
"""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


lowercase		:    Optional[Any]			=       logging.get_logger(__name__)

lowercase		:    Dict			=       {
    "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  _snake_case	(    lowercase__							):
							SCREAMING_SNAKE_CASE		  =    '''layoutlm'''






							def __init__(       self    :      Any    ,			snake_case__    :      List[str]=3_0_5_2_2    ,			snake_case__    :      Union[str, Any]=7_6_8    ,			snake_case__    :      Dict=1_2    ,			snake_case__    :      Any=1_2    ,			snake_case__    :      List[str]=3_0_7_2    ,			snake_case__    :      Tuple="gelu"    ,			snake_case__    :      Optional[int]=0.1    ,			snake_case__    :      Any=0.1    ,			snake_case__    :      List[Any]=5_1_2    ,			snake_case__    :      Any=2    ,			snake_case__    :      str=0.02    ,			snake_case__    :      Union[str, Any]=1e-12    ,			snake_case__    :      Optional[int]=0    ,			snake_case__    :      List[str]="absolute"    ,			snake_case__    :      int=True    ,			snake_case__    :      List[Any]=1_0_2_4    ,			**snake_case__    :      Any    ,			):






												"""simple docstring"""



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






class  _snake_case	(    lowercase__							):






							def __init__(       self    :      List[Any]    ,			snake_case__    :      PretrainedConfig    ,			snake_case__    :      str = "default"    ,			snake_case__    :      List[PatchingSpec] = None    ,			):






												"""simple docstring"""



												super().__init__(__UpperCamelCase    ,			task=__UpperCamelCase    ,			patching_specs=__UpperCamelCase     )
												__a:    Optional[Any]		     =       config.max_ad_position_embeddings - 1






							@property
							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""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 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      PreTrainedTokenizer    ,			snake_case__    :      int = -1    ,			snake_case__    :      int = -1    ,			snake_case__    :      bool = False    ,			snake_case__    :      Optional[TensorType] = None    ,			):






												"""simple docstring"""



												__a:    List[str]		     =       super().generate_dummy_inputs(
												    __UpperCamelCase    ,			batch_size=__UpperCamelCase    ,			seq_length=__UpperCamelCase    ,			is_pair=__UpperCamelCase    ,			framework=__UpperCamelCase     )

												# Generate a dummy bbox
												__a:    Dict		     =       [4_8, 8_4, 7_3, 1_2_8]

												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:    Tuple		     =       torch.tensor([*[box] * seq_length]     ).tile(__UpperCamelCase    ,			1    ,			1     )
												return input_dict





'''simple docstring'''



import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


_lowercase       :						int						=[nn.LayerNorm]

_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						List[Any]						=version.parse(version.parse(torch.__version__).base_version)

_lowercase       :						int						=parsed_torch_version_base >= version.parse("2.0")
_lowercase       :						Tuple						=parsed_torch_version_base >= version.parse("1.12")
_lowercase       :						int						=parsed_torch_version_base >= version.parse("1.11")
_lowercase       :						Dict						=parsed_torch_version_base < version.parse("1.11")


def 							__UpperCAmelCase				(				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:List[str]   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:Any   ,				self				:Optional[int]			)		->						Union[str, Any]:
				from torch import _softmax_backward_data

				if is_torch_less_than_1_11:
								return _softmax_backward_data(UpperCamelCase__   ,				UpperCamelCase__   ,				parent.dim   ,				self			)
				else:
								return _softmax_backward_data(UpperCamelCase__   ,				UpperCamelCase__   ,				parent.dim   ,				self.dtype			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:nn.Linear   ,				UpperCamelCase__				:torch.LongTensor   ,				UpperCamelCase__				:int = 0			)		->						nn.Linear:
				snake_case__      :   Optional[Any]      =    index.to(layer.weight.device			)
				snake_case__      :   int      =    layer.weight.index_select(UpperCamelCase__   ,				UpperCamelCase__			).clone().detach()
				if layer.bias is not None:
								if dim == 1:
												snake_case__      :   Dict      =    layer.bias.clone().detach()
								else:
												snake_case__      :   Optional[Any]      =    layer.bias[index].clone().detach()
				snake_case__      :   Union[str, Any]      =    list(layer.weight.size()			)
				snake_case__      :   int      =    len(UpperCamelCase__			)
				snake_case__      :   int      =    nn.Linear(new_size[1]   ,				new_size[0]   ,				bias=layer.bias is not None			).to(layer.weight.device			)
				snake_case__      :   Union[str, Any]      =    False
				new_layer.weight.copy_(W.contiguous()			)
				snake_case__      :   List[Any]      =    True
				if layer.bias is not None:
								snake_case__      :   Union[str, Any]      =    False
								new_layer.bias.copy_(b.contiguous()			)
								snake_case__      :   Dict      =    True
				return new_layer






class 			_SCREAMING_SNAKE_CASE				(nn.Module      ):



			def __init__(     self		:						int    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Optional[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__()
							snake_case__      :   str      =    nf
							snake_case__      :   Tuple      =    nn.Parameter(torch.empty(__UpperCamelCase    ,			__UpperCamelCase			)			)
							snake_case__      :   Optional[Any]      =    nn.Parameter(torch.zeros(__UpperCamelCase			)			)
							nn.init.normal_(self.weight    ,			std=0.02			)







