


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase       :      List[Any]	=	logging.get_logger(__name__)

_lowercase       :      Dict	=	{"vocab_file": "sentencepiece.bpe.model"}

_lowercase       :      Union[str, Any]	=	{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

_lowercase       :      Dict	=	{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

_lowercase       :      int	=	"▁"






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= VOCAB_FILES_NAMES
				lowerCAmelCase 		= PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase 		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase 		= ['input_ids', 'attention_mask']

				def __init__(						self				,   a__				,   a__="<s>"				,   a__="</s>"				,   a__="</s>"				,   a__="<s>"				,   a__="<unk>"				,   a__="<pad>"				,   a__="<mask>"				,   a__ = None				,   **a__				,   )	->				None:
					# Mask token behave like a normal word, i.e. include the space before it
					A								=				AddedToken(a__				,   lstrip=a__				,   rstrip=a__   ) if isinstance(a__				,   a__   ) else mask_token

					A								=				{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=a__				,   eos_token=a__				,   unk_token=a__				,   sep_token=a__				,   cls_token=a__				,   pad_token=a__				,   mask_token=a__				,   sp_model_kwargs=self.sp_model_kwargs				,   **a__				,   )

					A								=				vocab_file
					A								=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(str(a__   )   )

					A								=				{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

					A								=				len(self.sp_model   ) - 1
					A								=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				List[int]:

					if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					A								=				[self.cls_token_id]
					A								=				[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None				,   a__ = False   )	->				List[int]:
					if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=a__				,   token_ids_a=a__				,   already_has_special_tokens=a__   )

					if token_ids_a is None:
						return [1] + ([0] * len(a__   )) + [1]
					return [1] + ([0] * len(a__   )) + [1, 1] + ([0] * len(a__   )) + [1]

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				List[int]:
					A								=				[self.sep_token_id]
					A								=				[self.cls_token_id]

					if token_ids_a is None:
						return len(cls + token_ids_a + sep   ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]

				@property
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					return len(self.sp_model   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				{self.convert_ids_to_tokens(a__   ): i for i in range(self.vocab_size   )}
					vocab.update(self.added_tokens_encoder   )
					return vocab

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[str]:
					return self.sp_model.encode(a__				,   out_type=a__   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Tuple:
					if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
					A								=				self.sp_model.PieceToId(a__   )

					return spm_id if spm_id else self.unk_token_id

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[Any]:
					if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(a__   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[str]:
					A								=				[]
					A								=				""""""
					A								=				False
					for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
							if not prev_is_special:
								out_string += " "
							out_string += self.sp_model.decode(a__   ) + token
							A								=				True
							A								=				[]
						else:
							current_sub_tokens.append(a__   )
							A								=				False
					out_string += self.sp_model.decode(a__   )
					return out_string.strip()

				def __getstate__(						self   )	->				int:
					A								=				self.__dict__.copy()
					A								=				None
					return state

				def __setstate__(						self				,   a__   )	->				Optional[Any]:
					A								=				d

					# for backward compatibility
					if not hasattr(self				,   """sp_model_kwargs"""   ):
						A								=				{}

					A								=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(self.vocab_file   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				Tuple[str]:
					if not os.path.isdir(a__   ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
						return
					A								=				os.path.join(
					    a__				,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

					if os.path.abspath(self.vocab_file   ) != os.path.abspath(a__   ) and os.path.isfile(self.vocab_file   ):
						copyfile(self.vocab_file				,   a__   )
					elif not os.path.isfile(self.vocab_file   ):
						with open(a__				,   """wb"""   ) as fi:
							A								=				self.sp_model.serialized_model_proto()
							fi.write(a__   )

					return (out_vocab_file,)







from typing import List, Optional, Union

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


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

_lowercase       :      List[str]	=	{
    "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       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'informer'
				lowerCAmelCase 		= {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				    'num_hidden_layers': 'encoder_layers',
				}

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

					# set cardinality
					if cardinality and num_static_categorical_features > 0:
						if len(a__   ) != 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]

					# set embedding_dimension
					if embedding_dimension and num_static_categorical_features > 0:
						if len(a__   ) != 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(self.lags_sequence   ) + 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

					# Informer
					A								=				attention_type
					A								=				sampling_factor
					A								=				distil

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

				@property
				def      _UpperCAmelCase					(						self   )	->				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
					)



