'''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_	:		Tuple			=     logging.get_logger(__name__)




class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						str       ,		lowercase__					:						List[Any]       ,		lowercase__					:						Optional[int]					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						question_encoder
										_UpperCamelCase			:			Dict   			=						generator
										_UpperCamelCase			:			List[str]   			=						self.question_encoder





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						str					)							->Optional[Any]:


										'''simple docstring'''




										if os.path.isfile(lowercase__					):
																raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''					)
										os.makedirs(lowercase__       ,		exist_ok=lowercase__					)
										_UpperCamelCase			:			Union[str, Any]   			=						os.path.join(lowercase__       ,		"question_encoder_tokenizer"					)
										_UpperCamelCase			:			List[str]   			=						os.path.join(lowercase__       ,		"generator_tokenizer"					)
										self.question_encoder.save_pretrained(lowercase__					)
										self.generator.save_pretrained(lowercase__					)





				@classmethod
				def  snake_case__				(						cls					:						str       ,		lowercase__					:						Optional[Any]       ,		**lowercase__					:						int					)							->Dict:


										'''simple docstring'''




										from ..auto.tokenization_auto import AutoTokenizer

										_UpperCamelCase			:			List[str]   			=						kwargs.pop("config"       ,		lowercase__					)

										if config is None:
																_UpperCamelCase			:			Tuple   			=						RagConfig.from_pretrained(lowercase__					)

										_UpperCamelCase			:			Optional[int]   			=						AutoTokenizer.from_pretrained(
										    lowercase__       ,		config=config.question_encoder       ,		subfolder="question_encoder_tokenizer"					)
										_UpperCamelCase			:			Any   			=						AutoTokenizer.from_pretrained(
										    lowercase__       ,		config=config.generator       ,		subfolder="generator_tokenizer"					)
										return cls(question_encoder=lowercase__       ,		generator=lowercase__					)





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


										'''simple docstring'''




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





				def  snake_case__				(						self					:						Optional[Any]       ,		*lowercase__					:						Any       ,		**lowercase__					:						Union[str, Any]					)							->Tuple:


										'''simple docstring'''




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





				def  snake_case__				(						self					:						Tuple       ,		*lowercase__					:						Union[str, Any]       ,		**lowercase__					:						Optional[Any]					)							->Union[str, Any]:


										'''simple docstring'''




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





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


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						self.question_encoder





				def  snake_case__				(						self					:						Dict					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						self.generator







				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						List[str]       ,		lowercase__					:						Optional[List[str]] = None       ,		lowercase__					:						Optional[int] = None       ,		lowercase__					:						Optional[int] = None       ,		lowercase__					:						str = "longest"       ,		lowercase__					:						str = None       ,		lowercase__					:						bool = True       ,		**lowercase__					:						List[str]       ,		)							->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"       ,		lowercase__       ,		)
										if max_length is None:
																_UpperCamelCase			:			Optional[int]   			=						self.current_tokenizer.model_max_length
										_UpperCamelCase			:			Optional[Any]   			=						self(
										    lowercase__       ,		add_special_tokens=lowercase__       ,		return_tensors=lowercase__       ,		max_length=lowercase__       ,		padding=lowercase__       ,		truncation=lowercase__       ,		**lowercase__       ,		)
										if tgt_texts is None:
																return model_inputs
										# Process tgt_texts
										if max_target_length is None:
																_UpperCamelCase			:			List[Any]   			=						self.current_tokenizer.model_max_length
										_UpperCamelCase			:			Tuple   			=						self(
										    text_target=lowercase__       ,		add_special_tokens=lowercase__       ,		return_tensors=lowercase__       ,		padding=lowercase__       ,		max_length=lowercase__       ,		truncation=lowercase__       ,		**lowercase__       ,		)
										_UpperCamelCase			:			int   			=						labels["input_ids"]
										return model_inputs



'''simple docstring'''





from typing import List, Optional, Union

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


lowerCAmelCase_	:		Optional[int]			=     logging.get_logger(__name__)

lowerCAmelCase_	:		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     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''time_series_transformer'''
				UpperCAmelCase__							=							{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''num_hidden_layers''': '''encoder_layers''',
				}





				def __init__(						self					:						int       ,		lowercase__					:						Optional[int] = None       ,		lowercase__					:						Optional[int] = None       ,		lowercase__					:						str = "student_t"       ,		lowercase__					:						str = "nll"       ,		lowercase__					:						int = 1       ,		lowercase__					:						List[int] = [1, 2, 3, 4, 5, 6, 7]       ,		lowercase__					:						Optional[Union[str, bool]] = "mean"       ,		lowercase__					:						int = 0       ,		lowercase__					:						int = 0       ,		lowercase__					:						int = 0       ,		lowercase__					:						int = 0       ,		lowercase__					:						Optional[List[int]] = None       ,		lowercase__					:						Optional[List[int]] = None       ,		lowercase__					:						int = 32       ,		lowercase__					:						int = 32       ,		lowercase__					:						int = 2       ,		lowercase__					:						int = 2       ,		lowercase__					:						int = 2       ,		lowercase__					:						int = 2       ,		lowercase__					:						bool = True       ,		lowercase__					:						str = "gelu"       ,		lowercase__					:						int = 64       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						float = 0.1       ,		lowercase__					:						int = 100       ,		lowercase__					:						float = 0.0_2       ,		lowercase__					:						Optional[int]=True       ,		**lowercase__					:						Optional[Any]       ,		)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						prediction_length
										_UpperCamelCase			:			Optional[Any]   			=						context_length or prediction_length
										_UpperCamelCase			:			List[str]   			=						distribution_output
										_UpperCamelCase			:			Optional[Any]   			=						loss
										_UpperCamelCase			:			Tuple   			=						input_size
										_UpperCamelCase			:			Optional[int]   			=						num_time_features
										_UpperCamelCase			:			Union[str, Any]   			=						lags_sequence
										_UpperCamelCase			:			int   			=						scaling
										_UpperCamelCase			:			Dict   			=						num_dynamic_real_features
										_UpperCamelCase			:			str   			=						num_static_real_features
										_UpperCamelCase			:			Any   			=						num_static_categorical_features
										if cardinality and num_static_categorical_features > 0:
																if len(lowercase__					) != num_static_categorical_features:
																						raise ValueError(
																						    "The cardinality should be a list of the same length as `num_static_categorical_features`"					)
																_UpperCamelCase			:			Dict   			=						cardinality
										else:
																_UpperCamelCase			:			str   			=						[0]
										if embedding_dimension and num_static_categorical_features > 0:
																if len(lowercase__					) != num_static_categorical_features:
																						raise ValueError(
																						    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"					)
																_UpperCamelCase			:			int   			=						embedding_dimension
										else:
																_UpperCamelCase			:			Optional[Any]   			=						[min(50       ,		(cat + 1) // 2					) for cat in self.cardinality]
										_UpperCamelCase			:			Union[str, Any]   			=						num_parallel_samples

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

										_UpperCamelCase			:			int   			=						dropout
										_UpperCamelCase			:			Optional[int]   			=						attention_dropout
										_UpperCamelCase			:			int   			=						activation_dropout
										_UpperCamelCase			:			List[Any]   			=						encoder_layerdrop
										_UpperCamelCase			:			int   			=						decoder_layerdrop

										_UpperCamelCase			:			List[Any]   			=						activation_function
										_UpperCamelCase			:			Any   			=						init_std

										_UpperCamelCase			:			Optional[Any]   			=						use_cache

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







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


