
'''simple docstring'''







import os

import jsonlines
import numpy as np
from tqdm import tqdm


_snake_case				:   Optional[Any]     =				2_048
_snake_case				:   Any     =				4_096
_snake_case				:   List[str]     =				42
_snake_case				:   int     =				os.environ.pop("""PROCESS_TRAIN""", """false""")
_snake_case				:   List[str]     =				{"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}



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

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

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

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

						return answer



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=False  ):
						_SCREAMING_SNAKE_CASE				= _get_single_answer(_SCREAMING_SNAKE_CASE  )
						# 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
												_SCREAMING_SNAKE_CASE				= example["document"]["tokens"]
												_SCREAMING_SNAKE_CASE				= []
												for i in range(len(doc["token"]  )  ):
																		if not doc["is_html"][i]:
																								context.append(doc["token"][i]  )
												return {
												    "context": " ".join(_SCREAMING_SNAKE_CASE  ),
												    "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

						_SCREAMING_SNAKE_CASE				= ["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

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

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

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

						return {
						    "context": " ".join(_SCREAMING_SNAKE_CASE  ),
						    "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 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   int=2048	,			_SCREAMING_SNAKE_CASE		:   Tuple=4096	,			_SCREAMING_SNAKE_CASE		:   str=True  ):
						# overlap will be of doc_stride - q_len

						_SCREAMING_SNAKE_CASE				= get_context_and_ans(_SCREAMING_SNAKE_CASE	,			assertion=_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= 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"],
												    },
												}

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

						# return yes/no
						if answer["category"][0] in ["yes", "no"]:  # category is list with one element
												_SCREAMING_SNAKE_CASE				= []
												_SCREAMING_SNAKE_CASE				= []
												_SCREAMING_SNAKE_CASE				= input_ids[:q_len]
												_SCREAMING_SNAKE_CASE				= range(_SCREAMING_SNAKE_CASE	,			len(_SCREAMING_SNAKE_CASE  )	,			max_length - doc_stride  )
												for i in doc_start_indices:
																		_SCREAMING_SNAKE_CASE				= i + max_length - q_len
																		_SCREAMING_SNAKE_CASE				= 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(_SCREAMING_SNAKE_CASE  ),
												        "end_token": [-100] * len(_SCREAMING_SNAKE_CASE  ),
												        "category": category,
												    },
												}

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

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

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

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

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

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

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

						_SCREAMING_SNAKE_CASE				= []
						_SCREAMING_SNAKE_CASE				= []
						_SCREAMING_SNAKE_CASE				= []
						_SCREAMING_SNAKE_CASE				= []  # null, yes, no, long, short
						for i in doc_start_indices:
												_SCREAMING_SNAKE_CASE				= i + max_length - q_len
												_SCREAMING_SNAKE_CASE				= 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:
																		_SCREAMING_SNAKE_CASE				= start_token - i + q_len
																		_SCREAMING_SNAKE_CASE				= end_token - i + q_len
																		answers_category.append(answer["category"][0]  )  # ["short"] -> "short"
												else:
																		_SCREAMING_SNAKE_CASE				= -100
																		_SCREAMING_SNAKE_CASE				= -100
																		answers_category.append("null"  )
												_SCREAMING_SNAKE_CASE				= inputs[-1][start_token : end_token + 1]

												answers_start_token.append(_SCREAMING_SNAKE_CASE  )
												answers_end_token.append(_SCREAMING_SNAKE_CASE  )
												if assertion:
																		if new != old and new != [tokenizer.cls_token_id]:
																								print("ISSUE in strided for ID:"	,			example["id"]  )
																								print("New:"	,			tokenizer.decode(_SCREAMING_SNAKE_CASE  )  )
																								print("Old:"	,			tokenizer.decode(_SCREAMING_SNAKE_CASE  )	,			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 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Dict=2048	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=4096	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=False  ):
						_SCREAMING_SNAKE_CASE				= get_strided_contexts_and_ans(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			doc_stride=_SCREAMING_SNAKE_CASE	,			max_length=_SCREAMING_SNAKE_CASE	,			assertion=_SCREAMING_SNAKE_CASE	,			)

						return example



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Any  ):
						with jsonlines.open(_SCREAMING_SNAKE_CASE	,			"a"  ) as writer:
												for example in tqdm(_SCREAMING_SNAKE_CASE	,			total=len(_SCREAMING_SNAKE_CASE  )	,			desc="Saving samples ... "  ):
																		_SCREAMING_SNAKE_CASE				= 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

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

						_snake_case				:   Optional[Any]     =				data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

						_snake_case				:   List[Any]     =				{
						    """tokenizer""": tokenizer,
						    """doc_stride""": DOC_STRIDE,
						    """max_length""": MAX_LENGTH,
						    """assertion""": False,
						}
						_snake_case				:   Optional[Any]     =				data.map(prepare_inputs, fn_kwargs=fn_kwargs)
						_snake_case				:   Union[str, Any]     =				data.remove_columns(["""annotations""", """document""", """id""", """question"""])
						print(data)

						np.random.seed(SEED)
						_snake_case				:   List[str]     =				"""nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
						save_to_disk(data, file_name=cache_file_name)

'''simple docstring'''







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Dict       =					DistilBertTokenizer
		a							:					Tuple       =					DistilBertTokenizerFast
		a							:					List[str]       =					True




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= DistilBertTokenizer.from_pretrained("distilbert-base-uncased"      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode("sequence builders"					,						add_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode("multi-sequence build"					,						add_special_tokens=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase					,						UpperCamelCase      )

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]