'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType


UpperCamelCase__     :	str				=			logging.get_logger(__name__)

UpperCamelCase__     :	Dict				=			{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[Any]      							=   '''layoutlmv3'''




      def __init__(				self					,lowerCamelCase_=50265					,lowerCamelCase_=768					,lowerCamelCase_=12					,lowerCamelCase_=12					,lowerCamelCase_=3072					,lowerCamelCase_="gelu"					,lowerCamelCase_=0.1					,lowerCamelCase_=0.1					,lowerCamelCase_=512					,lowerCamelCase_=2					,lowerCamelCase_=0.02					,lowerCamelCase_=1e-5					,lowerCamelCase_=1					,lowerCamelCase_=0					,lowerCamelCase_=2					,lowerCamelCase_=1024					,lowerCamelCase_=128					,lowerCamelCase_=128					,lowerCamelCase_=True					,lowerCamelCase_=32					,lowerCamelCase_=128					,lowerCamelCase_=64					,lowerCamelCase_=256					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_=224					,lowerCamelCase_=3					,lowerCamelCase_=16					,lowerCamelCase_=None					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         super().__init__(
             vocab_size=lowerCamelCase_					,hidden_size=lowerCamelCase_					,num_hidden_layers=lowerCamelCase_					,num_attention_heads=lowerCamelCase_					,intermediate_size=lowerCamelCase_					,hidden_act=lowerCamelCase_					,hidden_dropout_prob=lowerCamelCase_					,attention_probs_dropout_prob=lowerCamelCase_					,max_position_embeddings=lowerCamelCase_					,type_vocab_size=lowerCamelCase_					,initializer_range=lowerCamelCase_					,layer_norm_eps=lowerCamelCase_					,pad_token_id=lowerCamelCase_					,bos_token_id=lowerCamelCase_					,eos_token_id=lowerCamelCase_					,**lowerCamelCase_					,)
         UpperCAmelCase__							:				str   				=  max_ad_position_embeddings
         UpperCAmelCase__							:				Optional[Any]   				=  coordinate_size
         UpperCAmelCase__							:				Union[str, Any]   				=  shape_size
         UpperCAmelCase__							:				List[str]   				=  has_relative_attention_bias
         UpperCAmelCase__							:				Any   				=  rel_pos_bins
         UpperCAmelCase__							:				Tuple   				=  max_rel_pos
         UpperCAmelCase__							:				Tuple   				=  has_spatial_attention_bias
         UpperCAmelCase__							:				str   				=  rel_ad_pos_bins
         UpperCAmelCase__							:				Optional[int]   				=  max_rel_ad_pos
         UpperCAmelCase__							:				Any   				=  text_embed
         UpperCAmelCase__							:				Optional[Any]   				=  visual_embed
         UpperCAmelCase__							:				Union[str, Any]   				=  input_size
         UpperCAmelCase__							:				Any   				=  num_channels
         UpperCAmelCase__							:				Union[str, Any]   				=  patch_size
         UpperCAmelCase__							:				int   				=  classifier_dropout




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Union[str, Any]      							=   version.parse('''1.12'''   )




      @property
      def 		lowerCAmelCase__					(				self							)			->			Mapping[str, Mapping[int, str]]:



         '''simple docstring'''



         if self.task in ["question-answering", "sequence-classification"]:
            return OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                    ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                    ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ]							)
         else:
            return OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                    ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                    ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
                ]							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			float:



         '''simple docstring'''



         return 1e-5




      @property
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         return 12




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = -1					,lowerCamelCase_ = -1					,lowerCamelCase_ = False					,lowerCamelCase_ = None					,lowerCamelCase_ = 3					,lowerCamelCase_ = 40					,lowerCamelCase_ = 40					,)			->			Mapping[str, Any]:



         '''simple docstring'''



         setattr(processor.image_processor					,'''apply_ocr'''					,lowerCamelCase_							)

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCAmelCase__							:				Optional[Any]   				=  compute_effective_axis_dimension(
             lowerCamelCase_					,fixed_dimension=OnnxConfig.default_fixed_batch					,num_token_to_add=0							)
         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         UpperCAmelCase__							:				Optional[int]   				=  processor.tokenizer.num_special_tokens_to_add(lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  compute_effective_axis_dimension(
             lowerCamelCase_					,fixed_dimension=OnnxConfig.default_fixed_sequence					,num_token_to_add=lowerCamelCase_							)
         # Generate dummy inputs according to compute batch and sequence
         UpperCAmelCase__							:				Tuple   				=  [[''' '''.join([processor.tokenizer.unk_token]							) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         UpperCAmelCase__							:				Union[str, Any]   				=  [[[48, 84, 73, 128]]] * batch_size

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
         UpperCAmelCase__							:				int   				=  self._generate_dummy_images(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				int   				=  dict(
             processor(
                 lowerCamelCase_					,text=lowerCamelCase_					,boxes=lowerCamelCase_					,return_tensors=lowerCamelCase_					,)							)

         return inputs





'''simple docstring'''

from typing import List, Optional, Union

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


UpperCamelCase__     :	List[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	Optional[Any]				=			{
    '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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   '''time_series_transformer'''
      UpperCAmelCase_      :      List[Any]      							=   {
          '''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_ = 32					,lowerCamelCase_ = 32					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = 2					,lowerCamelCase_ = True					,lowerCamelCase_ = "gelu"					,lowerCamelCase_ = 64					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 0.1					,lowerCamelCase_ = 100					,lowerCamelCase_ = 0.02					,lowerCamelCase_=True					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  prediction_length
         UpperCAmelCase__							:				Optional[Any]   				=  context_length or prediction_length
         UpperCAmelCase__							:				Union[str, Any]   				=  distribution_output
         UpperCAmelCase__							:				List[Any]   				=  loss
         UpperCAmelCase__							:				Optional[int]   				=  input_size
         UpperCAmelCase__							:				Tuple   				=  num_time_features
         UpperCAmelCase__							:				str   				=  lags_sequence
         UpperCAmelCase__							:				Union[str, Any]   				=  scaling
         UpperCAmelCase__							:				Optional[int]   				=  num_dynamic_real_features
         UpperCAmelCase__							:				Optional[Any]   				=  num_static_real_features
         UpperCAmelCase__							:				str   				=  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`'''							)
            UpperCAmelCase__							:				Union[str, Any]   				=  cardinality
         else:
            UpperCAmelCase__							:				List[str]   				=  [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`'''							)
            UpperCAmelCase__							:				int   				=  embedding_dimension
         else:
            UpperCAmelCase__							:				Union[str, Any]   				=  [min(50					,(cat + 1) // 2							) for cat in self.cardinality]
         UpperCAmelCase__							:				Dict   				=  num_parallel_samples

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

         UpperCAmelCase__							:				Optional[Any]   				=  dropout
         UpperCAmelCase__							:				Any   				=  attention_dropout
         UpperCAmelCase__							:				Optional[int]   				=  activation_dropout
         UpperCAmelCase__							:				List[Any]   				=  encoder_layerdrop
         UpperCAmelCase__							:				Optional[int]   				=  decoder_layerdrop

         UpperCAmelCase__							:				List[Any]   				=  activation_function
         UpperCAmelCase__							:				List[Any]   				=  init_std

         UpperCAmelCase__							:				Optional[Any]   				=  use_cache

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




      @property
      def 		lowerCAmelCase__					(				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
         )




