





"""simple docstring"""





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_UpperCamelCase							=      {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_UpperCamelCase							=      {
    '''gpt-neox-20b''': 2048,
}
class 		SCREAMING_SNAKE_CASE_							(    __a   ):
				"""simple docstring"""
				__snake_case   :       str	 =		VOCAB_FILES_NAMES
				__snake_case   :       List[str]	 =		PRETRAINED_VOCAB_FILES_MAP
				__snake_case   :       Optional[int]	 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case   :       str	 =		['''input_ids''', '''attention_mask''']




				def __init__(     self  :Any						,    __lowercase  :List[Any]=None						,    __lowercase  :Optional[Any]=None						,    __lowercase  :Any=None						,    __lowercase  :List[Any]="<|endoftext|>"						,    __lowercase  :Dict="<|endoftext|>"						,    __lowercase  :List[str]="<|endoftext|>"						,    __lowercase  :int=False						,    **__lowercase  :Optional[Any]						,    ):
											super().__init__(
											    snake_case__						,    snake_case__						,    tokenizer_file=snake_case__						,    unk_token=snake_case__						,    bos_token=snake_case__						,    eos_token=snake_case__						,    add_prefix_space=snake_case__						,    **snake_case__						,    )

											__lowerCamelCase      :      str         =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
											if pre_tok_state.get('''add_prefix_space'''						,    snake_case__			) != add_prefix_space:
																		__lowerCamelCase      :      List[str]         =getattr(snake_case__						,    pre_tok_state.pop('''type'''			)			)
																		__lowerCamelCase      :      Optional[int]         =add_prefix_space
																		__lowerCamelCase      :      Any         =pre_tok_class(**snake_case__			)

											__lowerCamelCase      :      Optional[int]         =add_prefix_space




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :str						,    __lowercase  :Optional[str] = None			):
											__lowerCamelCase      :      str         =self._tokenizer.model.save(snake_case__						,    name=snake_case__			)
											return tuple(snake_case__			)




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :"Conversation"			):
											__lowerCamelCase      :      str         =[]
											for is_user, text in conversation.iter_texts():
																		input_ids.extend(self.encode(snake_case__						,    add_special_tokens=snake_case__			) + [self.eos_token_id]			)

											if len(snake_case__			) > self.model_max_length:
																		__lowerCamelCase      :      Any         =input_ids[-self.model_max_length :]
											return input_ids











"""simple docstring"""





from typing import List, Optional, Union

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


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    '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_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		"""time_series_transformer"""
				__snake_case   :       Any	 =		{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}




				def __init__(     self  :List[Any]						,    __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 = 0.02						,    __lowercase  :int=True						,    **__lowercase  :Optional[Any]						,    ):
											# time series specific configuration
											__lowerCamelCase      :      Tuple         =prediction_length
											__lowerCamelCase      :      List[Any]         =context_length or prediction_length
											__lowerCamelCase      :      Dict         =distribution_output
											__lowerCamelCase      :      str         =loss
											__lowerCamelCase      :      Tuple         =input_size
											__lowerCamelCase      :      int         =num_time_features
											__lowerCamelCase      :      int         =lags_sequence
											__lowerCamelCase      :      Optional[int]         =scaling
											__lowerCamelCase      :      str         =num_dynamic_real_features
											__lowerCamelCase      :      Optional[Any]         =num_static_real_features
											__lowerCamelCase      :      List[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`'''			)
																		__lowerCamelCase      :      Optional[int]         =cardinality
											else:
																		__lowerCamelCase      :      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`'''			)
																		__lowerCamelCase      :      int         =embedding_dimension
											else:
																		__lowerCamelCase      :      Optional[int]         =[min(50						,    (cat + 1) // 2			) for cat in self.cardinality]
											__lowerCamelCase      :      List[str]         =num_parallel_samples

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

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

											__lowerCamelCase      :      Tuple         =activation_function
											__lowerCamelCase      :      str         =init_std

											__lowerCamelCase      :      Dict         =use_cache

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




				@property
				def 					__lowercase  (     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
											)




