'''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										= logging.get_logger(__name__)

_A										= {
    '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__	(							_snake_case      ):






   """simple docstring"""
   A				:							Any			     =     '''layoutlm'''
   def __init__(self							,					_a=30_522							,					_a=768							,					_a=12							,					_a=12							,					_a=3_072							,					_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=1_024							,					**_a							,					)     ->  Dict:
        super().__init__(pad_token_id=_a							,					**_a					)
        lowercase_       :							Optional[Any]  					=       vocab_size
        lowercase_       :							List[Any]  					=       hidden_size
        lowercase_       :							Optional[int]  					=       num_hidden_layers
        lowercase_       :							List[Any]  					=       num_attention_heads
        lowercase_       :							List[Any]  					=       hidden_act
        lowercase_       :							Any  					=       intermediate_size
        lowercase_       :							Optional[int]  					=       hidden_dropout_prob
        lowercase_       :							Optional[Any]  					=       attention_probs_dropout_prob
        lowercase_       :							Dict  					=       max_position_embeddings
        lowercase_       :							str  					=       type_vocab_size
        lowercase_       :							List[str]  					=       initializer_range
        lowercase_       :							Any  					=       layer_norm_eps
        lowercase_       :							List[Any]  					=       position_embedding_type
        lowercase_       :							Optional[int]  					=       use_cache
        lowercase_       :							Optional[int]  					=       max_ad_position_embeddings



class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   def __init__(self							,					_a							,					_a = "default"							,					_a = None							,					)     ->  Any:
        super().__init__(_a							,					task=_a							,					patching_specs=_a					)
        lowercase_       :							Optional[int]  					=       config.max_ad_position_embeddings - 1
   @property
   def 		_lowerCamelCase			(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 		_lowerCamelCase			(self							,					_a							,					_a = -1							,					_a = -1							,					_a = False							,					_a = None							,					)     ->  Mapping[str, Any]:
        lowercase_       :							Dict  					=       super().generate_dummy_inputs(
            _a							,					batch_size=_a							,					seq_length=_a							,					is_pair=_a							,					framework=_a					)

        # Generate a dummy bbox
        lowercase_       :							Optional[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

        lowercase_					,lowercase_       :							Dict  					=       input_dict['input_ids'].shape
        lowercase_       :							List[Any]  					=       torch.tensor([*[box] * seq_length]					).tile(_a							,					1							,					1					)
        return input_dict





'''simple docstring'''




_A										= '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_A										= [{'type': 'code', 'content': INSTALL_CONTENT}]
_A										= {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




