'''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


__SCREAMING_SNAKE_CASE :  Optional[int]  =						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						{
    """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_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						Dict      			=		"layoutlm"




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

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      def __init__( self		:       Tuple		,      A		:       PretrainedConfig		,      A		:       str = "default"		,      A		:       List[PatchingSpec] = None		,      ):
             super().__init__(A		,      task=A		,      patching_specs=A )
             _UpperCAmelCase  :						Optional[Any]							   =					config.max_ad_position_embeddings - 1




      @property
      def   _A			( self		:       Optional[Any] ):
             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		:       Dict		,      A		:       PreTrainedTokenizer		,      A		:       int = -1		,      A		:       int = -1		,      A		:       bool = False		,      A		:       Optional[TensorType] = None		,      ):
             _UpperCAmelCase  :						List[Any]							   =					super().generate_dummy_inputs(
                 A		,      batch_size=A		,      seq_length=A		,      is_pair=A		,      framework=A )

             # Generate a dummy bbox
             _UpperCAmelCase  :						int							   =					[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  :						List[str]							   =					input_dict["input_ids"].shape
             _UpperCAmelCase  :						List[Any]							   =					torch.tensor([*[box] * seq_length] ).tile(A		,      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 			UpperCamelCase_							(      _UpperCAmelCase						:   List[str]					)					->					Union[str, Any]:


       """simple docstring"""







       return EnvironmentCommand()


class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      @staticmethod
      def   _A			( A		:       ArgumentParser ):
             _UpperCAmelCase  :						List[str]							   =					parser.add_parser("env"		,      help="Print relevant system environment info." )
             download_parser.set_defaults(func=A )




      def   _A			( self		:       List[str] ):
             _UpperCAmelCase  :						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(A ) )

             return info





      @staticmethod
      def   _A			( A		:       Tuple ):
             return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"

