'''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


__a:			Union[str, Any]	       =       logging.get_logger(__name__)

__a:			Any	       =       {
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "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=0.0_2     ,       __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     ,       )  ->  Dict:
        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     ,       )
        lowercase__    :   Optional[int]						= max_ad_position_embeddings
        lowercase__    :   str						= coordinate_size
        lowercase__    :   Union[str, Any]						= shape_size
        lowercase__    :   str						= has_relative_attention_bias
        lowercase__    :   str						= rel_pos_bins
        lowercase__    :   Union[str, Any]						= max_rel_pos
        lowercase__    :   Optional[Any]						= has_spatial_attention_bias
        lowercase__    :   Any						= rel_ad_pos_bins
        lowercase__    :   Any						= max_rel_ad_pos
        lowercase__    :   List[str]						= text_embed
        lowercase__    :   Dict						= visual_embed
        lowercase__    :   int						= input_size
        lowercase__    :   int						= num_channels
        lowercase__    :   Union[str, Any]						= patch_size
        lowercase__    :   Tuple						= classifier_dropout



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   version.parse("1.12" )

       @property
       def _lowerCAmelCase(						self			)  ->  Mapping[str, Mapping[int, str]]:
        # The order of inputs is different for question answering and sequence classification
        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:
        return 1E-5

       @property
       def _lowerCAmelCase(						self			)  ->  int:
        return 12


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = -1     ,       __lowerCAmelCase = -1     ,       __lowerCAmelCase = False     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = 3     ,       __lowerCAmelCase = 40     ,       __lowerCAmelCase = 40     ,       )  ->  Mapping[str, Any]:
        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
        lowercase__    :   str						= 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
        lowercase__    :   List[str]						= processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase			)
        lowercase__    :   Optional[int]						= 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
        lowercase__    :   Union[str, Any]						= [[''' '''.join([processor.tokenizer.unk_token]			) * seq_length]] * batch_size

        # Generate dummy bounding boxes
        lowercase__    :   Dict						= [[[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)
        lowercase__    :   Optional[Any]						= self._generate_dummy_images(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__    :   Optional[Any]						= dict(
            processor(
                __lowerCAmelCase     ,       text=__lowerCAmelCase     ,       boxes=__lowerCAmelCase     ,       return_tensors=__lowerCAmelCase     ,       )			)

        return inputs


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 print('''The following activities are selected:'''					)

 # The first activity is always selected
 lowercase__    :   str						= 0
 print(UpperCAmelCase		,					end=''','''					)

 # Consider rest of the activities
 for j in range(UpperCAmelCase					):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(UpperCAmelCase		,					end=''','''					)
   lowercase__    :   str						= j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a:			str	       =       [1, 3, 0, 5, 8, 5]
  __a:			Optional[Any]	       =       [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)

