



"""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_	:						Union[str, Any]				=			logging.get_logger(__name__)

A_	:						int				=			{
    '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__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		List[str]     	=   '''layoutlm'''





	def __init__(			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Tuple=30_522    ,					_SCREAMING_SNAKE_CASE							:					Dict=768    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]=12    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=12    ,					_SCREAMING_SNAKE_CASE							:					Dict=3_072    ,					_SCREAMING_SNAKE_CASE							:					List[Any]="gelu"    ,					_SCREAMING_SNAKE_CASE							:					Tuple=0.1    ,					_SCREAMING_SNAKE_CASE							:					Any=0.1    ,					_SCREAMING_SNAKE_CASE							:					Tuple=512    ,					_SCREAMING_SNAKE_CASE							:					str=2    ,					_SCREAMING_SNAKE_CASE							:					List[str]=0.0_2    ,					_SCREAMING_SNAKE_CASE							:					List[str]=1E-1_2    ,					_SCREAMING_SNAKE_CASE							:					int=0    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]="absolute"    ,					_SCREAMING_SNAKE_CASE							:					Tuple=True    ,					_SCREAMING_SNAKE_CASE							:					int=1_024    ,					**_SCREAMING_SNAKE_CASE							:					Any    ,					)	->  Dict:





					"""simple docstring"""


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




class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					PretrainedConfig    ,					_SCREAMING_SNAKE_CASE							:					str = "default"    ,					_SCREAMING_SNAKE_CASE							:					List[PatchingSpec] = None    ,					)	->  Optional[Any]:





					"""simple docstring"""


					super().__init__(_SCREAMING_SNAKE_CASE    ,					task=_SCREAMING_SNAKE_CASE    ,					patching_specs=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    config.max_ad_position_embeddings - 1





	@property
	def  _lowerCAmelCase    (			self							:					Dict						)	->  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  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					PreTrainedTokenizer    ,					_SCREAMING_SNAKE_CASE							:					int = -1    ,					_SCREAMING_SNAKE_CASE							:					int = -1    ,					_SCREAMING_SNAKE_CASE							:					bool = False    ,					_SCREAMING_SNAKE_CASE							:					Optional[TensorType] = None    ,					)	->  Mapping[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    super().generate_dummy_inputs(
					    _SCREAMING_SNAKE_CASE    ,					batch_size=_SCREAMING_SNAKE_CASE    ,					seq_length=_SCREAMING_SNAKE_CASE    ,					is_pair=_SCREAMING_SNAKE_CASE    ,					framework=_SCREAMING_SNAKE_CASE						)

					# Generate a dummy bbox
					SCREAMING_SNAKE_CASE	:       Dict 							=    [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

					SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       int 							=    input_dict['input_ids'].shape
					SCREAMING_SNAKE_CASE	:       List[str] 							=    torch.tensor([*[box] * seq_length]						).tile(_SCREAMING_SNAKE_CASE    ,					1    ,					1						)
					return input_dict










"""simple docstring"""

import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def    __snake_case       (  __A			:	Union[str, Any]    )    ->					Tuple:






				'''simple docstring'''
				return EnvironmentCommand()







class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					ArgumentParser						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    parser.add_parser('env'    ,					help='Print relevant system environment info.'						)
					download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    {
					    '`datasets` version': version,
					    'Platform': platform.platform(),
					    'Python version': platform.python_version(),
					    'Huggingface_hub version': huggingface_hub.__version__,
					    'PyArrow version': pyarrow.__version__,
					    'Pandas version': pandas.__version__,
					}

					print('\nCopy-and-paste the text below in your GitHub issue.\n'						)
					print(self.format_dict(_SCREAMING_SNAKE_CASE						)						)

					return info







	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  List[Any]:





					"""simple docstring"""


					return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"