			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						Optional[int]			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Dict      =    x.size()[:-1] + (self.nf,)
							snake_case__      :   List[str]      =    torch.addmm(self.bias    ,			x.view(-1    ,			x.size(-1			)			)    ,			self.weight			)
							snake_case__      :   Dict      =    x.view(__UpperCamelCase			)
							return x



def 							__UpperCAmelCase				(				UpperCamelCase__				:ConvaD   ,				UpperCamelCase__				:torch.LongTensor   ,				UpperCamelCase__				:int = 1			)		->						ConvaD:
				snake_case__      :   Optional[Any]      =    index.to(layer.weight.device			)
				snake_case__      :   List[Any]      =    layer.weight.index_select(UpperCamelCase__   ,				UpperCamelCase__			).clone().detach()
				if dim == 0:
								snake_case__      :   Any      =    layer.bias.clone().detach()
				else:
								snake_case__      :   Optional[Any]      =    layer.bias[index].clone().detach()
				snake_case__      :   Union[str, Any]      =    list(layer.weight.size()			)
				snake_case__      :   List[str]      =    len(UpperCamelCase__			)
				snake_case__      :   Optional[Any]      =    ConvaD(new_size[1]   ,				new_size[0]			).to(layer.weight.device			)
				snake_case__      :   List[str]      =    False
				new_layer.weight.copy_(W.contiguous()			)
				snake_case__      :   List[Any]      =    True
				snake_case__      :   List[str]      =    False
				new_layer.bias.copy_(b.contiguous()			)
				snake_case__      :   str      =    True
				return new_layer


def 							__UpperCAmelCase				(				UpperCamelCase__				:Union[nn.Linear, ConvaD]   ,				UpperCamelCase__				:torch.LongTensor   ,				UpperCamelCase__				:Optional[int] = None			)		->						Union[nn.Linear, ConvaD]:
				if isinstance(UpperCamelCase__   ,				nn.Linear			):
								return prune_linear_layer(UpperCamelCase__   ,				UpperCamelCase__   ,				dim=0 if dim is None else dim			)
				elif isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								return prune_convad_layer(UpperCamelCase__   ,				UpperCamelCase__   ,				dim=1 if dim is None else dim			)
				else:
								raise ValueError(F'''Can\'t prune layer of class {layer.__class__}'''			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Callable[..., torch.Tensor]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:int   ,				*UpperCamelCase__				:List[str]			)		->						torch.Tensor:
				assert len(UpperCamelCase__			) > 0, F'''{input_tensors} has to be a tuple/list of tensors'''

				# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
				snake_case__      :   str      =    len(inspect.signature(UpperCamelCase__			).parameters			)
				if num_args_in_forward_chunk_fn != len(UpperCamelCase__			):
								raise ValueError(
								    F'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase__			)} input '''
								    '''tensors are given'''			)

				if chunk_size > 0:
								snake_case__      :   Tuple      =    input_tensors[0].shape[chunk_dim]
								for input_tensor in input_tensors:
												if input_tensor.shape[chunk_dim] != tensor_shape:
																raise ValueError(
																    F'''All input tenors have to be of the same shape: {tensor_shape}, '''
																    F'''found shape {input_tensor.shape[chunk_dim]}'''			)

								if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
												raise ValueError(
												    F'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
												    F'''size {chunk_size}'''			)

								snake_case__      :   Optional[Any]      =    input_tensors[0].shape[chunk_dim] // chunk_size

								# chunk input tensor into tuples
								snake_case__      :   List[Any]      =    tuple(input_tensor.chunk(UpperCamelCase__   ,				dim=UpperCamelCase__			) for input_tensor in input_tensors			)
								# apply forward fn to every tuple
								snake_case__      :   List[Any]      =    tuple(forward_fn(*UpperCamelCase__			) for input_tensors_chunk in zip(*UpperCamelCase__			)			)
								# concatenate output at same dimension
								return torch.cat(UpperCamelCase__   ,				dim=UpperCamelCase__			)

				return forward_fn(*UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[int]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Set[int]			)		->						Tuple[Set[int], torch.LongTensor]:
				snake_case__      :   str      =    torch.ones(UpperCamelCase__   ,				UpperCamelCase__			)
				snake_case__      :   int      =    set(UpperCamelCase__			) - already_pruned_heads  # Convert to set and remove already pruned heads
				for head in heads:
								# Compute how many pruned heads are before the head and move the index accordingly
								snake_case__      :   str      =    head - sum(1 if h < head else 0 for h in already_pruned_heads			)
								snake_case__      :   Optional[int]      =    0
				snake_case__      :   Optional[Any]      =    mask.view(-1			).contiguous().eq(1			)
				snake_case__      :   torch.LongTensor      =    torch.arange(len(UpperCamelCase__			)			)[mask].long()
				return heads, index


def 							__UpperCAmelCase				(				*UpperCamelCase__				:Union[torch.Tensor, List[torch.Tensor]]   ,				UpperCamelCase__				:Optional[str] = None			)		->						Tuple[torch.Tensor, ...]:
				return torch.meshgrid(*UpperCamelCase__   ,				indexing=UpperCamelCase__			)



def 							__UpperCAmelCase				(				UpperCamelCase__				:torch.Tensor			)		->						Tuple[torch.device, int, int]:
				return tensor.device, storage_ptr(UpperCamelCase__			), storage_size(UpperCamelCase__			)




