






"""simple docstring"""







import os

import jsonlines
import numpy as np
from tqdm import tqdm


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





def 						lowercase__(A						)    ->Tuple:




						"""simple docstring"""



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

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

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

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

						return answer





def 						lowercase__(A ,		A=False						)    ->Tuple:




						"""simple docstring"""



						lowercase__    :				str=	_get_single_answer(A						)
						# 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
												lowercase__    :				Tuple=	example["document"]["tokens"]
												lowercase__    :				Tuple=	[]
												for i in range(len(doc["token"]						)						):
																		if not doc["is_html"][i]:
																								context.append(doc["token"][i]						)
												return {
												    "context": " ".join(A						),
												    "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

						lowercase__    :				Optional[int]=	["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

						lowercase__    :				Dict=	example["document"]["tokens"]
						lowercase__    :				Dict=	answer["start_token"]
						lowercase__    :				str=	answer["end_token"]

						lowercase__    :				List[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
						lowercase__    :				List[Any]=	" ".join(context[start_token:end_token]						)

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

						return {
						    "context": " ".join(A						),
						    "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 						lowercase__(A ,		A ,		A=2_048 ,		A=4_096 ,		A=True						)    ->str:




						"""simple docstring"""



						lowercase__    :				Any=	get_context_and_ans(A ,		assertion=A						)
						lowercase__    :				Union[str, 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"],
												    },
												}

						lowercase__    :				int=	tokenizer(example["question"]["text"] ,		out["context"]						).input_ids
						lowercase__    :				List[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
												lowercase__    :				Optional[int]=	[]
												lowercase__    :				List[str]=	[]
												lowercase__    :				Dict=	input_ids[:q_len]
												lowercase__    :				str=	range(A ,		len(A						) ,		max_length - doc_stride						)
												for i in doc_start_indices:
																		lowercase__    :				List[Any]=	i + max_length - q_len
																		lowercase__    :				Dict=	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(A						),
												        "end_token": [-100] * len(A						),
												        "category": category,
												    },
												}

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

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

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

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

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

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

						lowercase__    :				Union[str, Any]=	input_ids[:q_len]
						lowercase__    :				List[Any]=	range(A ,		len(A						) ,		max_length - doc_stride						)

						lowercase__    :				Optional[Any]=	[]
						lowercase__    :				Dict=	[]
						lowercase__    :				List[Any]=	[]
						lowercase__    :				Optional[int]=	[]  # null, yes, no, long, short
						for i in doc_start_indices:
												lowercase__    :				str=	i + max_length - q_len
												lowercase__    :				Union[str, Any]=	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:
																		lowercase__    :				str=	start_token - i + q_len
																		lowercase__    :				str=	end_token - i + q_len
																		answers_category.append(answer["category"][0]						)  # ["short"] -> "short"
												else:
																		lowercase__    :				List[Any]=	-100
																		lowercase__    :				Any=	-100
																		answers_category.append("null"						)
												lowercase__    :				Optional[int]=	inputs[-1][start_token : end_token + 1]

												answers_start_token.append(A						)
												answers_end_token.append(A						)
												if assertion:
																		if new != old and new != [tokenizer.cls_token_id]:
																								print("ISSUE in strided for ID:" ,		example["id"]						)
																								print("New:" ,		tokenizer.decode(A						)						)
																								print("Old:" ,		tokenizer.decode(A						) ,		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 						lowercase__(A ,		A ,		A=2_048 ,		A=4_096 ,		A=False						)    ->Optional[int]:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	get_strided_contexts_and_ans(
						    A ,		A ,		doc_stride=A ,		max_length=A ,		assertion=A ,		)

						return example





def 						lowercase__(A ,		A						)    ->int:




						"""simple docstring"""



						with jsonlines.open(A ,		"a"						) as writer:
												for example in tqdm(A ,		total=len(A						) ,		desc="Saving samples ... "						):
																		lowercase__    :				Any=	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			:    int				 =		load_dataset("""natural_questions""")
			a			:    Tuple				 =		BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

			a			:    int				 =		data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

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

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














"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    List[str]				 =		{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    List[str]				 =		[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			a			:    List[str]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)






