


from typing import TYPE_CHECKING

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


UpperCAmelCase    =					{"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase    =					[
       """UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """UniSpeechForCTC""",
       """UniSpeechForPreTraining""",
       """UniSpeechForSequenceClassification""",
       """UniSpeechModel""",
       """UniSpeechPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech import (
          UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechForCTC,
          UniSpeechForPreTraining,
          UniSpeechForSequenceClassification,
          UniSpeechModel,
          UniSpeechPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase    =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



from typing import List, Optional, Union

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


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"informer"
 __snake_case				=						{
     "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 = None	,   _UpperCAmelCase = "mean"	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = 64	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = True	,   _UpperCAmelCase = "gelu"	,   _UpperCAmelCase = 0.05	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 1_00	,   _UpperCAmelCase = 0.02	,   _UpperCAmelCase=True	,   _UpperCAmelCase = "prob"	,   _UpperCAmelCase = 5	,   _UpperCAmelCase = True	,   **_UpperCAmelCase	,   ):
       # time series specific configuration
       snake_case_ =   prediction_length
       snake_case_ =   context_length or prediction_length
       snake_case_ =   distribution_output
       snake_case_ =   loss
       snake_case_ =   input_size
       snake_case_ =   num_time_features
       snake_case_ =   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       snake_case_ =   scaling
       snake_case_ =   num_dynamic_real_features
       snake_case_ =   num_static_real_features
       snake_case_ =   num_static_categorical_features

       # set cardinality
       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`'''      )
             snake_case_ =   cardinality
       else:
             snake_case_ =   [0]

       # set embedding_dimension
       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`'''      )
             snake_case_ =   embedding_dimension
       else:
             snake_case_ =   [min(50	,   (cat + 1) // 2      ) for cat in self.cardinality]

       snake_case_ =   num_parallel_samples

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

       snake_case_ =   dropout
       snake_case_ =   attention_dropout
       snake_case_ =   activation_dropout
       snake_case_ =   encoder_layerdrop
       snake_case_ =   decoder_layerdrop

       snake_case_ =   activation_function
       snake_case_ =   init_std

       snake_case_ =   use_cache

       # Informer
       snake_case_ =   attention_type
       snake_case_ =   sampling_factor
       snake_case_ =   distil

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





 @property
 def 		UpperCamelCase__  (			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
       )