


'''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


A   :  Dict       		=				logging.get_logger(__name__)

A   :  List[str]       		=				{
    """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 				__lowerCamelCase				( UpperCamelCase__ ):
    """simple docstring"""
    a									=					"""layoutlm"""


    def __init__(						self    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=30522   ,							SCREAMING_SNAKE_CASE    :							Dict=768   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=12   ,							SCREAMING_SNAKE_CASE    :							Dict=12   ,							SCREAMING_SNAKE_CASE    :							Tuple=3072   ,							SCREAMING_SNAKE_CASE    :							Dict="gelu"   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=0.1   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=0.1   ,							SCREAMING_SNAKE_CASE    :							int=512   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=2   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=0.02   ,							SCREAMING_SNAKE_CASE    :							Any=1e-12   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=0   ,							SCREAMING_SNAKE_CASE    :							Any="absolute"   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=True   ,							SCREAMING_SNAKE_CASE    :							Any=1024   ,							**SCREAMING_SNAKE_CASE    :							Dict   ,							):
           super().__init__(pad_token_id=lowerCAmelCase__   ,							**lowerCAmelCase__)
           _A  :			Tuple											=      vocab_size
           _A  :			Union[str, Any]											=      hidden_size
           _A  :			int											=      num_hidden_layers
           _A  :			Optional[int]											=      num_attention_heads
           _A  :			Tuple											=      hidden_act
           _A  :			Optional[Any]											=      intermediate_size
           _A  :			Tuple											=      hidden_dropout_prob
           _A  :			List[Any]											=      attention_probs_dropout_prob
           _A  :			Optional[Any]											=      max_position_embeddings
           _A  :			List[str]											=      type_vocab_size
           _A  :			Dict											=      initializer_range
           _A  :			List[Any]											=      layer_norm_eps
           _A  :			Any											=      position_embedding_type
           _A  :			Any											=      use_cache
           _A  :			int											=      max_ad_position_embeddings




class 				__lowerCamelCase				( UpperCamelCase__ ):
    """simple docstring"""


    def __init__(						self    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = "default"   ,							SCREAMING_SNAKE_CASE    :							Dict = None   ,							):
           super().__init__(lowerCAmelCase__   ,							task=lowerCAmelCase__   ,							patching_specs=lowerCAmelCase__)
           _A  :			Union[str, Any]											=      config.max_ad_position_embeddings - 1


    @property
    def      A					(						self    :							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    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any] = -1   ,							SCREAMING_SNAKE_CASE    :							Dict = -1   ,							SCREAMING_SNAKE_CASE    :							str = False   ,							SCREAMING_SNAKE_CASE    :							Tuple = None   ,							):
           _A  :			Optional[int]											=      super().generate_dummy_inputs(
               lowerCAmelCase__   ,							batch_size=lowerCAmelCase__   ,							seq_length=lowerCAmelCase__   ,							is_pair=lowerCAmelCase__   ,							framework=lowerCAmelCase__)

           # Generate a dummy bbox
           _A  :			Tuple											=      [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

           _A  :			int											=      input_dict["input_ids"].shape
           _A  :			Any											=      torch.tensor([*[box] * seq_length]).tile(lowerCAmelCase__   ,							1   ,							1)
           return input_dict










'''simple docstring'''







from unittest.mock import patch

import datasets
from datasets import Dataset






def    lowerCAmelCase__ (       ):
       _A  :			Optional[int]											=      Dataset.from_dict({'col_1': [3, 2, 0, 1]}     )

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

              mock_tqdm.reset_mock()

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






