
import os

import jsonlines
import numpy as np
from tqdm import tqdm


__A				=2_0_4_8
__A				=4_0_9_6
__A				=4_2
__A				=os.environ.pop('''PROCESS_TRAIN''', '''false''')
__A				={'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				def choose_first(lowerCamelCase__	, lowerCamelCase__=False    ):
								assert isinstance(lowerCamelCase__	, lowerCamelCase__    )
								if len(lowerCamelCase__    ) == 1:
												lowerCamelCase_						=			answer[0]
												return {k: [answer[k]] for k in answer} if is_long_answer else answer
								for a in answer:
												if is_long_answer:
																lowerCamelCase_						=			{k: [a[k]] for k in a}
												if len(a["start_token"]    ) > 0:
																break
								return a

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

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

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

				return answer





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__=False    ):
				lowerCamelCase_						=			_get_single_answer(lowerCamelCase__    )
				# 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
								lowerCamelCase_						=			example["document"]["tokens"]
								lowerCamelCase_						=			[]
								for i in range(len(doc["token"]    )    ):
												if not doc["is_html"][i]:
																context.append(doc["token"][i]    )
								return {
								    "context": " ".join(lowerCamelCase__    ),
								    "answer": {
								        "start_token": -1_0_0,  # ignore index in cross-entropy
								        "end_token": -1_0_0,  # 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

				lowerCamelCase_						=			["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

				lowerCamelCase_						=			example["document"]["tokens"]
				lowerCamelCase_						=			answer["start_token"]
				lowerCamelCase_						=			answer["end_token"]

				lowerCamelCase_						=			[]
				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
				lowerCamelCase_						=			" ".join(context[start_token:end_token]    )

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

				return {
				    "context": " ".join(lowerCamelCase__    ),
				    "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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=2_0_4_8	, lowerCamelCase__=4_0_9_6	, lowerCamelCase__=True    ):
				# overlap will be of doc_stride - q_len

				lowerCamelCase_						=			get_context_and_ans(lowerCamelCase__	, assertion=lowerCamelCase__    )
				lowerCamelCase_						=			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"],
								    },
								}

				lowerCamelCase_						=			tokenizer(example["question"]["text"]	, out["context"]    ).input_ids
				lowerCamelCase_						=			input_ids.index(tokenizer.sep_token_id    ) + 1

				# return yes/no
				if answer["category"][0] in ["yes", "no"]:  # category is list with one element
								lowerCamelCase_						=			[]
								lowerCamelCase_						=			[]
								lowerCamelCase_						=			input_ids[:q_len]
								lowerCamelCase_						=			range(lowerCamelCase__	, len(lowerCamelCase__    )	, max_length - doc_stride    )
								for i in doc_start_indices:
												lowerCamelCase_						=			i + max_length - q_len
												lowerCamelCase_						=			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": [-1_0_0] * len(lowerCamelCase__    ),
								        "end_token": [-1_0_0] * len(lowerCamelCase__    ),
								        "category": category,
								    },
								}

				lowerCamelCase_						=			out["context"].split()
				lowerCamelCase_						=			splitted_context[answer["end_token"]]
				lowerCamelCase_						=			len(
				    tokenizer(
				        " ".join(splitted_context[: answer["start_token"]]    )	, add_special_tokens=lowerCamelCase__	, ).input_ids    )
				lowerCamelCase_						=			len(
				    tokenizer(" ".join(splitted_context[: answer["end_token"]]    )	, add_special_tokens=lowerCamelCase__    ).input_ids    )

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

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

				lowerCamelCase_						=			input_ids[answer["start_token"] : answer["end_token"] + 1]  # right & left are inclusive
				lowerCamelCase_						=			answer["start_token"]
				lowerCamelCase_						=			answer["end_token"]

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

				if len(lowerCamelCase__    ) <= 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"],
								    },
								}

				lowerCamelCase_						=			input_ids[:q_len]
				lowerCamelCase_						=			range(lowerCamelCase__	, len(lowerCamelCase__    )	, max_length - doc_stride    )

				lowerCamelCase_						=			[]
				lowerCamelCase_						=			[]
				lowerCamelCase_						=			[]
				lowerCamelCase_						=			[]  # null, yes, no, long, short
				for i in doc_start_indices:
								lowerCamelCase_						=			i + max_length - q_len
								lowerCamelCase_						=			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:
												lowerCamelCase_						=			start_token - i + q_len
												lowerCamelCase_						=			end_token - i + q_len
												answers_category.append(answer["category"][0]    )  # ["short"] -> "short"
								else:
												lowerCamelCase_						=			-1_0_0
												lowerCamelCase_						=			-1_0_0
												answers_category.append("null"    )
								lowerCamelCase_						=			inputs[-1][start_token : end_token + 1]

								answers_start_token.append(lowerCamelCase__    )
								answers_end_token.append(lowerCamelCase__    )
								if assertion:
												if new != old and new != [tokenizer.cls_token_id]:
																print("ISSUE in strided for ID:"	, example["id"]    )
																print("New:"	, tokenizer.decode(lowerCamelCase__    )    )
																print("Old:"	, tokenizer.decode(lowerCamelCase__    )	, 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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=2_0_4_8	, lowerCamelCase__=4_0_9_6	, lowerCamelCase__=False    ):
				lowerCamelCase_						=			get_strided_contexts_and_ans(
				    lowerCamelCase__	, lowerCamelCase__	, doc_stride=lowerCamelCase__	, max_length=lowerCamelCase__	, assertion=lowerCamelCase__	, )

				return example





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				with jsonlines.open(lowerCamelCase__	, "a"    ) as writer:
								for example in tqdm(lowerCamelCase__	, total=len(lowerCamelCase__    )	, desc="Saving samples ... "    ):
												lowerCamelCase_						=			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				=load_dataset('''natural_questions''')
			__A				=BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')

			__A				=data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

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

			np.random.seed(SEED)
			__A				='''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
			save_to_disk(data, file_name=cache_file_name)




from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@staticmethod
	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    lowercase )				->       int:
					raise NotImplementedError()



	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					raise NotImplementedError()


