






import os

import jsonlines
import numpy as np
from tqdm import tqdm


lowerCAmelCase_           = 2_048
lowerCAmelCase_           = 4_096
lowerCAmelCase_           = 42
lowerCAmelCase_           = os.environ.pop('PROCESS_TRAIN', 'false')
lowerCAmelCase_           = {'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}







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

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

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

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

				return answer







def      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       List[Any]=False   ):
				A	 =      _get_single_answer(UpperCAmelCase   )
				# 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
								A	 =      example['document']['tokens']
								A	 =      []
								for i in range(len(doc['token']   )   ):
												if not doc["is_html"][i]:
																context.append(doc['token'][i]   )
								return {
								    "context": " ".join(UpperCAmelCase   ),
								    "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

				A	 =      ['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

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

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

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

				return {
				    "context": " ".join(UpperCAmelCase   ),
				    "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      snake_case		(							UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Any=20_48,						UpperCAmelCase	:       Tuple=40_96,						UpperCAmelCase	:       Any=True   ):
				# overlap will be of doc_stride - q_len

				A	 =      get_context_and_ans(UpperCAmelCase,						assertion=UpperCAmelCase   )
				A	 =      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"],
								    },
								}

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

				# return yes/no
				if answer["category"][0] in ["yes", "no"]:  # category is list with one element
								A	 =      []
								A	 =      []
								A	 =      input_ids[:q_len]
								A	 =      range(UpperCAmelCase,						len(UpperCAmelCase   ),						max_length - doc_stride   )
								for i in doc_start_indices:
												A	 =      i + max_length - q_len
												A	 =      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(UpperCAmelCase   ),
								        "end_token": [-1_00] * len(UpperCAmelCase   ),
								        "category": category,
								    },
								}

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

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

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

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

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

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

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

				A	 =      []
				A	 =      []
				A	 =      []
				A	 =      []  # null, yes, no, long, short
				for i in doc_start_indices:
								A	 =      i + max_length - q_len
								A	 =      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:
												A	 =      start_token - i + q_len
												A	 =      end_token - i + q_len
												answers_category.append(answer['category'][0]   )  # ["short"] -> "short"
								else:
												A	 =      -1_00
												A	 =      -1_00
												answers_category.append('null'   )
								A	 =      inputs[-1][start_token : end_token + 1]

								answers_start_token.append(UpperCAmelCase   )
								answers_end_token.append(UpperCAmelCase   )
								if assertion:
												if new != old and new != [tokenizer.cls_token_id]:
																print('ISSUE in strided for ID:',						example['id']   )
																print('New:',						tokenizer.decode(UpperCAmelCase   )   )
																print('Old:',						tokenizer.decode(UpperCAmelCase   ),						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      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       str=20_48,						UpperCAmelCase	:       List[str]=40_96,						UpperCAmelCase	:       Any=False   ):
				A	 =      get_strided_contexts_and_ans(
				    UpperCAmelCase,						UpperCAmelCase,						doc_stride=UpperCAmelCase,						max_length=UpperCAmelCase,						assertion=UpperCAmelCase,						)

				return example







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       str   ):
				with jsonlines.open(UpperCAmelCase,						'a'   ) as writer:
								for example in tqdm(UpperCAmelCase,						total=len(UpperCAmelCase   ),						desc='Saving samples ... '   ):
												A	 =      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

					lowerCAmelCase_           = load_dataset('natural_questions')
					lowerCAmelCase_           = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

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

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

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













import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   ["image_processor", "tokenizer"]
		snake_case    =   "LayoutLMv2ImageProcessor"
		snake_case    =   ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
		def __init__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       List[str]=None						,_SCREAMING_SNAKE_CASE		:       Optional[int]=None						,**_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'						,_SCREAMING_SNAKE_CASE						,)
										A	 =      kwargs.pop('feature_extractor'		)

						A	 =      image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
										raise ValueError('You need to specify an `image_processor`.'		)
						if tokenizer is None:
										raise ValueError('You need to specify a `tokenizer`.'		)

						super().__init__(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
		def __call__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,_SCREAMING_SNAKE_CASE		:       Union[List[List[int]], List[List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Union[List[int], List[List[int]]]] = None						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, PaddingStrategy] = False						,_SCREAMING_SNAKE_CASE		:       Union[bool, str, TruncationStrategy] = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       int = 0						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       Optional[bool] = None						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = False						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, TensorType]] = None						,**_SCREAMING_SNAKE_CASE		:       Tuple						,)    ->   BatchEncoding:


						'''simple docstring'''
						# verify input
						if self.image_processor.apply_ocr and (boxes is not None):
										raise ValueError(
										    'You cannot provide bounding boxes '
										    'if you initialized the image processor with apply_ocr set to True.'		)

						if self.image_processor.apply_ocr and (word_labels is not None):
										raise ValueError(
										    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

						if return_overflowing_tokens is True and return_offsets_mapping is False:
										raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'		)

						# first, apply the image processor
						A	 =      self.image_processor(images=_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE		)

						# second, apply the tokenizer
						if text is not None and self.image_processor.apply_ocr and text_pair is None:
										if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														A	 =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
										A	 =      features['words']

						A	 =      self.tokenizer(
						    text=text if text is not None else features['words']						,text_pair=text_pair if text_pair is not None else None						,boxes=boxes if boxes is not None else features['boxes']						,word_labels=_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE						,padding=_SCREAMING_SNAKE_CASE						,truncation=_SCREAMING_SNAKE_CASE						,max_length=_SCREAMING_SNAKE_CASE						,stride=_SCREAMING_SNAKE_CASE						,pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,return_token_type_ids=_SCREAMING_SNAKE_CASE						,return_attention_mask=_SCREAMING_SNAKE_CASE						,return_overflowing_tokens=_SCREAMING_SNAKE_CASE						,return_special_tokens_mask=_SCREAMING_SNAKE_CASE						,return_offsets_mapping=_SCREAMING_SNAKE_CASE						,return_length=_SCREAMING_SNAKE_CASE						,verbose=_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						# add pixel values
						A	 =      features.pop('pixel_values'		)
						if return_overflowing_tokens is True:
										A	 =      self.get_overflowing_images(_SCREAMING_SNAKE_CASE						,encoded_inputs['overflow_to_sample_mapping']		)
						A	 =      images

						return encoded_inputs
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   Dict:


						'''simple docstring'''
						# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
						A	 =      []
						for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]		)

						if len(_SCREAMING_SNAKE_CASE		) != len(_SCREAMING_SNAKE_CASE		):
										raise ValueError(
										    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
										    f' {len(_SCREAMING_SNAKE_CASE		)} and {len(_SCREAMING_SNAKE_CASE		)}'		)

						return images_with_overflow
		def       A(  self		:       List[str]						,*_SCREAMING_SNAKE_CASE		:       str						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Tuple:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Dict						,*_SCREAMING_SNAKE_CASE		:       Tuple						,**_SCREAMING_SNAKE_CASE		:       int		)    ->   int:


						'''simple docstring'''
						return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		@property
		def       A(  self		:       Any		)    ->   Union[str, Any]:


						'''simple docstring'''
						return ["input_ids", "bbox", "attention_mask", "image"]
		@property
		def       A(  self		:       Dict		)    ->   int:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor_class







		@property
		def       A(  self		:       Optional[int]		)    ->   Union[str, Any]:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor





