inputs[-1][start_token : end_token + 1]

		answers_start_token.append(_lowercase					)
		answers_end_token.append(_lowercase					)
		if assertion:
			if new != old and new != [tokenizer.cls_token_id]:
				print("ISSUE in strided for ID:"    ,       example["id"]					)
				print("New:"    ,       tokenizer.decode(_lowercase					)					)
				print("Old:"    ,       tokenizer.decode(_lowercase					)    ,       end="\n\n"					)
		if slice[-1] == tokenizer.sep_token_id:
			break

	return {
	    "example_id": example["id"],
	    "input_ids": inputs,
	    "labels": {
	        "start_token": answers_start_token,
	        "end_token": answers_end_token,
	        "category": answers_category,
	    },
	}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Tuple    ,       _lowercase						:    str    ,       _lowercase						:    Any=2048    ,       _lowercase						:    str=4096    ,       _lowercase						:    Dict=False					) ->Union[str, Any]:






	'''simple docstring'''






	a      :		str	=				get_strided_contexts_and_ans(
	    _lowercase    ,       _lowercase    ,       doc_stride=_lowercase    ,       max_length=_lowercase    ,       assertion=_lowercase    ,       )

	return example






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[str]    ,       _lowercase						:    Union[str, Any]					) ->Optional[Any]:






	'''simple docstring'''






	with jsonlines.open(_lowercase    ,       "a"					) as writer:
		for example in tqdm(_lowercase    ,       total=len(_lowercase					)    ,       desc="Saving samples ... "					):
			a      :		Dict	=				example["labels"]
			for ids, start, end, cat in zip(
			    example["input_ids"]    ,       labels["start_token"]    ,       labels["end_token"]    ,       labels["category"]    ,       ):
				if start == -1 and end == -1:
					continue  # leave waste samples with no answer
				if cat == "null" and np.random.rand() < 0.6:
					continue  # removing 50 % samples
				writer.write(
				    {
				        "input_ids": ids,
				        "start_token": start,
				        "end_token": end,
				        "category": CATEGORY_MAPPING[cat],
				    }					)


if __name__ == "__main__":
		from datasets import load_dataset

		from transformers import BigBirdTokenizer

		a						: List[Any]     	=			load_dataset('''natural_questions''')
		a						: Union[str, Any]     	=			BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')

		a						: Any     	=			data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

		a						: Optional[int]     	=			{
		    '''tokenizer''': tokenizer,
		    '''doc_stride''': DOC_STRIDE,
		    '''max_length''': MAX_LENGTH,
		    '''assertion''': False,
		}
		a						: str     	=			data.map(prepare_inputs, fn_kwargs=fn_kwargs)
		a						: Dict     	=			data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
		print(data)

		np.random.seed(SEED)
		a						: Union[str, Any]     	=			'''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
		save_to_disk(data, file_name=cache_file_name)









"""simple docstring"""




from __future__ import annotations






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list[int]    ,       _lowercase						:    int					) ->int:






	'''simple docstring'''






	if len(_lowercase					) < k or k < 0:
		raise ValueError("Invalid Input"					)
	a      :		Optional[Any]	=				sum(array[:k]					)
	for i in range(len(_lowercase					) - k					):
		a      :		Optional[Any]	=				current_sum - array[i] + array[i + k]
		a      :		Union[str, Any]	=				max(_lowercase    ,       _lowercase					)
	return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		a						: Any     	=			[randint(-1000, 1000) for i in range(100)]
		a						: List[str]     	=			randint(0, 110)
		print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


