





from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						List[Any]     		=				["""image_processor""", """tokenizer"""]
   _UpperCamelCase :						List[Any]     		=				"""BlipImageProcessor"""
   _UpperCamelCase :						Optional[Any]     		=				"""AutoTokenizer"""





   def __init__( self					,			snake_case					,			snake_case      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  False
      super().__init__(snake_case					,			snake_case      )
      a__	:						int      		=  self.image_processor





   def __call__( self					,			snake_case = None					,			snake_case = None					,			snake_case = True					,			snake_case = False					,			snake_case = None					,			snake_case = None					,			snake_case = 0					,			snake_case = None					,			snake_case = None					,			snake_case = False					,			snake_case = False					,			snake_case = False					,			snake_case = False					,			snake_case = False					,			snake_case = True					,			snake_case = None					,			**snake_case					,			)				->  BatchEncoding:
      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You have to specify either images or text."      )

      # Get only text
      if images is None:
         a__	:						Optional[int]      		=  self.tokenizer
         a__	:						Optional[int]      		=  self.tokenizer(
             text=snake_case					,			add_special_tokens=snake_case					,			padding=snake_case					,			truncation=snake_case					,			max_length=snake_case					,			stride=snake_case					,			pad_to_multiple_of=snake_case					,			return_attention_mask=snake_case					,			return_overflowing_tokens=snake_case					,			return_special_tokens_mask=snake_case					,			return_offsets_mapping=snake_case					,			return_token_type_ids=snake_case					,			return_length=snake_case					,			verbose=snake_case					,			return_tensors=snake_case					,			**snake_case					,			)
         return text_encoding

      # add pixel_values
      a__	:						Dict      		=  self.image_processor(snake_case					,			return_tensors=snake_case      )

      if text is not None:
         a__	:						Tuple      		=  self.tokenizer(
             text=snake_case					,			add_special_tokens=snake_case					,			padding=snake_case					,			truncation=snake_case					,			max_length=snake_case					,			stride=snake_case					,			pad_to_multiple_of=snake_case					,			return_attention_mask=snake_case					,			return_overflowing_tokens=snake_case					,			return_special_tokens_mask=snake_case					,			return_offsets_mapping=snake_case					,			return_token_type_ids=snake_case					,			return_length=snake_case					,			verbose=snake_case					,			return_tensors=snake_case					,			**snake_case					,			)
      else:
         a__	:						Optional[int]      		=  None

      if text_encoding is not None:
         encoding_image_processor.update(snake_case      )

      return encoding_image_processor





   def 	_snake_case ( self					,			*snake_case					,			**snake_case      )				->  Tuple:
      """simple docstring"""





      return self.tokenizer.batch_decode(*snake_case					,			**snake_case      )





   def 	_snake_case ( self					,			*snake_case					,			**snake_case      )				->  List[Any]:
      """simple docstring"""





      return self.tokenizer.decode(*snake_case					,			**snake_case      )





   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.tokenizer.model_input_names
      a__	:						str      		=  self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )








from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE__       :			Any			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			str			      =						{
    """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			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[Any]     		=				"""informer"""
   _UpperCamelCase :						Any     		=				{
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
       """num_hidden_layers""": """encoder_layers""",
   }





   def __init__( self					,			snake_case = None					,			snake_case = None					,			snake_case = "student_t"					,			snake_case = "nll"					,			snake_case = 1					,			snake_case = None					,			snake_case = "mean"					,			snake_case = 0					,			snake_case = 0					,			snake_case = 0					,			snake_case = 0					,			snake_case = None					,			snake_case = None					,			snake_case = 64					,			snake_case = 32					,			snake_case = 32					,			snake_case = 2					,			snake_case = 2					,			snake_case = 2					,			snake_case = 2					,			snake_case = True					,			snake_case = "gelu"					,			snake_case = 0.05					,			snake_case = 0.1					,			snake_case = 0.1					,			snake_case = 0.1					,			snake_case = 0.1					,			snake_case = 100					,			snake_case = 0.02					,			snake_case=True					,			snake_case = "prob"					,			snake_case = 5					,			snake_case = True					,			**snake_case					,			)				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  prediction_length
      a__	:						Optional[int]      		=  context_length or prediction_length
      a__	:						Optional[int]      		=  distribution_output
      a__	:						str      		=  loss
      a__	:						Optional[Any]      		=  input_size
      a__	:						int      		=  num_time_features
      a__	:						Optional[int]      		=  lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
      a__	:						Optional[int]      		=  scaling
      a__	:						List[str]      		=  num_dynamic_real_features
      a__	:						Optional[int]      		=  num_static_real_features
      a__	:						Optional[int]      		=  num_static_categorical_features

      # set cardinality
      if cardinality and num_static_categorical_features > 0:
         if len(snake_case      ) != num_static_categorical_features:
            raise ValueError(
                "The cardinality should be a list of the same length as `num_static_categorical_features`"      )
         a__	:						List[Any]      		=  cardinality
      else:
         a__	:						Tuple      		=  [0]

      # set embedding_dimension
      if embedding_dimension and num_static_categorical_features > 0:
         if len(snake_case      ) != num_static_categorical_features:
            raise ValueError(
                "The embedding dimension should be a list of the same length as `num_static_categorical_features`"      )
         a__	:						Tuple      		=  embedding_dimension
      else:
         a__	:						int      		=  [min(50					,			(cat + 1) // 2      ) for cat in self.cardinality]

      a__	:						Optional[Any]      		=  num_parallel_samples

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

      a__	:						List[str]      		=  dropout
      a__	:						int      		=  attention_dropout
      a__	:						List[Any]      		=  activation_dropout
      a__	:						Optional[int]      		=  encoder_layerdrop
      a__	:						Tuple      		=  decoder_layerdrop

      a__	:						Any      		=  activation_function
      a__	:						Tuple      		=  init_std

      a__	:						Optional[int]      		=  use_cache

      # Informer
      a__	:						Union[str, Any]      		=  attention_type
      a__	:						List[str]      		=  sampling_factor
      a__	:						Optional[int]      		=  distil

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





   @property
   def 	_snake_case ( self      )				->  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
      )

