

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


snake_case_			  = logging.get_logger(__name__)

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


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Union[str, Any]														=     """layoutlmv3"""





    def __init__(      self						,   a=5_0265						,   a=768						,   a=12						,   a=12						,   a=3072						,   a="gelu"						,   a=0.1						,   a=0.1						,   a=512						,   a=2						,   a=0.02						,   a=1e-5						,   a=1						,   a=0						,   a=2						,   a=1024						,   a=128						,   a=128						,   a=True						,   a=32						,   a=128						,   a=64						,   a=256						,   a=True						,   a=True						,   a=True						,   a=224						,   a=3						,   a=16						,   a=None						,   **a						,   ):
         super().__init__(
             vocab_size=a						,   hidden_size=a						,   num_hidden_layers=a						,   num_attention_heads=a						,   intermediate_size=a						,   hidden_act=a						,   hidden_dropout_prob=a						,   attention_probs_dropout_prob=a						,   max_position_embeddings=a						,   type_vocab_size=a						,   initializer_range=a						,   layer_norm_eps=a						,   pad_token_id=a						,   bos_token_id=a						,   eos_token_id=a						,   **a						,   )
         lowercase__    :   Tuple      =       max_ad_position_embeddings
         lowercase__    :   Optional[int]      =       coordinate_size
         lowercase__    :   Optional[int]      =       shape_size
         lowercase__    :   Dict      =       has_relative_attention_bias
         lowercase__    :   str      =       rel_pos_bins
         lowercase__    :   List[str]      =       max_rel_pos
         lowercase__    :   List[str]      =       has_spatial_attention_bias
         lowercase__    :   str      =       rel_ad_pos_bins
         lowercase__    :   Union[str, Any]      =       max_rel_ad_pos
         lowercase__    :   Optional[Any]      =       text_embed
         lowercase__    :   List[Any]      =       visual_embed
         lowercase__    :   Any      =       input_size
         lowercase__    :   List[str]      =       num_channels
         lowercase__    :   Union[str, Any]      =       patch_size
         lowercase__    :   Union[str, Any]      =       classifier_dropout



class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Optional[Any]														=     version.parse("""1.12"""     )





    @property
    def       snake_case_				(      self):
         # 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       snake_case_				(      self):
         return 1e-5





    @property
    def       snake_case_				(      self):
         return 12





    def       snake_case_				(      self						,   a						,   a = -1						,   a = -1						,   a = False						,   a = None						,   a = 3						,   a = 40						,   a = 40						,   ):
         setattr(processor.image_processor						,   'apply_ocr'						,   a)

         # 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(
             a						,   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__    :   Dict      =       processor.tokenizer.num_special_tokens_to_add(a)
         lowercase__    :   Any      =       compute_effective_axis_dimension(
             a						,   fixed_dimension=OnnxConfig.default_fixed_sequence						,   num_token_to_add=a)
         # Generate dummy inputs according to compute batch and sequence
         lowercase__    :   str      =       [[' '.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         lowercase__    :   Optional[int]      =       [[[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__    :   List[Any]      =       self._generate_dummy_images(a						,   a						,   a						,   a)

         lowercase__    :   Tuple      =       dict(
             processor(
                 a						,   text=a						,   boxes=a						,   return_tensors=a						,   ))

         return inputs




from __future__ import annotations

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    dict	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__      , lowercase__    :   List[str]      =       set(SCREAMING_SNAKE_CASE_			), [start]

     while stack:
          lowercase__    :   Union[str, Any]      =       stack.pop()
          explored.add(SCREAMING_SNAKE_CASE_			)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]			):
               if adj not in explored:
                    stack.append(SCREAMING_SNAKE_CASE_			)
     return explored


snake_case_			  = {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, '''A'''))

