
"""simple docstring"""

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 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		def choose_first(a_,						a_=False      ):
				assert isinstance(a_,						a_      )
				if len(a_      ) == 1:
						lowerCamelCase					:      Dict          =   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					:      Optional[Any]          =   {k: [a[k]] for k in a}
						if len(a['start_token']      ) > 0:
								break
				return a

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

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

		lowerCamelCase					:      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 		UpperCAmelCase (		a_,						a_=False      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[Any]          =   _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
				lowerCamelCase					:      Union[str, Any]          =   example['document']['tokens']
				lowerCamelCase					:      Any          =   []
				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

		lowerCamelCase					:      Dict          =   ['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					:      List[str]          =   example['document']['tokens']
		lowerCamelCase					:      Tuple          =   answer['start_token']
		lowerCamelCase					:      int          =   answer['end_token']

		lowerCamelCase					:      Optional[int]          =   []
		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					:      Union[str, Any]          =   ' '.join(context[start_token:end_token]      )

		# checking above code
		if assertion:
				lowerCamelCase					:      Dict          =   doc['is_html'][answer['start_token'] : answer['end_token']]
				lowerCamelCase					:      Any          =   doc['token'][answer['start_token'] : answer['end_token']]
				lowerCamelCase					:      Tuple          =   ' '.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 		UpperCAmelCase (		a_,						a_,						a_=2048,						a_=4096,						a_=True      ):





		'''simple docstring'''


		lowerCamelCase					:      List[Any]          =   get_context_and_ans(a_,						assertion=a_      )
		lowerCamelCase					:      Tuple          =   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					:      Any          =   tokenizer(example['question']['text'],						out['context']      ).input_ids
		lowerCamelCase					:      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
				lowerCamelCase					:      Union[str, Any]          =   []
				lowerCamelCase					:      Tuple          =   []
				lowerCamelCase					:      Tuple          =   input_ids[:q_len]
				lowerCamelCase					:      Dict          =   range(a_,						len(a_      ),						max_length - doc_stride      )
				for i in doc_start_indices:
						lowerCamelCase					:      Optional[int]          =   i + max_length - q_len
						lowerCamelCase					:      str          =   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,
				    },
				}

		lowerCamelCase					:      str          =   out['context'].split()
		lowerCamelCase					:      Union[str, Any]          =   splitted_context[answer['end_token']]
		lowerCamelCase					:      List[Any]          =   len(
		    tokenizer(
		        ' '.join(splitted_context[: answer['start_token']]      ),						add_special_tokens=a_,						).input_ids      )
		lowerCamelCase					:      int          =   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
		lowerCamelCase					:      str          =   len(tokenizer(a_,						add_special_tokens=a_      ).input_ids      )
		if num_sub_tokens > 1:
				answer["end_token"] += num_sub_tokens - 1

		lowerCamelCase					:      Dict          =   input_ids[answer['start_token'] : answer['end_token'] + 1]  # right & left are inclusive
		lowerCamelCase					:      Tuple          =   answer['start_token']
		lowerCamelCase					:      Dict          =   answer['end_token']

		if assertion:
				lowerCamelCase					:      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"],
				    },
				}

		lowerCamelCase					:      Dict          =   input_ids[:q_len]
		lowerCamelCase					:      int          =   range(a_,						len(a_      ),						max_length - doc_stride      )

		lowerCamelCase					:      Dict          =   []
		lowerCamelCase					:      List[Any]          =   []
		lowerCamelCase					:      Optional[int]          =   []
		lowerCamelCase					:      Any          =   []  # null, yes, no, long, short
		for i in doc_start_indices:
				lowerCamelCase					:      List[str]          =   i + max_length - q_len
				lowerCamelCase					:      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:
						lowerCamelCase					:      str          =   start_token - i + q_len
						lowerCamelCase					:      List[Any]          =   end_token - i + q_len
						answers_category.append(answer['category'][0]      )  # ["short"] -> "short"
				else:
						lowerCamelCase					:      str          =   -100
						lowerCamelCase					:      Optional[Any]          =   -100
						answers_category.append('null'      )
				lowerCamelCase					:      Union[str, Any]          =   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 		UpperCAmelCase (		a_,						a_,						a_=2048,						a_=4096,						a_=False      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   get_strided_contexts_and_ans(
		    a_,						a_,						doc_stride=a_,						max_length=a_,						assertion=a_,						)

		return example
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		with jsonlines.open(a_,						'a'      ) as writer:
				for example in tqdm(a_,						total=len(a_      ),						desc='Saving samples ... '      ):
						lowerCamelCase					:      str          =   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)








"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






