
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__   :		int 		=logging.get_logger(__name__)

A__   :		Optional[int] 		={
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''layoutlmv3'''
 def __init__(   self			:    Union[str, Any]				,   lowerCamelCase			:    Tuple=5_02_65				,   lowerCamelCase			:    Union[str, Any]=7_68				,   lowerCamelCase			:    List[Any]=12				,   lowerCamelCase			:    Union[str, Any]=12				,   lowerCamelCase			:    Optional[Any]=30_72				,   lowerCamelCase			:    Optional[Any]="gelu"				,   lowerCamelCase			:    List[Any]=0.1				,   lowerCamelCase			:    Any=0.1				,   lowerCamelCase			:    Any=5_12				,   lowerCamelCase			:    int=2				,   lowerCamelCase			:    Dict=0.02				,   lowerCamelCase			:    List[str]=1e-5				,   lowerCamelCase			:    Any=1				,   lowerCamelCase			:    Tuple=0				,   lowerCamelCase			:    str=2				,   lowerCamelCase			:    List[str]=10_24				,   lowerCamelCase			:    str=1_28				,   lowerCamelCase			:    List[Any]=1_28				,   lowerCamelCase			:    str=True				,   lowerCamelCase			:    Union[str, Any]=32				,   lowerCamelCase			:    List[Any]=1_28				,   lowerCamelCase			:    str=64				,   lowerCamelCase			:    Optional[Any]=2_56				,   lowerCamelCase			:    Tuple=True				,   lowerCamelCase			:    Optional[Any]=True				,   lowerCamelCase			:    List[Any]=True				,   lowerCamelCase			:    Union[str, Any]=2_24				,   lowerCamelCase			:    Tuple=3				,   lowerCamelCase			:    Optional[Any]=16				,   lowerCamelCase			:    Union[str, Any]=None				,   **lowerCamelCase			:    Any				,   ):



   """simple docstring"""





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







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =version.parse('''1.12'''			)
 @property
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """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 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   return 1e-5
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   return 12






 def 		lowercase_(   self			:    str				,   lowerCamelCase			:    "ProcessorMixin"				,   lowerCamelCase			:    int = -1				,   lowerCamelCase			:    int = -1				,   lowerCamelCase			:    bool = False				,   lowerCamelCase			:    Optional["TensorType"] = None				,   lowerCamelCase			:    int = 3				,   lowerCamelCase			:    int = 40				,   lowerCamelCase			:    int = 40				,   ):



   """simple docstring"""





   setattr(processor.image_processor				,   """apply_ocr"""				,   UpperCAmelCase__       )

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

   # Generate dummy bounding boxes
   __A		:     Tuple												=		[[[48, 84, 73, 1_28]]] * 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)
   __A		:     int												=		self._generate_dummy_images(UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__       )

   __A		:     Any												=		dict(
       processor(
           UpperCAmelCase__				,   text=UpperCAmelCase__				,   boxes=UpperCAmelCase__				,   return_tensors=UpperCAmelCase__				,   )       )

   return inputs





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[Any]:





  """simple docstring"""

  if not head:
    return True
  # split the list to two parts
  __A							,					__A		:     Any												=		head.next, head
  while fast and fast.next:
    __A		:     List[str]												=		fast.next.next
    __A		:     List[Any]												=		slow.next
  __A		:     int												=		slow.next
  __A		:     List[str]												=		None  # Don't forget here! But forget still works!
  # reverse the second part
  __A		:     Dict												=		None
  while second:
    __A		:     Dict												=		second.next
    __A		:     str												=		node
    __A		:     int												=		second
    __A		:     Optional[int]												=		nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    __A		:     List[Any]												=		node.next
    __A		:     Tuple												=		head.next
  return True







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Optional[int]:





  """simple docstring"""

  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  __A		:     int												=		head
  while fast and fast.next:
    __A							,					__A		:     Optional[int]												=		fast.next.next, slow.next

  # 2. Push the second half into the stack
  __A		:     Union[str, Any]												=		[slow.val]
  while slow.next:
    __A		:     List[str]												=		slow.next
    stack.append(slow.val )

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    __A		:     Tuple												=		cur.next

  return True







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Any:





  """simple docstring"""

  if not head or not head.next:
    return True
  __A		:     List[Any]												=		{}
  __A		:     Optional[int]												=		0
  while head:
    if head.val in d:
      d[head.val].append(__SCREAMING_SNAKE_CASE )
    else:
      __A		:     Tuple												=		[pos]
    __A		:     List[Any]												=		head.next
    pos += 1
  __A		:     List[Any]												=		pos - 1
  __A		:     List[str]												=		0
  for v in d.values():
    if len(__SCREAMING_SNAKE_CASE ) % 2 != 0:
      middle += 1
    else:
      __A		:     List[Any]												=		0
      for i in range(0			,     len(__SCREAMING_SNAKE_CASE ) ):
        if v[i] + v[len(__SCREAMING_SNAKE_CASE ) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True



