





import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A				:    Optional[Any]       =   logging.getLogger(__name__)





def 						__SCREAMING_SNAKE_CASE  (				)  ->				str:







			'''simple docstring'''

			UpperCAmelCase   		=				argparse.ArgumentParser(
			    description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).''' )
			parser.add_argument('''--file_path'''			,			type=UpperCamelCase__			,			default='''data/dump.txt'''			,			help='''The path to the data.''' )
			parser.add_argument('''--tokenizer_type'''			,			type=UpperCamelCase__			,			default='''bert'''			,			choices=['''bert''', '''roberta''', '''gpt2'''] )
			parser.add_argument('''--tokenizer_name'''			,			type=UpperCamelCase__			,			default='''bert-base-uncased'''			,			help='''The tokenizer to use.''' )
			parser.add_argument('''--dump_file'''			,			type=UpperCamelCase__			,			default='''data/dump'''			,			help='''The dump file prefix.''' )
			UpperCAmelCase   		=				parser.parse_args()

			logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
			if args.tokenizer_type == "bert":
						UpperCAmelCase   		=				BertTokenizer.from_pretrained(args.tokenizer_name )
						UpperCAmelCase   		=				tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
						UpperCAmelCase   		=				tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						UpperCAmelCase   		=				RobertaTokenizer.from_pretrained(args.tokenizer_name )
						UpperCAmelCase   		=				tokenizer.special_tokens_map['''cls_token''']  # `<s>`
						UpperCAmelCase   		=				tokenizer.special_tokens_map['''sep_token''']  # `</s>`
			elif args.tokenizer_type == "gpt2":
						UpperCAmelCase   		=				GPTaTokenizer.from_pretrained(args.tokenizer_name )
						UpperCAmelCase   		=				tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
						UpperCAmelCase   		=				tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

			logger.info(F"""Loading text from {args.file_path}""" )
			with open(args.file_path			,			'''r'''			,			encoding='''utf8''' ) as fp:
						UpperCAmelCase   		=				fp.readlines()

			logger.info('''Start encoding''' )
			logger.info(F"""{len(UpperCamelCase__ )} examples to process.""" )

			UpperCAmelCase   		=				[]
			UpperCAmelCase   		=				0
			UpperCAmelCase   		=				1_0000
			UpperCAmelCase   		=				time.time()
			for text in data:
						UpperCAmelCase   		=				F"""{bos} {text.strip()} {sep}"""
						UpperCAmelCase   		=				tokenizer.encode(UpperCamelCase__			,			add_special_tokens=UpperCamelCase__ )
						rslt.append(UpperCamelCase__ )

						iter += 1
						if iter % interval == 0:
									UpperCAmelCase   		=				time.time()
									logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
									UpperCAmelCase   		=				time.time()
			logger.info('''Finished binarization''' )
			logger.info(F"""{len(UpperCamelCase__ )} examples processed.""" )

			UpperCAmelCase   		=				F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
			UpperCAmelCase   		=				tokenizer.vocab_size
			if vocab_size < (1 << 16):
						UpperCAmelCase   		=				[np.uintaa(UpperCamelCase__ ) for d in rslt]
			else:
						UpperCAmelCase   		=				[np.intaa(UpperCamelCase__ ) for d in rslt]
			random.shuffle(rslt_ )
			logger.info(F"""Dump to {dp_file}""" )
			with open(UpperCamelCase__			,			'''wb''' ) as handle:
						pickle.dump(rslt_			,			UpperCamelCase__			,			protocol=pickle.HIGHEST_PROTOCOL )


if __name__ == "__main__":
				main()










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
				from ...onnx.config import PatchingSpec
				from ...tokenization_utils_base import PreTrainedTokenizerBase


__A				:    Dict       =   logging.get_logger(__name__)

__A				:    str       =   {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}

class 	A_       (a_			):
	UpperCAmelCase__					=			'''longformer'''
	def __init__(    self							,					_A = 5_1_2							,					_A = 2							,					_A = 1							,					_A = 0							,					_A = 2							,					_A = 3_0_5_2_2							,					_A = 7_6_8							,					_A = 1_2							,					_A = 1_2							,					_A = 3_0_7_2							,					_A = "gelu"							,					_A = 0.1							,					_A = 0.1							,					_A = 5_1_2							,					_A = 2							,					_A = 0.02							,					_A = 1E-12							,					_A = False							,					**_A							,					):






				'''simple docstring'''







				super().__init__(pad_token_id=_A							,					**_A		)

				UpperCAmelCase   		=				attention_window
				UpperCAmelCase   		=				sep_token_id
				UpperCAmelCase   		=				bos_token_id
				UpperCAmelCase   		=				eos_token_id
				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				layer_norm_eps
				UpperCAmelCase   		=				onnx_export

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A = "default"							,					_A = None		):






				'''simple docstring'''







				super().__init__(_A							,					_A							,					_A		)
				UpperCAmelCase   		=				True
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				if self.task == "multiple-choice":
							UpperCAmelCase   		=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							UpperCAmelCase   		=				{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				        ('''global_attention_mask''', dynamic_axis),
				    ]		)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				super().outputs
				if self.task == "default":
							UpperCAmelCase   		=				{0: '''batch'''}
				return outputs
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return 1E-4
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return max(super().default_onnx_opset							,					1_4		)







	def      _lowercase				(    self							,					_A							,					_A = -1							,					_A = -1							,					_A = False							,					_A = None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				super().generate_dummy_inputs(
				    preprocessor=_A							,					batch_size=_A							,					seq_length=_A							,					is_pair=_A							,					framework=_A		)
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				UpperCAmelCase   		=				torch.zeros_like(inputs['''input_ids''']		)
				# make every second token global
				UpperCAmelCase   		=				1

				return inputs



