

import os

import jsonlines
import numpy as np
from tqdm import tqdm


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





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							def choose_first(UpperCamelCase__   ,							UpperCamelCase__=False      ):
														assert isinstance(UpperCamelCase__   ,							UpperCamelCase__      )
														if len(UpperCamelCase__      ) == 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=UpperCamelCase__      )
																					snake_case_							=    []
														answer.update(UpperCamelCase__      )

							# 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]   ,							UpperCamelCase__      ) for k in cols      ):
														raise ValueError('Issue in ID'   ,							example['id']      )

							return answer





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__=False      ):







							'''simple docstring'''






							snake_case_							=    _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
														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(UpperCamelCase__      ),
														    "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

							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(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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__=2048   ,							UpperCamelCase__=4096   ,							UpperCamelCase__=True      ):







							'''simple docstring'''






							snake_case_							=    get_context_and_ans(UpperCamelCase__   ,							assertion=UpperCamelCase__      )
							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(UpperCamelCase__   ,							len(UpperCamelCase__      )   ,							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": [-100] * len(UpperCamelCase__      ),
														        "end_token": [-100] * len(UpperCamelCase__      ),
														        "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=UpperCamelCase__   ,							).input_ids      )
							snake_case_							=    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
							snake_case_							=    len(tokenizer(UpperCamelCase__   ,							add_special_tokens=UpperCamelCase__      ).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(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"],
														    },
														}

							snake_case_							=    input_ids[:q_len]
							snake_case_							=    range(UpperCamelCase__   ,							len(UpperCamelCase__      )   ,							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_							=    -100
																					snake_case_							=    -100
																					answers_category.append('null'      )
														snake_case_							=    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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__=2048   ,							UpperCamelCase__=4096   ,							UpperCamelCase__=False      ):







							'''simple docstring'''






							snake_case_							=    get_strided_contexts_and_ans(
							    UpperCamelCase__   ,							UpperCamelCase__   ,							doc_stride=UpperCamelCase__   ,							max_length=UpperCamelCase__   ,							assertion=UpperCamelCase__   ,							)

							return example





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with jsonlines.open(UpperCamelCase__   ,							'a'      ) as writer:
														for example in tqdm(UpperCamelCase__   ,							total=len(UpperCamelCase__      )   ,							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		:   Any										=       load_dataset("""natural_questions""")
					_UpperCAmelCase		:   Optional[Any]										=       BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")

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

					_UpperCAmelCase		:   List[str]										=       {
					    """tokenizer""": tokenizer,
					    """doc_stride""": DOC_STRIDE,
					    """max_length""": MAX_LENGTH,
					    """assertion""": False,
					}
					_UpperCAmelCase		:   str										=       data.map(prepare_inputs, fn_kwargs=fn_kwargs)
					_UpperCAmelCase		:   List[Any]										=       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)







import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    1.5
							snake_case_							=    int(factor * num_class_images      )
							snake_case_							=    ClipClient(
							    url='https://knn.laion.ai/knn-service'   ,							indice_name='laion_400m'   ,							num_images=UpperCamelCase__   ,							aesthetic_weight=0.1      )

							os.makedirs(F'''{class_data_dir}/images'''   ,							exist_ok=UpperCamelCase__      )
							if len(list(Path(F'''{class_data_dir}/images'''      ).iterdir()      )      ) >= num_class_images:
														return

							while True:
														snake_case_							=    client.query(text=UpperCamelCase__      )
														if len(UpperCamelCase__      ) >= factor * num_class_images or num_images > 1E4:
																					break
														else:
																					snake_case_							=    int(factor * num_images      )
																					snake_case_							=    ClipClient(
																					    url='https://knn.laion.ai/knn-service'   ,							indice_name='laion_400m'   ,							num_images=UpperCamelCase__   ,							aesthetic_weight=0.1   ,							)

							snake_case_							=    0
							snake_case_							=    0
							snake_case_							=    tqdm(desc='downloading real regularization images'   ,							total=UpperCamelCase__      )

							with open(F'''{class_data_dir}/caption.txt'''   ,							'w'      ) as fa, open(F'''{class_data_dir}/urls.txt'''   ,							'w'      ) as fa, open(
							    F'''{class_data_dir}/images.txt'''   ,							'w'      ) as fa:
														while total < num_class_images:
																					snake_case_							=    class_images[count]
																					count += 1
																					try:
																												snake_case_							=    requests.get(images['url']      )
																												if img.status_code == 200:
																																			snake_case_							=    Image.open(BytesIO(img.content      )      )
																																			with open(F'''{class_data_dir}/images/{total}.jpg'''   ,							'wb'      ) as f:
																																										f.write(img.content      )
																																			fa.write(images['caption'] + '\n'      )
																																			fa.write(images['url'] + '\n'      )
																																			fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '\n'      )
																																			total += 1
																																			pbar.update(1      )
																												else:
																																			continue
																					except Exception:
																												continue
							return





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    argparse.ArgumentParser(''   ,							add_help=UpperCamelCase__      )
							parser.add_argument('--class_prompt'   ,							help='text prompt to retrieve images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--class_data_dir'   ,							help='path to save images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--num_class_images'   ,							help='number of images to download'   ,							default=200   ,							type=UpperCamelCase__      )
							return parser.parse_args()


if __name__ == "__main__":
					_UpperCAmelCase		:   Optional[int]										=       parse_args()
					retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




