



import os

import jsonlines
import numpy as np
from tqdm import tqdm


UpperCAmelCase_							:      List[str]      =  2_048
UpperCAmelCase_							:      int      =  4_096
UpperCAmelCase_							:      Optional[int]      =  42
UpperCAmelCase_							:      Tuple      =  os.environ.pop("PROCESS_TRAIN", "false")
UpperCAmelCase_							:      Union[str, Any]      =  {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
def  UpperCamelCase						(       _A			:				str    )->       Union[str, Any]:





						"""simple docstring"""





						def choose_first(_A			:				Tuple			,							_A			:				Optional[Any]=False    ):
												assert isinstance(_A			,							_A    )
												if len(_A    ) == 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=_A    )
																		A__							=       []
												answer.update(_A    )

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

						return answer
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				int=False    )->       List[str]:





						"""simple docstring"""





						A__							=       _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
												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(_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

						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(_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			:				Optional[Any]			,							_A			:				Optional[int]			,							_A			:				int=2048			,							_A			:				Optional[Any]=4096			,							_A			:				Union[str, Any]=True    )->       Optional[int]:





						"""simple docstring"""





						A__							=       get_context_and_ans(_A			,							assertion=_A    )
						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(_A			,							len(_A    )			,							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": [-100] * len(_A    ),
												        "end_token": [-100] * len(_A    ),
												        "category": category,
												    },
												}

						A__							=       out["context"].split()
						A__							=       splitted_context[answer["end_token"]]
						A__							=       len(
						    tokenizer(
						        " ".join(splitted_context[: answer["start_token"]]    )			,							add_special_tokens=_A			,							).input_ids    )
						A__							=       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
						A__							=       len(tokenizer(_A			,							add_special_tokens=_A    ).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(_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"],
												    },
												}

						A__							=       input_ids[:q_len]
						A__							=       range(_A			,							len(_A    )			,							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__							=       -100
																		A__							=       -100
																		answers_category.append("null"    )
												A__							=       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			:				Optional[int]			,							_A			:				List[str]			,							_A			:				Optional[Any]=2048			,							_A			:				int=4096			,							_A			:				Any=False    )->       Optional[int]:





						"""simple docstring"""





						A__							=       get_strided_contexts_and_ans(
						    _A			,							_A			,							doc_stride=_A			,							max_length=_A			,							assertion=_A			,							)

						return example
def  UpperCamelCase						(       _A			:				int			,							_A			:				Dict    )->       int:





						"""simple docstring"""





						with jsonlines.open(_A			,							"a"    ) as writer:
												for example in tqdm(_A			,							total=len(_A    )			,							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

			UpperCAmelCase_							:      Optional[Any]      =  load_dataset("natural_questions")
			UpperCAmelCase_							:      List[Any]      =  BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

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

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

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










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





