

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


__snake_case							:			Any      		= logging.get_logger(__name__)

__snake_case							:			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 					A__(a_       ):
						"""simple docstring"""






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





						def __init__(						self     ,			_lowercase=30_522     ,			_lowercase=768     ,			_lowercase=12     ,			_lowercase=12     ,			_lowercase=3_072     ,			_lowercase="gelu"     ,			_lowercase=0.1     ,			_lowercase=0.1     ,			_lowercase=512     ,			_lowercase=2     ,			_lowercase=0.0_2     ,			_lowercase=1e-12     ,			_lowercase=0     ,			_lowercase="absolute"     ,			_lowercase=True     ,			_lowercase=1_024     ,			**_lowercase     ,			)    ->   str:
									super().__init__(pad_token_id=_lowercase     ,			**_lowercase						)
									a_	:	Optional[Any]          =   vocab_size
									a_	:	List[str]          =   hidden_size
									a_	:	Union[str, Any]          =   num_hidden_layers
									a_	:	int          =   num_attention_heads
									a_	:	str          =   hidden_act
									a_	:	List[Any]          =   intermediate_size
									a_	:	str          =   hidden_dropout_prob
									a_	:	str          =   attention_probs_dropout_prob
									a_	:	Dict          =   max_position_embeddings
									a_	:	Tuple          =   type_vocab_size
									a_	:	Any          =   initializer_range
									a_	:	Optional[Any]          =   layer_norm_eps
									a_	:	Union[str, Any]          =   position_embedding_type
									a_	:	Tuple          =   use_cache
									a_	:	Optional[int]          =   max_ad_position_embeddings







class 					A__(a_       ):
						"""simple docstring"""






						def __init__(						self     ,			_lowercase     ,			_lowercase = "default"     ,			_lowercase = None     ,			)    ->   Optional[Any]:
									super().__init__(_lowercase     ,			task=_lowercase     ,			patching_specs=_lowercase						)
									a_	:	Dict          =   config.max_ad_position_embeddings - 1





						@property
						def  UpperCamelCase__				(						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  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase = -1     ,			_lowercase = -1     ,			_lowercase = False     ,			_lowercase = None     ,			)    ->   Mapping[str, Any]:
									a_	:	Optional[int]          =   super().generate_dummy_inputs(
									    _lowercase     ,			batch_size=_lowercase     ,			seq_length=_lowercase     ,			is_pair=_lowercase     ,			framework=_lowercase						)

									# Generate a dummy bbox
									a_	:	List[str]          =   [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_       ,				a_	:	int          =   input_dict["""input_ids"""].shape
									a_	:	Optional[int]          =   torch.tensor([*[box] * seq_length]						).tile(_lowercase     ,			1     ,			1						)
									return input_dict





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 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			return EnvironmentCommand()



class 					A__(a_       ):
						"""simple docstring"""






						@staticmethod
						def  UpperCamelCase__				(						_lowercase						)    ->   Optional[Any]:
									a_	:	List[str]          =   parser.add_parser("""env"""     ,			help="""Print relevant system environment info."""						)
									download_parser.set_defaults(func=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   int:
									a_	:	List[str]          =   {
									    """`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(_lowercase						)						)

									return info





						@staticmethod
						def  UpperCamelCase__				(						_lowercase						)    ->   Dict:
									return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"


