
"""simple docstring"""





from typing import List, Optional, Union

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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "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_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		'''time_series_transformer'''
		__lowercase							:  List[str]						   =		{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''num_hidden_layers''': '''encoder_layers''',
		}




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

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

				__SCREAMING_SNAKE_CASE            = dropout
				__SCREAMING_SNAKE_CASE            = attention_dropout
				__SCREAMING_SNAKE_CASE            = activation_dropout
				__SCREAMING_SNAKE_CASE            = encoder_layerdrop
				__SCREAMING_SNAKE_CASE            = decoder_layerdrop

				__SCREAMING_SNAKE_CASE            = activation_function
				__SCREAMING_SNAKE_CASE            = init_std

				__SCREAMING_SNAKE_CASE            = use_cache

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




		@property
		def 	snake_case_     (     self):
				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
				)











"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case     :		Union[str, Any]       						= {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case     :		int       						= [
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		_snake_case     :		str       						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


