
"""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
)
_snake_case          =						logging.getLogger(__name__)






def 	__snake_case  (    ):






							"""simple docstring"""



							_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=SCREAMING_SNAKE_CASE     ,  default='data/dump.txt'     ,  help='The path to the data.'						)
							parser.add_argument('--tokenizer_type'     ,  type=SCREAMING_SNAKE_CASE     ,  default='bert'     ,  choices=['bert', 'roberta', 'gpt2']						)
							parser.add_argument('--tokenizer_name'     ,  type=SCREAMING_SNAKE_CASE     ,  default='bert-base-uncased'     ,  help='The tokenizer to use.'						)
							parser.add_argument('--dump_file'     ,  type=SCREAMING_SNAKE_CASE     ,  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(SCREAMING_SNAKE_CASE						)} examples to process."""						)

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

														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(SCREAMING_SNAKE_CASE						)} examples processed."""						)

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


if __name__ == "__main__":
	main()








"""simple docstring"""



import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_snake_case          =						logging.get_logger(__name__)
class        _SCREAMING_SNAKE_CASE  (  enum.Enum							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Any							=					0
				SCREAMING_SNAKE_CASE_:							str							=					1






@add_end_docstrings(UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							int							=					"generated"


				def __init__(					self	:			Optional[Any]				,     *UpperCAmelCase_	:			Dict				,     **UpperCAmelCase_	:			Union[str, Any]  )		->  Optional[Any]:







											"""simple docstring"""




											super().__init__(*UpperCAmelCase_				,     **UpperCAmelCase_  )

											self.check_model_type(
											    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
											    if self.framework == 'tf'
											    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING  )


				def 				__lowerCamelCase	(					self	:			List[str]				,     UpperCAmelCase_	:			Any=None				,     UpperCAmelCase_	:			List[str]=None				,     UpperCAmelCase_	:			Union[str, Any]=None				,     UpperCAmelCase_	:			List[str]=None				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Optional[Any]=None				,     **UpperCAmelCase_	:			Union[str, Any]				,     )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			{}
											if truncation is not None:
																		_lowerCAmelCase  	=			truncation

											_lowerCAmelCase  	=			generate_kwargs

											_lowerCAmelCase  	=			{}
											if return_tensors is not None and return_type is None:
																		_lowerCAmelCase  	=			ReturnType.TENSORS if return_tensors else ReturnType.TEXT
											if return_type is not None:
																		_lowerCAmelCase  	=			return_type

											if clean_up_tokenization_spaces is not None:
																		_lowerCAmelCase  	=			clean_up_tokenization_spaces

											if stop_sequence is not None:
																		_lowerCAmelCase  	=			self.tokenizer.encode(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_  )
																		if len(UpperCAmelCase_  ) > 1:
																									warnings.warn(
																									    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
																									    ' the stop sequence will be used as the stop sequence string in the interim.'  )
																		_lowerCAmelCase  	=			stop_sequence_ids[0]

											return preprocess_params, forward_params, postprocess_params


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  Optional[Any]:







											"""simple docstring"""




											return True


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     *UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			int  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.model.config.prefix if self.model.config.prefix is not None else ''
											if isinstance(args[0]				,     UpperCAmelCase_  ):
																		if self.tokenizer.pad_token_id is None:
																									raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'  )
																		_lowerCAmelCase  	=			([prefix + arg for arg in args[0]],)
																		_lowerCAmelCase  	=			True

											elif isinstance(args[0]				,     UpperCAmelCase_  ):
																		_lowerCAmelCase  	=			(prefix + args[0],)
																		_lowerCAmelCase  	=			False
											else:
																		raise ValueError(
																		    F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""  )
											_lowerCAmelCase  	=			self.tokenizer(*UpperCAmelCase_				,     padding=UpperCAmelCase_				,     truncation=UpperCAmelCase_				,     return_tensors=self.framework  )
											# This is produced by tokenizers but is an invalid generate kwargs
											if "token_type_ids" in inputs:
																		del inputs["token_type_ids"]
											return inputs


				def __call__(					self	:			Union[str, Any]				,     *UpperCAmelCase_	:			Optional[Any]				,     **UpperCAmelCase_	:			Tuple  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			super().__call__(*UpperCAmelCase_				,     **UpperCAmelCase_  )
											if (
											    isinstance(args[0]				,     UpperCAmelCase_  )
											    and all(isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ) for el in args[0]  )
											    and all(len(UpperCAmelCase_  ) == 1 for res in result  )
											):
																		return [res[0] for res in result]
											return result


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Dict=TruncationStrategy.DO_NOT_TRUNCATE				,     **UpperCAmelCase_	:			Tuple  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			self._parse_and_tokenize(UpperCAmelCase_				,     truncation=UpperCAmelCase_				,     **UpperCAmelCase_  )
											return inputs


