



'''simple docstring'''






import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__:     Optional[int]										=    get_tests_dir("fixtures/test_sentencepiece_bpe.model")

class        SCREAMING_SNAKE_CASE(							A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= BartphoTokenizer
	lowerCamelCase__    				= False
	lowerCamelCase__    				= True







	def   A			(		self    :  int	)    ->	int:
								super().setUp()

								UpperCAmelCase				: Optional[int]											=	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
								UpperCAmelCase				: List[str]											=	dict(zip(__snake_case	,    range(len(__snake_case	)	)	)	)
								UpperCAmelCase				: List[Any]											=	{'''unk_token''': '''<unk>'''}

								UpperCAmelCase				: List[Any]											=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES['''monolingual_vocab_file''']	)
								with open(self.monolingual_vocab_file	,    '''w'''	,    encoding='''utf-8'''	) as fp:
															for token in vocab_tokens:
																						fp.write(F"""{token} {vocab_tokens[token]}\n"""	)

								UpperCAmelCase				: str											=	BartphoTokenizer(__snake_case	,    self.monolingual_vocab_file	,    **self.special_tokens_map	)
								tokenizer.save_pretrained(self.tmpdirname	)







	def   A			(		self    :  List[Any]	,    **__snake_case    :  int	)    ->	Union[str, Any]:
								kwargs.update(self.special_tokens_map	)
								return BartphoTokenizer.from_pretrained(self.tmpdirname	,    **__snake_case	)







	def   A			(		self    :  Any	,    __snake_case    :  List[str]	)    ->	List[Any]:
								UpperCAmelCase				: List[Any]											=	'''This is a là test'''
								UpperCAmelCase				: Optional[int]											=	'''This is a<unk><unk> test'''
								return input_text, output_text







	def   A			(		self    :  str	)    ->	List[Any]:
								UpperCAmelCase				: int											=	BartphoTokenizer(__snake_case	,    self.monolingual_vocab_file	,    **self.special_tokens_map	)
								UpperCAmelCase				: List[str]											=	'''This is a là test'''
								UpperCAmelCase				: Dict											=	'''▁This ▁is ▁a ▁l à ▁t est'''.split()
								UpperCAmelCase				: Dict											=	tokenizer.tokenize(__snake_case	)
								self.assertListEqual(__snake_case	,    __snake_case	)

								UpperCAmelCase				: Tuple											=	tokens + [tokenizer.unk_token]
								UpperCAmelCase				: List[Any]											=	[4, 5, 6, 3, 3, 7, 8, 3]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case	)	,    __snake_case	)










'''simple docstring'''






from typing import List, Optional, Union

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


UpperCamelCase__:     Optional[Any]										=    logging.get_logger(__name__)

UpperCamelCase__:     Tuple										=    {
    "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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """time_series_transformer"""
	lowerCamelCase__    				= {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}







	def __init__(		self    :  str	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  str = "student_t"	,    __snake_case    :  str = "nll"	,    __snake_case    :  int = 1	,    __snake_case    :  List[int] = [1, 2, 3, 4, 5, 6, 7]	,    __snake_case    :  Optional[Union[str, bool]] = "mean"	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  int = 32	,    __snake_case    :  int = 32	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  bool = True	,    __snake_case    :  str = "gelu"	,    __snake_case    :  int = 64	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  int = 100	,    __snake_case    :  float = 0.02	,    __snake_case    :  Optional[Any]=True	,    **__snake_case    :  List[Any]	,    )    ->	List[str]:
								# time series specific configuration
								UpperCAmelCase				: List[Any]											=	prediction_length
								UpperCAmelCase				: List[Any]											=	context_length or prediction_length
								UpperCAmelCase				: Tuple											=	distribution_output
								UpperCAmelCase				: Optional[Any]											=	loss
								UpperCAmelCase				: Tuple											=	input_size
								UpperCAmelCase				: Optional[int]											=	num_time_features
								UpperCAmelCase				: Dict											=	lags_sequence
								UpperCAmelCase				: Any											=	scaling
								UpperCAmelCase				: Tuple											=	num_dynamic_real_features
								UpperCAmelCase				: Any											=	num_static_real_features
								UpperCAmelCase				: Optional[int]											=	num_static_categorical_features
								if cardinality and num_static_categorical_features > 0:
															if len(__snake_case	) != num_static_categorical_features:
																						raise ValueError(
																						    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''	)
															UpperCAmelCase				: Any											=	cardinality
								else:
															UpperCAmelCase				: Optional[Any]											=	[0]
								if embedding_dimension and num_static_categorical_features > 0:
															if len(__snake_case	) != num_static_categorical_features:
																						raise ValueError(
																						    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''	)
															UpperCAmelCase				: Optional[int]											=	embedding_dimension
								else:
															UpperCAmelCase				: Optional[Any]											=	[min(50	,    (cat + 1) // 2	) for cat in self.cardinality]
								UpperCAmelCase				: List[Any]											=	num_parallel_samples

								# Transformer architecture configuration
								UpperCAmelCase				: int											=	input_size * len(__snake_case	) + self._number_of_features
								UpperCAmelCase				: int											=	d_model
								UpperCAmelCase				: str											=	encoder_attention_heads
								UpperCAmelCase				: str											=	decoder_attention_heads
								UpperCAmelCase				: List[str]											=	encoder_ffn_dim
								UpperCAmelCase				: Any											=	decoder_ffn_dim
								UpperCAmelCase				: Any											=	encoder_layers
								UpperCAmelCase				: str											=	decoder_layers

								UpperCAmelCase				: Optional[int]											=	dropout
								UpperCAmelCase				: Union[str, Any]											=	attention_dropout
								UpperCAmelCase				: List[Any]											=	activation_dropout
								UpperCAmelCase				: Optional[int]											=	encoder_layerdrop
								UpperCAmelCase				: Dict											=	decoder_layerdrop

								UpperCAmelCase				: Tuple											=	activation_function
								UpperCAmelCase				: Dict											=	init_std

								UpperCAmelCase				: Optional[int]											=	use_cache

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







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





