






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 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     List[str]	=   "layoutlm"


 def __init__(		self							,					_A=30522							,					_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-12							,					_A=0							,					_A="absolute"							,					_A=True							,					_A=1024							,					**_A							,					)	->						Dict:

     """simple docstring"""



     super().__init__(pad_token_id=_A							,					**_A)
     _UpperCAmelCase			: Optional[int]     					=			vocab_size
     _UpperCAmelCase			: List[str]     					=			hidden_size
     _UpperCAmelCase			: Union[str, Any]     					=			num_hidden_layers
     _UpperCAmelCase			: List[str]     					=			num_attention_heads
     _UpperCAmelCase			: Union[str, Any]     					=			hidden_act
     _UpperCAmelCase			: str     					=			intermediate_size
     _UpperCAmelCase			: Union[str, Any]     					=			hidden_dropout_prob
     _UpperCAmelCase			: List[str]     					=			attention_probs_dropout_prob
     _UpperCAmelCase			: List[str]     					=			max_position_embeddings
     _UpperCAmelCase			: List[Any]     					=			type_vocab_size
     _UpperCAmelCase			: Union[str, Any]     					=			initializer_range
     _UpperCAmelCase			: int     					=			layer_norm_eps
     _UpperCAmelCase			: int     					=			position_embedding_type
     _UpperCAmelCase			: Union[str, Any]     					=			use_cache
     _UpperCAmelCase			: List[Any]     					=			max_ad_position_embeddings



class 							A_      (  __lowercase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A = "default"							,					_A = None							,					)	->						Dict:

     """simple docstring"""



     super().__init__(_A							,					task=_A							,					patching_specs=_A)
     _UpperCAmelCase			: Dict     					=			config.max_ad_position_embeddings - 1


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

     """simple docstring"""



     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 	snake_case__			(		self							,					_A							,					_A = -1							,					_A = -1							,					_A = False							,					_A = None							,					)	->						Mapping[str, Any]:

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			super().generate_dummy_inputs(
         _A							,					batch_size=_A							,					seq_length=_A							,					is_pair=_A							,					framework=_A)

     # Generate a dummy bbox
     _UpperCAmelCase			: Any     					=			[48, 84, 73, 128]

     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			: List[Any]     					=			input_dict['''input_ids'''].shape
     _UpperCAmelCase			: str     					=			torch.tensor([*[box] * seq_length]).tile(_A							,					1							,					1)
     return input_dict












from unittest.mock import patch

import datasets
from datasets import Dataset

def        _lowerCamelCase		(  ) ->   Tuple:
    _UpperCAmelCase			: Union[str, Any]     					=			Dataset.from_dict({'''col_1''': [3, 2, 0, 1]}	)

    with patch('''tqdm.auto.tqdm'''	) as mock_tqdm:
        datasets.disable_progress_bar()
        dset.map(lambda __A	: {"col_2": x["col_1"] + 1}	)
        mock_tqdm.assert_not_called()

        mock_tqdm.reset_mock()

        datasets.enable_progress_bar()
        dset.map(lambda __A	: {"col_2": x["col_1"] + 1}	)
        mock_tqdm.assert_called()




