





from typing import List, Optional, Union

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


A       :   Tuple					= logging.get_logger(__name__)

A       :   Optional[int]					= {
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Any							     =       '''time_series_transformer'''
			__lowerCamelCase      :			str							     =       {
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			    '''num_hidden_layers''': '''encoder_layers''',
			}






			def __init__(     self  :       Dict				,      __lowerCAmelCase  :       Optional[int] = None				,      __lowerCAmelCase  :       Optional[int] = None				,      __lowerCAmelCase  :       str = "student_t"				,      __lowerCAmelCase  :       str = "nll"				,      __lowerCAmelCase  :       int = 1				,      __lowerCAmelCase  :       List[int] = [1, 2, 3, 4, 5, 6, 7]				,      __lowerCAmelCase  :       Optional[Union[str, bool]] = "mean"				,      __lowerCAmelCase  :       int = 0				,      __lowerCAmelCase  :       int = 0				,      __lowerCAmelCase  :       int = 0				,      __lowerCAmelCase  :       int = 0				,      __lowerCAmelCase  :       Optional[List[int]] = None				,      __lowerCAmelCase  :       Optional[List[int]] = None				,      __lowerCAmelCase  :       int = 32				,      __lowerCAmelCase  :       int = 32				,      __lowerCAmelCase  :       int = 2				,      __lowerCAmelCase  :       int = 2				,      __lowerCAmelCase  :       int = 2				,      __lowerCAmelCase  :       int = 2				,      __lowerCAmelCase  :       bool = True				,      __lowerCAmelCase  :       str = "gelu"				,      __lowerCAmelCase  :       int = 64				,      __lowerCAmelCase  :       float = 0.1				,      __lowerCAmelCase  :       float = 0.1				,      __lowerCAmelCase  :       float = 0.1				,      __lowerCAmelCase  :       float = 0.1				,      __lowerCAmelCase  :       float = 0.1				,      __lowerCAmelCase  :       int = 1_00				,      __lowerCAmelCase  :       float = 0.0_2				,      __lowerCAmelCase  :       Dict=True				,      **__lowerCAmelCase  :       Optional[Any]				,      )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  prediction_length
					A__     =  context_length or prediction_length
					A__     =  distribution_output
					A__     =  loss
					A__     =  input_size
					A__     =  num_time_features
					A__     =  lags_sequence
					A__     =  scaling
					A__     =  num_dynamic_real_features
					A__     =  num_static_real_features
					A__     =  num_static_categorical_features
					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__     =  cardinality
					else:
							A__     =  [0]
					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__     =  embedding_dimension
					else:
							A__     =  [min(50				,      (cat + 1) // 2  ) for cat in self.cardinality]
					A__     =  num_parallel_samples

					# Transformer architecture configuration
					A__     =  input_size * len(__lowerCAmelCase  ) + self._number_of_features
					A__     =  d_model
					A__     =  encoder_attention_heads
					A__     =  decoder_attention_heads
					A__     =  encoder_ffn_dim
					A__     =  decoder_ffn_dim
					A__     =  encoder_layers
					A__     =  decoder_layers

					A__     =  dropout
					A__     =  attention_dropout
					A__     =  activation_dropout
					A__     =  encoder_layerdrop
					A__     =  decoder_layerdrop

					A__     =  activation_function
					A__     =  init_std

					A__     =  use_cache

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







			@property
			def   a_			(     self  :       Optional[int]  )      ->      int:







					"""simple docstring"""






					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
					)










import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   __lowerCamelCase   (				__a  :Optional[int]       )						->							str:






		"""simple docstring"""

		A__     =  {}
		A__     =  tokenizer(example["""content"""]       ,	truncation=__a       )["""input_ids"""]
		A__     =  len(example["""content"""]       ) / len(output["""input_ids"""]       )
		return output


A       :   Union[str, Any]					= HfArgumentParser(PretokenizationArguments)
A       :   str					= parser.parse_args()
if args.num_workers is None:
					A       :   Any					= multiprocessing.cpu_count()
A       :   int					= AutoTokenizer.from_pretrained(args.tokenizer_dir)

A       :   Optional[int]					= time.time()
A       :   int					= load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

A       :   Optional[Any]					= time.time()
A       :   int					= ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

A       :   Tuple					= time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')



