
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[Any]          = ["image_processor", "tokenizer"]
	_UpperCAmelCase	:		Optional[Any]          = "CLIPImageProcessor"
	_UpperCAmelCase	:		Union[str, Any]          = ("CLIPTokenizer", "CLIPTokenizerFast")



	def __init__(  self     :   Union[str, Any]      ,  lowercase     :   List[Any]=None      ,  lowercase     :   Any=None      ,  **lowercase     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      None
				if "feature_extractor" in kwargs:
							warnings.warn(
							    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
							    ' instead.'      ,  lowercase      ,  )
							_snake_case          =      kwargs.pop('feature_extractor'     )

				_snake_case          =      image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
							raise ValueError('You need to specify an `image_processor`.'     )
				if tokenizer is None:
							raise ValueError('You need to specify a `tokenizer`.'     )

				super().__init__(lowercase      ,  lowercase     )



	def __call__(  self     :   List[str]      ,  lowercase     :   int=None      ,  lowercase     :   Union[str, Any]=None      ,  lowercase     :   List[str]=None      ,  **lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				if text is None and images is None:
							raise ValueError('You have to specify either text or images. Both cannot be none.'     )

				if text is not None:
							_snake_case          =      self.tokenizer(lowercase      ,  return_tensors=lowercase      ,  **lowercase     )

				if images is not None:
							_snake_case          =      self.image_processor(lowercase      ,  return_tensors=lowercase      ,  **lowercase     )

				if text is not None and images is not None:
							_snake_case          =      image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowercase     )      ,  tensor_type=lowercase     )



	def 			A							(  self     :   Tuple      ,  *lowercase     :   int      ,  **lowercase     :   Tuple     ):


				'''simple docstring'''


				return self.tokenizer.batch_decode(*lowercase      ,  **lowercase     )



	def 			A							(  self     :   List[str]      ,  *lowercase     :   List[str]      ,  **lowercase     :   Any     ):


				'''simple docstring'''


				return self.tokenizer.decode(*lowercase      ,  **lowercase     )



	@property
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer.model_input_names
				_snake_case          =      self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )



	@property
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				warnings.warn(
				    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,  lowercase      ,  )
				return self.image_processor_class





	@property
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				warnings.warn(
				    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,  lowercase      ,  )
				return self.image_processor

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
)
_lowerCamelCase	:    Tuple          =      logging.getLogger(__name__)






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

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

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

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

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

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


if __name__ == "__main__":
			main()