






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






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

	logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""       )
	if args.tokenizer_type == "bert":
		A_		:    str						     = BertTokenizer.from_pretrained(args.tokenizer_name       )
		A_		:    List[str]						     = tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
		A_		:    Any						     = tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
	elif args.tokenizer_type == "roberta":
		A_		:    Dict						     = RobertaTokenizer.from_pretrained(args.tokenizer_name       )
		A_		:    List[Any]						     = tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
		A_		:    Optional[Any]						     = tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
	elif args.tokenizer_type == "gpt2":
		A_		:    str						     = GPTaTokenizer.from_pretrained(args.tokenizer_name       )
		A_		:    Dict						     = tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
		A_		:    Optional[Any]						     = 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:
		A_		:    Optional[int]						     = fp.readlines()

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

	A_		:    List[Any]						     = []
	A_		:    List[Any]						     = 0
	A_		:    Optional[Any]						     = 10_000
	A_		:    int						     = time.time()
	for text in data:
		A_		:    List[str]						     = F"""{bos} {text.strip()} {sep}"""
		A_		:    Tuple						     = tokenizer.encode(snake_case__ , add_special_tokens=snake_case__       )
		rslt.append(snake_case__       )

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

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


if __name__ == "__main__":
							main()









"""simple docstring"""
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase       =				logging.get_logger(__name__)

_lowerCAmelCase       =				{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[Any] 			=					"""informer"""
	_A							:					Dict 			=					{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}





	def __init__(self  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "student_t"  ,  lowerCAmelCase_ = "nll"  ,  lowerCAmelCase_ = 1  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "mean"  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = 64  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = "gelu"  ,  lowerCAmelCase_ = 0.05  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 100  ,  lowerCAmelCase_ = 0.02  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_ = "prob"  ,  lowerCAmelCase_ = 5  ,  lowerCAmelCase_ = True  ,  **lowerCAmelCase_  ,  ):
		# time series specific configuration
		A_		:    Optional[Any]						     = prediction_length
		A_		:    Dict						     = context_length or prediction_length
		A_		:    Dict						     = distribution_output
		A_		:    Tuple						     = loss
		A_		:    Dict						     = input_size
		A_		:    Union[str, Any]						     = num_time_features
		A_		:    List[str]						     = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
		A_		:    Optional[int]						     = scaling
		A_		:    Optional[Any]						     = num_dynamic_real_features
		A_		:    Tuple						     = num_static_real_features
		A_		:    Tuple						     = num_static_categorical_features

		# set cardinality
		if cardinality and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    List[str]						     = cardinality
		else:
			A_		:    List[Any]						     = [0]

		# set embedding_dimension
		if embedding_dimension and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    int						     = embedding_dimension
		else:
			A_		:    Optional[int]						     = [min(50  ,  (cat + 1) // 2				) for cat in self.cardinality]

		A_		:    Optional[int]						     = num_parallel_samples

		# Transformer architecture configuration
		A_		:    Union[str, Any]						     = input_size * len(self.lags_sequence				) + self._number_of_features
		A_		:    Dict						     = d_model
		A_		:    Dict						     = encoder_attention_heads
		A_		:    Dict						     = decoder_attention_heads
		A_		:    Any						     = encoder_ffn_dim
		A_		:    Tuple						     = decoder_ffn_dim
		A_		:    Tuple						     = encoder_layers
		A_		:    Optional[int]						     = decoder_layers

		A_		:    List[str]						     = dropout
		A_		:    List[str]						     = attention_dropout
		A_		:    Any						     = activation_dropout
		A_		:    Any						     = encoder_layerdrop
		A_		:    List[Any]						     = decoder_layerdrop

		A_		:    str						     = activation_function
		A_		:    Optional[Any]						     = init_std

		A_		:    Optional[int]						     = use_cache

		# Informer
		A_		:    Dict						     = attention_type
		A_		:    List[Any]						     = sampling_factor
		A_		:    List[Any]						     = distil

		super().__init__(is_encoder_decoder=lowerCAmelCase_  ,  **lowerCAmelCase_				)





	@property
	def  lowerCamelCase(self				):
		return (
		    sum(self.embedding_dimension				)
		    + self.num_dynamic_real_features
		    + self.num_time_features
		    + self.num_static_real_features
		    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
		)