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			List[Any]				,     **UpperCAmelCase_	:			List[str]  )		->  Any:







											"""simple docstring"""




											if self.framework == "pt":
																		_lowerCAmelCase	,		_lowerCAmelCase  	=			model_inputs['input_ids'].shape
											elif self.framework == "tf":
																		_lowerCAmelCase	,		_lowerCAmelCase  	=			tf.shape(model_inputs['input_ids']  ).numpy()

											_lowerCAmelCase  	=			generate_kwargs.get('min_length'				,     self.model.config.min_length  )
											_lowerCAmelCase  	=			generate_kwargs.get('max_length'				,     self.model.config.max_length  )
											self.check_inputs(UpperCAmelCase_				,     generate_kwargs['min_length']				,     generate_kwargs['max_length']  )
											_lowerCAmelCase  	=			self.model.generate(**UpperCAmelCase_				,     **UpperCAmelCase_  )
											_lowerCAmelCase  	=			output_ids.shape[0]
											if self.framework == "pt":
																		_lowerCAmelCase  	=			output_ids.reshape(UpperCAmelCase_				,     out_b // in_b				,     *output_ids.shape[1:]  )
											elif self.framework == "tf":
																		_lowerCAmelCase  	=			tf.reshape(UpperCAmelCase_				,     (in_b, out_b // in_b, *output_ids.shape[1:])  )
											return {"output_ids": output_ids}







				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Union[str, Any]=ReturnType.TEXT				,     UpperCAmelCase_	:			Optional[Any]=False  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			[]
											for output_ids in model_outputs["output_ids"][0]:
																		if return_type == ReturnType.TENSORS:
																									_lowerCAmelCase  	=			{F"""{self.return_name}_token_ids""": output_ids}
																		elif return_type == ReturnType.TEXT:
																									_lowerCAmelCase  	=			{
																									    F"""{self.return_name}_text""": self.tokenizer.decode(
																									        UpperCAmelCase_				,     skip_special_tokens=UpperCAmelCase_				,     clean_up_tokenization_spaces=UpperCAmelCase_				,     )
																									}
																		records.append(UpperCAmelCase_  )
											return records






@add_end_docstrings(UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[Any]							=					"summary"


				def __call__(					self	:			Optional[int]				,     *UpperCAmelCase_	:			Optional[Any]				,     **UpperCAmelCase_	:			Dict  )		->  int:







											"""simple docstring"""




											return super().__call__(*UpperCAmelCase_				,     **UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  bool:







											"""simple docstring"""




											if max_length < min_length:
																		logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}."""  )

											if input_length < max_length:
																		logger.warning(
																		    F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
																		    'a summarization task, where outputs shorter than the input are typically wanted, you might '
																		    F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""  )






@add_end_docstrings(UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					"translation"


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			int  )		->  Any:







											"""simple docstring"""




											if input_length > 0.9 * max_length:
																		logger.warning(
																		    F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
																		    'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'  )
											return True


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     *UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]=TruncationStrategy.DO_NOT_TRUNCATE				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Dict=None  )		->  Optional[int]:







											"""simple docstring"""




											if getattr(self.tokenizer				,     '_build_translation_inputs'				,     UpperCAmelCase_  ):
																		return self.tokenizer._build_translation_inputs(
																		    *UpperCAmelCase_				,     return_tensors=self.framework				,     truncation=UpperCAmelCase_				,     src_lang=UpperCAmelCase_				,     tgt_lang=UpperCAmelCase_  )
											else:
																		return super()._parse_and_tokenize(*UpperCAmelCase_				,     truncation=UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Union[str, Any]=None				,     **UpperCAmelCase_	:			Optional[int]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			super()._sanitize_parameters(**UpperCAmelCase_  )
											if src_lang is not None:
																		_lowerCAmelCase  	=			src_lang
											if tgt_lang is not None:
																		_lowerCAmelCase  	=			tgt_lang
											if src_lang is None and tgt_lang is None:
																		# Backward compatibility, direct arguments use is preferred.
																		_lowerCAmelCase  	=			kwargs.get('task'				,     self.task  )
																		_lowerCAmelCase  	=			task.split('_'  )
																		if task and len(UpperCAmelCase_  ) == 4:
																									# translation, XX, to YY
																									_lowerCAmelCase  	=			items[1]
																									_lowerCAmelCase  	=			items[3]
											return preprocess_params, forward_params, postprocess_params







				def __call__(					self	:			Optional[int]				,     *UpperCAmelCase_	:			int				,     **UpperCAmelCase_	:			Optional[int]  )		->  Union[str, Any]:







											"""simple docstring"""




											return super().__call__(*UpperCAmelCase_				,     **UpperCAmelCase_  )






