


import os

import jsonlines
import numpy as np
from tqdm import tqdm


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





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]						) ->   Tuple:





	"""simple docstring"""







	def choose_first(UpperCamelCase__:     Any							,					UpperCamelCase__:     Optional[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   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     List[str]=False						) ->   List[Any]:





	"""simple docstring"""







	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   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     Dict=20_48							,					UpperCamelCase__:     Any=40_96							,					UpperCamelCase__:     str=True						) ->   Any:





	"""simple docstring"""







	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   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     List[str]							,					UpperCamelCase__:     List[Any]=20_48							,					UpperCamelCase__:     Dict=40_96							,					UpperCamelCase__:     List[Any]=False						) ->   Dict:





	"""simple docstring"""







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

	return example





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Tuple						) ->   Union[str, Any]:





	"""simple docstring"""







	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

				_lowercase       :      List[str]	=	load_dataset("natural_questions")
				_lowercase       :      str	=	BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")

				_lowercase       :      Union[str, Any]	=	data["train" if PROCESS_TRAIN == "true" else "validation"]

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

				np.random.seed(SEED)
				_lowercase       :      Optional[Any]	=	"nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
				save_to_disk(data, file_name=cache_file_name)







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







	assert masked_input.count("""<mask>"""						) == 1
	A								=				torch.tensor(tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
	A								=				model(UpperCamelCase__						)[0]  # The last hidden-state is the first element of the output tuple
	A								=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	A								=				logits[0, masked_index, :]
	A								=				logits.softmax(dim=0						)
	A					,			A								=				prob.topk(k=UpperCamelCase__							,					dim=0						)
	A								=				""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
	A								=				tokenizer.mask_token
	A								=				[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """						)						):
		A								=				predicted_token_bpe.replace("""\u2581"""							,					""" """						)
		if " {0}".format(UpperCamelCase__						) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(UpperCamelCase__						)							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(UpperCamelCase__							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
	return topk_filled_outputs


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



