
"""simple docstring"""

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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_         =  {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}
class        _UpperCAmelCase							(						_UpperCAmelCase							):
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		'''layoutlm'''




      def __init__(							self				,    lowercase_=3_0_5_2_2				,    lowercase_=7_6_8				,    lowercase_=1_2				,    lowercase_=1_2				,    lowercase_=3_0_7_2				,    lowercase_="gelu"				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=5_1_2				,    lowercase_=2				,    lowercase_=0.0_2				,    lowercase_=1E-12				,    lowercase_=0				,    lowercase_="absolute"				,    lowercase_=True				,    lowercase_=1_0_2_4				,    **lowercase_				,    )  ->   Tuple:
           super().__init__(pad_token_id=lowerCAmelCase__				,    **lowerCAmelCase__							)
           UpperCAmelCase       =   vocab_size
           UpperCAmelCase       =   hidden_size
           UpperCAmelCase       =   num_hidden_layers
           UpperCAmelCase       =   num_attention_heads
           UpperCAmelCase       =   hidden_act
           UpperCAmelCase       =   intermediate_size
           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       =   position_embedding_type
           UpperCAmelCase       =   use_cache
           UpperCAmelCase       =   max_ad_position_embeddings

class        _UpperCAmelCase							(						_UpperCAmelCase							):




      def __init__(							self				,    lowercase_				,    lowercase_ = "default"				,    lowercase_ = None				,    )  ->   str:
           super().__init__(lowerCAmelCase__				,    task=lowerCAmelCase__				,    patching_specs=lowerCAmelCase__							)
           UpperCAmelCase       =   config.max_ad_position_embeddings - 1




      @property
      def       a_ (							self							)  ->   Mapping[str, Mapping[int, str]]:
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('bbox', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
                   ('token_type_ids', {0: 'batch', 1: 'sequence'}),
               ]							)




      def       a_ (							self				,    lowercase_				,    lowercase_ = -1				,    lowercase_ = -1				,    lowercase_ = False				,    lowercase_ = None				,    )  ->   Mapping[str, Any]:
           UpperCAmelCase       =   super().generate_dummy_inputs(
               lowerCAmelCase__				,    batch_size=lowerCAmelCase__				,    seq_length=lowerCAmelCase__				,    is_pair=lowerCAmelCase__				,    framework=lowerCAmelCase__							)

           # Generate a dummy bbox
           UpperCAmelCase       =   [4_8, 8_4, 7_3, 1_2_8]

           if not framework == TensorType.PYTORCH:
                raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'							)

           if not is_torch_available():
                raise ValueError('Cannot generate dummy inputs without PyTorch installed.'							)
           import torch

           UpperCAmelCase     ,							UpperCAmelCase       =   input_dict['input_ids'].shape
           UpperCAmelCase       =   torch.tensor([*[box] * seq_length]							).tile(lowerCAmelCase__				,    1				,    1							)
           return input_dict





"""simple docstring"""

SCREAMING_SNAKE_CASE_         =  {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

SCREAMING_SNAKE_CASE_         =  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

SCREAMING_SNAKE_CASE_         =  [0 for x in range(len(graph))]
SCREAMING_SNAKE_CASE_         =  []




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Any	)	-> List[str]:






    """simple docstring"""




    UpperCAmelCase       =   1
    while stack:
        UpperCAmelCase       =   stack.pop()
        print(lowerCAmelCase	,			clothes[current_clothing]	)
        order += 1




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   1
    for v in graph[u]:
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    stack.append(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    for v in range(len(lowerCAmelCase	)	):
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)



