






"""simple docstring"""







import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowerCAmelCase_	:    int            =      logging.get_logger(__name__)





class   UpperCamelCase_ :



  def __init__(      self ,		snake_case__ ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			question_encoder
         UpperCAmelCase	    =			generator
         UpperCAmelCase	    =			self.question_encoder



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			int:



         """simple docstring"""





         if os.path.isfile(snake_case__				):
                raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''				)
         os.makedirs(snake_case__ ,		exist_ok=snake_case__				)
         UpperCAmelCase	    =			os.path.join(snake_case__ ,		"""question_encoder_tokenizer"""				)
         UpperCAmelCase	    =			os.path.join(snake_case__ ,		"""generator_tokenizer"""				)
         self.question_encoder.save_pretrained(snake_case__				)
         self.generator.save_pretrained(snake_case__				)



  @classmethod
  def 		UpperCamelCase_				(      cls ,		snake_case__ ,		**snake_case__				)			->			Dict:



         """simple docstring"""





         from ..auto.tokenization_auto import AutoTokenizer

         UpperCAmelCase	    =			kwargs.pop("""config""" ,		snake_case__				)

         if config is None:
                UpperCAmelCase	    =			RagConfig.from_pretrained(snake_case__				)

         UpperCAmelCase	    =			AutoTokenizer.from_pretrained(
             snake_case__ ,		config=config.question_encoder ,		subfolder="""question_encoder_tokenizer"""				)
         UpperCAmelCase	    =			AutoTokenizer.from_pretrained(
             snake_case__ ,		config=config.generator ,		subfolder="""generator_tokenizer"""				)
         return cls(question_encoder=snake_case__ ,		generator=snake_case__				)



  def __call__(      self ,		*snake_case__ ,		**snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         return self.current_tokenizer(*snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         return self.generator.batch_decode(*snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Any:



         """simple docstring"""





         return self.generator.decode(*snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.question_encoder



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.generator



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = "longest" ,		snake_case__ = None ,		snake_case__ = True ,		**snake_case__ ,		)			->			BatchEncoding:



         """simple docstring"""





         warnings.warn(
             """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
             """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
             """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
             """details""" ,		snake_case__ ,		)
         if max_length is None:
                UpperCAmelCase	    =			self.current_tokenizer.model_max_length
         UpperCAmelCase	    =			self(
             snake_case__ ,		add_special_tokens=snake_case__ ,		return_tensors=snake_case__ ,		max_length=snake_case__ ,		padding=snake_case__ ,		truncation=snake_case__ ,		**snake_case__ ,		)
         if tgt_texts is None:
                return model_inputs
         # Process tgt_texts
         if max_target_length is None:
                UpperCAmelCase	    =			self.current_tokenizer.model_max_length
         UpperCAmelCase	    =			self(
             text_target=snake_case__ ,		add_special_tokens=snake_case__ ,		return_tensors=snake_case__ ,		padding=snake_case__ ,		max_length=snake_case__ ,		truncation=snake_case__ ,		**snake_case__ ,		)
         UpperCAmelCase	    =			labels["""input_ids"""]
         return model_inputs








"""simple docstring"""







from typing import List, Optional, Union

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


lowerCAmelCase_	:    str            =      logging.get_logger(__name__)

lowerCAmelCase_	:    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   UpperCamelCase_ (       a_  ):
  _A       :					int				=	'time_series_transformer'
  _A       :					List[Any]				=	{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }



  def __init__(      self ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = "student_t" ,		snake_case__ = "nll" ,		snake_case__ = 1 ,		snake_case__ = [1, 2, 3, 4, 5, 6, 7] ,		snake_case__ = "mean" ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = 32 ,		snake_case__ = 32 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = True ,		snake_case__ = "gelu" ,		snake_case__ = 64 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 1_00 ,		snake_case__ = 0.02 ,		snake_case__=True ,		**snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





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

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

         UpperCAmelCase	    =			dropout
         UpperCAmelCase	    =			attention_dropout
         UpperCAmelCase	    =			activation_dropout
         UpperCAmelCase	    =			encoder_layerdrop
         UpperCAmelCase	    =			decoder_layerdrop

         UpperCAmelCase	    =			activation_function
         UpperCAmelCase	    =			init_std

         UpperCAmelCase	    =			use_cache

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



  @property
  def 		UpperCamelCase_				(      self				)			->			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
         )
