




'''simple docstring'''



import os

import jsonlines
import numpy as np
from tqdm import tqdm


UpperCAmelCase_							:	List[str]	     =		2_0_4_8
UpperCAmelCase_							:	List[Any]	     =		4_0_9_6
UpperCAmelCase_							:	Any	     =		4_2
UpperCAmelCase_							:	List[str]	     =		os.environ.pop('''PROCESS_TRAIN''', '''false''')
UpperCAmelCase_							:	List[Any]	     =		{'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple						)-> List[Any]:




     '''simple docstring'''





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

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

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

     __snake_case            =	['''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       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  Any=False						)-> Any:




     '''simple docstring'''





     __snake_case            =	_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
          __snake_case            =	example['''document''']['''tokens''']
          __snake_case            =	[]
          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_00,  # ignore index in cross-entropy
                  "end_token": -1_00,  # 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

     __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

     __snake_case            =	example['''document''']['''tokens''']
     __snake_case            =	answer['''start_token''']
     __snake_case            =	answer['''end_token''']

     __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
     __snake_case            =	''' '''.join(context[start_token:end_token]						)

     # checking above code
     if assertion:
          __snake_case            =	doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
          __snake_case            =	doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
          __snake_case            =	''' '''.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       _UpperCamelCase			(_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Union[str, Any] ,						_lowerCamelCase				:  List[str]=20_48 ,						_lowerCamelCase				:  List[Any]=40_96 ,						_lowerCamelCase				:  Union[str, Any]=True						)-> List[Any]:




     '''simple docstring'''





     __snake_case            =	get_context_and_ans(_lowerCamelCase ,						assertion=_lowerCamelCase						)
     __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"],
              },
          }

     __snake_case            =	tokenizer(example['''question''']['''text'''] ,						out['''context''']						).input_ids
     __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
          __snake_case            =	[]
          __snake_case            =	[]
          __snake_case            =	input_ids[:q_len]
          __snake_case            =	range(_lowerCamelCase ,						len(_lowerCamelCase						) ,						max_length - doc_stride						)
          for i in doc_start_indices:
               __snake_case            =	i + max_length - q_len
               __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": [-1_00] * len(_lowerCamelCase						),
                  "end_token": [-1_00] * len(_lowerCamelCase						),
                  "category": category,
              },
          }

     __snake_case            =	out['''context'''].split()
     __snake_case            =	splitted_context[answer['''end_token''']]
     __snake_case            =	len(
         tokenizer(
             ''' '''.join(splitted_context[: answer['''start_token''']]						) ,						add_special_tokens=_lowerCamelCase ,						).input_ids						)
     __snake_case            =	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
     __snake_case            =	len(tokenizer(_lowerCamelCase ,						add_special_tokens=_lowerCamelCase						).input_ids						)
     if num_sub_tokens > 1:
          answer["end_token"] += num_sub_tokens - 1

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

     if assertion:
          __snake_case            =	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"],
              },
          }

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

     __snake_case            =	[]
     __snake_case            =	[]
     __snake_case            =	[]
     __snake_case            =	[]  # null, yes, no, long, short
     for i in doc_start_indices:
          __snake_case            =	i + max_length - q_len
          __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:
               __snake_case            =	start_token - i + q_len
               __snake_case            =	end_token - i + q_len
               answers_category.append(answer['''category'''][0]						)  # ["short"] -> "short"
          else:
               __snake_case            =	-1_00
               __snake_case            =	-1_00
               answers_category.append('''null'''						)
          __snake_case            =	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       _UpperCamelCase			(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  int=20_48 ,						_lowerCamelCase				:  Optional[Any]=40_96 ,						_lowerCamelCase				:  Any=False						)-> Any:




     '''simple docstring'''





     __snake_case            =	get_strided_contexts_and_ans(
         _lowerCamelCase ,						_lowerCamelCase ,						doc_stride=_lowerCamelCase ,						max_length=_lowerCamelCase ,						assertion=_lowerCamelCase ,						)

     return example







def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  Dict						)-> int:




     '''simple docstring'''





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

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

     UpperCAmelCase_							:	int	     =		data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

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

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









def 		UpperCamelCase   (lowercase_:					list   )	-> list:
	A__				:       Union[str, Any]	  =						False
	while is_sorted is False:  # Until all the indices are traversed keep looping
		A__				:       Dict	  =						True
		for i in range(0       , len(lowercase_   ) - 1       , 2   ):  # iterating over all even indices
			if input_list[i] > input_list[i + 1]:
				A__  ,  A__				:       Dict	  =						input_list[i + 1], input_list[i]
				# swapping if elements not in order
				A__				:       Optional[Any]	  =						False

		for i in range(1       , len(lowercase_   ) - 1       , 2   ):  # iterating over all odd indices
			if input_list[i] > input_list[i + 1]:
				A__  ,  A__				:       Dict	  =						input_list[i + 1], input_list[i]
				# swapping if elements not in order
				A__				:       Optional[Any]	  =						False
	return input_list


if __name__ == "__main__":
	print('Enter list to be sorted')
	A_			:					Optional[int]    			=     [int(x) for x in input().split()]
	# inputing elements of the list in one line
	A_			:					Optional[int]    			=     odd_even_sort(input_list)
	print('The sorted list is')
	print(sorted_list)



