
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict






@pytest.mark.parametrize(
    'files'					,				[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,				)
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       tmp_path_factory.mktemp('dset_infos_dir'				)
	if "full:README.md" in files:
		with open(dataset_infos_dir / 'README.md'					,				'w'				) as f:
			f.write('---\ndataset_info:\n  dataset_size: 42\n---'				)
	if "empty:README.md" in files:
		with open(dataset_infos_dir / 'README.md'					,				'w'				) as f:
			f.write(''				)
    # we want to support dataset_infos.json for backward compatibility
	if "dataset_infos.json" in files:
		with open(dataset_infos_dir / 'dataset_infos.json'					,				'w'				) as f:
			f.write('{"default": {"dataset_size": 42}}'				)
	lowercase	    =       DatasetInfosDict.from_directory(__SCREAMING_SNAKE_CASE				)
	assert dataset_infos
	assert dataset_infos["default"].dataset_size == 42






@pytest.mark.parametrize(
    'dataset_info'					,				[
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,				features=Features({'a': Value('int32'				)}				)					,				builder_name='builder'					,				config_name='config'					,				version='1.0.0'					,				splits=[{'name': 'train'}]					,				download_size=42					,				),
    ]					,				)
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       str(__SCREAMING_SNAKE_CASE				)
	dataset_info.write_to_directory(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       DatasetInfo.from_directory(__SCREAMING_SNAKE_CASE				)
	assert dataset_info == reloaded
	assert os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE					,				'dataset_info.json'				)				)






def 				UpperCAmelCase_						(				):
	lowercase	    =       DatasetInfo(
	    description='foo'					,				citation='bar'					,				homepage='https://foo.bar'					,				license='CC0'					,				features=Features({'a': Value('int32'				)}				)					,				post_processed={}					,				supervised_keys=()					,				task_templates=[]					,				builder_name='builder'					,				config_name='config'					,				version='1.0.0'					,				splits=[{'name': 'train', 'num_examples': 42}]					,				download_checksums={}					,				download_size=1337					,				post_processing_size=442					,				dataset_size=1234					,				size_in_bytes=1337 + 442 + 1234					,				)
	lowercase	    =       dataset_info._to_yaml_dict()
	assert sorted(__SCREAMING_SNAKE_CASE				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		assert key in dataset_info_yaml_dict
		assert isinstance(dataset_info_yaml_dict[key]					,				(list, dict, int, str)				)
	lowercase	    =       yaml.safe_dump(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       yaml.safe_load(__SCREAMING_SNAKE_CASE				)
	assert dataset_info_yaml_dict == reloaded






def 				UpperCAmelCase_						(				):
	lowercase	    =       DatasetInfo()
	lowercase	    =       dataset_info._to_yaml_dict()
	assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    'dataset_infos_dict'					,				[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}				),
        DatasetInfosDict({'my_config_name': DatasetInfo()}				),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,				features=Features({'a': Value('int32'				)}				)					,				builder_name='builder'					,				config_name='config'					,				version='1.0.0'					,				splits=[{'name': 'train'}]					,				download_size=42					,				)
            }				),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42				),
                'v2': DatasetInfo(dataset_size=1337				),
            }				),
    ]					,				)
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       str(__SCREAMING_SNAKE_CASE				)
	dataset_infos_dict.write_to_directory(__SCREAMING_SNAKE_CASE				)
	lowercase	    =       DatasetInfosDict.from_directory(__SCREAMING_SNAKE_CASE				)

	# the config_name of the dataset_infos_dict take over the attribute
	for config_name, dataset_info in dataset_infos_dict.items():
		lowercase	    =       config_name
		# the yaml representation doesn't include fields like description or citation
		# so we just test that we can recover what we can from the yaml
		lowercase	    =       DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
	assert dataset_infos_dict == reloaded

	if dataset_infos_dict:
		assert os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE					,				'README.md'				)				)







import os

import jsonlines
import numpy as np
from tqdm import tqdm


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




def 		lowerCAmelCase_ (						lowerCamelCase ):
				def choose_first(lowerCamelCase						,  lowerCamelCase=False ):
								assert isinstance(lowerCamelCase						,  lowerCamelCase )
								if len(lowerCamelCase ) == 1:
												__magic_name__						:						List[str]										=answer[0]
												return {k: [answer[k]] for k in answer} if is_long_answer else answer
								for a in answer:
												if is_long_answer:
																__magic_name__						:						Tuple										={k: [a[k]] for k in a}
												if len(a["""start_token"""] ) > 0:
																break
								return a

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

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

				__magic_name__						:						int										=["""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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						Optional[int]										=_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
								__magic_name__						:						Any										=example["""document"""]["""tokens"""]
								__magic_name__						:						str										=[]
								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": -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

				__magic_name__						:						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

				__magic_name__						:						Tuple										=example["""document"""]["""tokens"""]
				__magic_name__						:						Optional[int]										=answer["""start_token"""]
				__magic_name__						:						List[Any]										=answer["""end_token"""]

				__magic_name__						:						Optional[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
				__magic_name__						:						Optional[int]										=""" """.join(context[start_token:end_token] )

				# checking above code
				if assertion:
								__magic_name__						:						List[str]										=doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
								__magic_name__						:						str										=doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
								__magic_name__						:						Dict										=""" """.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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=2048						,  lowerCamelCase=4096						,  lowerCamelCase=True ):
				# overlap will be of doc_stride - q_len

				__magic_name__						:						Any										=get_context_and_ans(lowerCamelCase						,  assertion=lowerCamelCase )
				__magic_name__						:						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"],
								    },
								}

				__magic_name__						:						List[Any]										=tokenizer(example["""question"""]["""text"""]						,  out["""context"""] ).input_ids
				__magic_name__						:						Dict										=input_ids.index(tokenizer.sep_token_id ) + 1

				# return yes/no
				if answer["category"][0] in ["yes", "no"]:  # category is list with one element
								__magic_name__						:						List[str]										=[]
								__magic_name__						:						int										=[]
								__magic_name__						:						List[str]										=input_ids[:q_len]
								__magic_name__						:						Dict										=range(lowerCamelCase						,  len(lowerCamelCase )						,  max_length - doc_stride )
								for i in doc_start_indices:
												__magic_name__						:						List[Any]										=i + max_length - q_len
												__magic_name__						:						Tuple										=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(lowerCamelCase ),
								        "end_token": [-100] * len(lowerCamelCase ),
								        "category": category,
								    },
								}

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

				__magic_name__						:						str										=input_ids[answer["""start_token"""] : answer["""end_token"""] + 1]  # right & left are inclusive
				__magic_name__						:						Dict										=answer["""start_token"""]
				__magic_name__						:						int										=answer["""end_token"""]

				if assertion:
								__magic_name__						:						Any										=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"],
								    },
								}

				__magic_name__						:						Any										=input_ids[:q_len]
				__magic_name__						:						Union[str, Any]										=range(lowerCamelCase						,  len(lowerCamelCase )						,  max_length - doc_stride )

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

				return example




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				with jsonlines.open(lowerCamelCase						,  """a""" ) as writer:
								for example in tqdm(lowerCamelCase						,  total=len(lowerCamelCase )						,  desc="""Saving samples ... """ ):
												__magic_name__						:						int										=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_		:					Optional[int]          =  load_dataset("natural_questions")
							UpperCAmelCase_		:					Optional[int]          =  BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

							UpperCAmelCase_		:					str          =  data["train" if PROCESS_TRAIN == "true" else "validation"]

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

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




