
"""simple docstring"""


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_    							=					logging.getLogger(__name__)






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

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

			logger.info('''Start encoding'''						)
			logger.info(f"{len(lowerCAmelCase__						)} examples to process."						)

			lowerCamelCase_					=  []
			lowerCamelCase_					=  0
			lowerCamelCase_					=  10_000
			lowerCamelCase_					=  time.time()
			for text in data:
						lowerCamelCase_					=  f"{bos} {text.strip()} {sep}"
						lowerCamelCase_					=  tokenizer.encode(lowerCAmelCase__	,add_special_tokens=lowerCAmelCase__						)
						rslt.append(lowerCAmelCase__						)

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

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


if __name__ == "__main__":
						main()









from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DPTForDepthEstimation",
		    "DPTForSemanticSegmentation",
		    "DPTModel",
		    "DPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
