






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   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   str							=  '''time_series_transformer'''
		_snake_case   :   Union[str, Any]							=  {
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''num_hidden_layers''': '''encoder_layers''',
		}



		def __init__(							self    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "student_t"    ,		_UpperCamelCase = "nll"    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = [1, 2, 3, 4, 5, 6, 7]    ,		_UpperCamelCase = "mean"    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 3_2    ,		_UpperCamelCase = 3_2    ,		_UpperCamelCase = 2    ,		_UpperCamelCase = 2    ,		_UpperCamelCase = 2    ,		_UpperCamelCase = 2    ,		_UpperCamelCase = True    ,		_UpperCamelCase = "gelu"    ,		_UpperCamelCase = 6_4    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 1_0_0    ,		_UpperCamelCase = 0.02    ,		_UpperCamelCase=True    ,		**_UpperCamelCase    ,		)     ->							Union[str, Any]:
							# time series specific configuration
							UpperCAmelCase_   :				int						=						prediction_length
							UpperCAmelCase_   :				List[str]						=						context_length or prediction_length
							UpperCAmelCase_   :				Union[str, Any]						=						distribution_output
							UpperCAmelCase_   :				Optional[int]						=						loss
							UpperCAmelCase_   :				Dict						=						input_size
							UpperCAmelCase_   :				Tuple						=						num_time_features
							UpperCAmelCase_   :				Dict						=						lags_sequence
							UpperCAmelCase_   :				Dict						=						scaling
							UpperCAmelCase_   :				Dict						=						num_dynamic_real_features
							UpperCAmelCase_   :				List[Any]						=						num_static_real_features
							UpperCAmelCase_   :				Tuple						=						num_static_categorical_features
							if cardinality and num_static_categorical_features > 0:
												if len(_UpperCamelCase       ) != num_static_categorical_features:
																	raise ValueError(
																	    'The cardinality should be a list of the same length as `num_static_categorical_features`'       )
												UpperCAmelCase_   :				List[str]						=						cardinality
							else:
												UpperCAmelCase_   :				str						=						[0]
							if embedding_dimension and num_static_categorical_features > 0:
												if len(_UpperCamelCase       ) != num_static_categorical_features:
																	raise ValueError(
																	    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'       )
												UpperCAmelCase_   :				List[Any]						=						embedding_dimension
							else:
												UpperCAmelCase_   :				Any						=						[min(5_0    ,		(cat + 1) // 2       ) for cat in self.cardinality]
							UpperCAmelCase_   :				Union[str, Any]						=						num_parallel_samples

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

							UpperCAmelCase_   :				Any						=						dropout
							UpperCAmelCase_   :				List[str]						=						attention_dropout
							UpperCAmelCase_   :				str						=						activation_dropout
							UpperCAmelCase_   :				Union[str, Any]						=						encoder_layerdrop
							UpperCAmelCase_   :				List[str]						=						decoder_layerdrop

							UpperCAmelCase_   :				List[Any]						=						activation_function
							UpperCAmelCase_   :				int						=						init_std

							UpperCAmelCase_   :				Union[str, Any]						=						use_cache

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



		@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
							)














from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  '''umt5'''
		_snake_case   :   Union[str, Any]							=  ['''past_key_values''']



		def __init__(							self    ,		_UpperCamelCase=2_5_0_1_1_2    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=8    ,		_UpperCamelCase=None    ,		_UpperCamelCase=6    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=1E-6    ,		_UpperCamelCase=1.0    ,		_UpperCamelCase="gated-gelu"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase="T5Tokenizer"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=0    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		**_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__(
							    is_encoder_decoder=_UpperCamelCase    ,		tokenizer_class=_UpperCamelCase    ,		tie_word_embeddings=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		decoder_start_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Union[str, Any]						=						vocab_size
							UpperCAmelCase_   :				List[str]						=						d_model
							UpperCAmelCase_   :				Any						=						d_kv
							UpperCAmelCase_   :				Optional[int]						=						d_ff
							UpperCAmelCase_   :				List[Any]						=						num_layers
							UpperCAmelCase_   :				Optional[Any]						=						(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							UpperCAmelCase_   :				Optional[int]						=						num_heads
							UpperCAmelCase_   :				Optional[int]						=						relative_attention_num_buckets
							UpperCAmelCase_   :				Dict						=						relative_attention_max_distance
							UpperCAmelCase_   :				Tuple						=						dropout_rate
							UpperCAmelCase_   :				Union[str, Any]						=						layer_norm_epsilon
							UpperCAmelCase_   :				Optional[int]						=						initializer_factor
							UpperCAmelCase_   :				List[str]						=						feed_forward_proj
							UpperCAmelCase_   :				Any						=						use_cache

							UpperCAmelCase_   :				List[Any]						=						self.feed_forward_proj.split('-'       )
							UpperCAmelCase_   :				List[Any]						=						act_info[-1]
							UpperCAmelCase_   :				Union[str, Any]						=						act_info[0] == 'gated'

							if len(_UpperCamelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCamelCase       ) > 2:
												raise ValueError(
												    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
												    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
												    '\'gated-gelu\' or \'relu\''       )

							if feed_forward_proj == "gated-gelu":
												UpperCAmelCase_   :				Optional[int]						=						'gelu_new'



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return self.d_model



		@property
		def  __UpperCAmelCase      (							self       )     ->							Any:
							return self.num_heads



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							return self.num_layers





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							UpperCAmelCase_   :				str						=						{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
												UpperCAmelCase_   :				Optional[int]						=						'past_encoder_sequence + sequence'
												UpperCAmelCase_   :				str						=						{0: 'batch'}
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
												UpperCAmelCase_   :				Optional[int]						=						{0: 'batch', 1: 'decoder_sequence'}
												UpperCAmelCase_   :				Union[str, Any]						=						{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
												self.fill_with_past_key_values_(_UpperCamelCase    ,		direction='inputs'       )

							return common_inputs



		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_3



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 5E-4






