




"""simple docstring"""

import os

import jsonlines
import numpy as np
from tqdm import tqdm


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

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	def choose_first(_UpperCAmelCase  ,      _UpperCAmelCase=False	):
		assert isinstance(_snake_case  ,      _snake_case	)
		if len(_snake_case	) == 1:
			A_  :				List[Any]	   =		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_  :				Optional[Any]	   =		{k: [a[k]] for k in a}
			if len(a['''start_token''']	) > 0:
				break
		return a

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

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

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

	return answer

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=False	):




	"""simple docstring"""






	A_  :				str	   =		_get_single_answer(_snake_case	)
	# 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_  :				List[Any]	   =		example['''document''']['''tokens''']
		A_  :				List[Any]	   =		[]
		for i in range(len(doc['''token''']	)	):
			if not doc["is_html"][i]:
				context.append(doc['''token'''][i]	)
		return {
		    "context": " ".join(_snake_case	),
		    "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_  :				Tuple	   =		['''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_  :				Tuple	   =		example['''document''']['''tokens''']
	A_  :				Any	   =		answer['''start_token''']
	A_  :				Dict	   =		answer['''end_token''']

	A_  :				Optional[int]	   =		[]
	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_  :				Optional[Any]	   =		''' '''.join(context[start_token:end_token]	)

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

	return {
	    "context": " ".join(_snake_case	),
	    "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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=2048  ,      _UpperCAmelCase=4096  ,      _UpperCAmelCase=True	):




	"""simple docstring"""






	A_  :				Tuple	   =		get_context_and_ans(_snake_case  ,      assertion=_snake_case	)
	A_  :				int	   =		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_  :				List[str]	   =		tokenizer(example['''question''']['''text''']  ,      out['''context''']	).input_ids
	A_  :				Tuple	   =		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_  :				Optional[Any]	   =		[]
		A_  :				int	   =		[]
		A_  :				Tuple	   =		input_ids[:q_len]
		A_  :				Any	   =		range(_snake_case  ,      len(_snake_case	)  ,      max_length - doc_stride	)
		for i in doc_start_indices:
			A_  :				str	   =		i + max_length - q_len
			A_  :				Any	   =		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(_snake_case	),
		        "end_token": [-100] * len(_snake_case	),
		        "category": category,
		    },
		}

	A_  :				Union[str, Any]	   =		out['''context'''].split()
	A_  :				int	   =		splitted_context[answer['''end_token''']]
	A_  :				Optional[Any]	   =		len(
	    tokenizer(
	        ''' '''.join(splitted_context[: answer['''start_token''']]	)  ,      add_special_tokens=_snake_case  ,      ).input_ids	)
	A_  :				int	   =		len(
	    tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]	)  ,      add_special_tokens=_snake_case	).input_ids	)

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

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

	A_  :				List[str]	   =		input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
	A_  :				int	   =		answer['''start_token''']
	A_  :				List[str]	   =		answer['''end_token''']

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

	if len(_snake_case	) <= 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_  :				List[Any]	   =		input_ids[:q_len]
	A_  :				List[str]	   =		range(_snake_case  ,      len(_snake_case	)  ,      max_length - doc_stride	)

	A_  :				Tuple	   =		[]
	A_  :				Tuple	   =		[]
	A_  :				str	   =		[]
	A_  :				Any	   =		[]  # null, yes, no, long, short
	for i in doc_start_indices:
		A_  :				str	   =		i + max_length - q_len
		A_  :				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:
			A_  :				List[str]	   =		start_token - i + q_len
			A_  :				Dict	   =		end_token - i + q_len
			answers_category.append(answer['''category'''][0]	)  # ["short"] -> "short"
		else:
			A_  :				Dict	   =		-100
			A_  :				Union[str, Any]	   =		-100
			answers_category.append('''null'''	)
		A_  :				Tuple	   =		inputs[-1][start_token : end_token + 1]

		answers_start_token.append(_snake_case	)
		answers_end_token.append(_snake_case	)
		if assertion:
			if new != old and new != [tokenizer.cls_token_id]:
				print('''ISSUE in strided for ID:'''  ,      example['''id''']	)
				print('''New:'''  ,      tokenizer.decode(_snake_case	)	)
				print('''Old:'''  ,      tokenizer.decode(_snake_case	)  ,      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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase=2048  ,      _UpperCAmelCase=4096  ,      _UpperCAmelCase=False	):




	"""simple docstring"""






	A_  :				Any	   =		get_strided_contexts_and_ans(
	    _snake_case  ,      _snake_case  ,      doc_stride=_snake_case  ,      max_length=_snake_case  ,      assertion=_snake_case  ,      )

	return example

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	with jsonlines.open(_snake_case  ,      '''a'''	) as writer:
		for example in tqdm(_snake_case  ,      total=len(_snake_case	)  ,      desc='''Saving samples ... '''	):
			A_  :				Any	   =		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  :			Tuple        =       load_dataset('natural_questions')
		_lowerCamelCase  :			Any        =       BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

		_lowerCamelCase  :			str        =       data['train' if PROCESS_TRAIN == 'true' else 'validation']

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

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












"""simple docstring"""

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		[
	    '''decoder.version''',
	    '''decoder.output_projection.weight''',
	    '''_float_tensor''',
	    '''decoder.embed_positions._float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(_UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_   , A_  :				List[Any]	   =		emb.weight.shape
	A_  :				List[Any]	   =		nn.Linear(_UpperCAmelCase  ,      _UpperCAmelCase  ,      bias=_UpperCAmelCase	)
	A_  :				Any	   =		emb.weight.data
	return lin_layer

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	A_  :				Any	   =		Namespace(**checkpoint['''cfg''']['''model''']	)
	A_  :				List[str]	   =		checkpoint['''model''']
	remove_ignore_keys_(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		state_dict['''decoder.embed_tokens.weight'''].shape[0]

	A_  :				List[str]	   =		{key.replace('''decoder'''  ,      '''model'''	): val for key, val in state_dict.items()}

	A_  :				int	   =		XGLMConfig(
	    vocab_size=_UpperCAmelCase  ,      max_position_embeddings=args.max_target_positions  ,      num_layers=args.decoder_layers  ,      attention_heads=args.decoder_attention_heads  ,      ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.decoder_embed_dim  ,      layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''gelu'''  ,      scale_embedding=not args.no_scale_embedding  ,      tie_word_embeddings=args.share_decoder_input_output_embed  ,      )

	A_  :				Any	   =		XGLMForCausalLM(_UpperCAmelCase	)
	A_  :				int	   =		model.load_state_dict(_UpperCAmelCase  ,      strict=_UpperCAmelCase	)
	print(_UpperCAmelCase	)
	A_  :				int	   =		make_linear_from_emb(model.model.embed_tokens	)

	return model


if __name__ == "__main__":
		_lowerCamelCase  :			Any        =       argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_lowerCamelCase  :			int        =       parser.parse_args()
		_lowerCamelCase  :			Optional[Any]        =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





