
"""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


UpperCamelCase      :							Any          =					logging.get_logger(__name__)

UpperCamelCase      :							Optional[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						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"""layoutlm"""





     def __init__(      self      ,	__UpperCAmelCase=3_0522      ,	__UpperCAmelCase=768      ,	__UpperCAmelCase=12      ,	__UpperCAmelCase=12      ,	__UpperCAmelCase=3072      ,	__UpperCAmelCase="gelu"      ,	__UpperCAmelCase=0.1      ,	__UpperCAmelCase=0.1      ,	__UpperCAmelCase=512      ,	__UpperCAmelCase=2      ,	__UpperCAmelCase=0.0_2      ,	__UpperCAmelCase=1E-12      ,	__UpperCAmelCase=0      ,	__UpperCAmelCase="absolute"      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=1024      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      super().__init__(pad_token_id=_lowerCamelCase      ,	**_lowerCamelCase )
      __UpperCamelCase							    =			vocab_size
      __UpperCamelCase							    =			hidden_size
      __UpperCamelCase							    =			num_hidden_layers
      __UpperCamelCase							    =			num_attention_heads
      __UpperCamelCase							    =			hidden_act
      __UpperCamelCase							    =			intermediate_size
      __UpperCamelCase							    =			hidden_dropout_prob
      __UpperCamelCase							    =			attention_probs_dropout_prob
      __UpperCamelCase							    =			max_position_embeddings
      __UpperCamelCase							    =			type_vocab_size
      __UpperCamelCase							    =			initializer_range
      __UpperCamelCase							    =			layer_norm_eps
      __UpperCamelCase							    =			position_embedding_type
      __UpperCamelCase							    =			use_cache
      __UpperCamelCase							    =			max_ad_position_embeddings


class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):





     def __init__(      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = "default"      ,	__UpperCAmelCase = None      ,	):

      '''simple docstring'''




      super().__init__(_lowerCamelCase      ,	task=_lowerCamelCase      ,	patching_specs=_lowerCamelCase )
      __UpperCamelCase							    =			config.max_ad_position_embeddings - 1





     @property
     def 		UpperCAmelCase   (      self ):

      '''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 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase = -1      ,	__UpperCAmelCase = -1      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = None      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			super().generate_dummy_inputs(
          _lowerCamelCase      ,	batch_size=_lowerCamelCase      ,	seq_length=_lowerCamelCase      ,	is_pair=_lowerCamelCase      ,	framework=_lowerCamelCase )

      # Generate a dummy bbox
      __UpperCamelCase							    =			[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							    =			input_dict["input_ids"].shape
      __UpperCamelCase							    =			torch.tensor([*[box] * seq_length] ).tile(_lowerCamelCase      ,	1      ,	1 )
      return input_dict





import torch

from accelerate import Accelerator, DistributedType
class    _UpperCamelCase:

						def __enter__(					self		:			str			):
													if self.enabled:
																				_UpperCAmelCase  :			int	       =		self.model.no_sync()
																				self.model_sync_obj.__enter__()

													return self

						def __exit__(					self		:			str	,						_lowerCamelCase		:			Union[str, Any]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			Optional[Any]			):
													if self.enabled:
																				# Average all models on exit
																				self._sync_and_avg_model_params()
																				self.model_sync_obj.__exit__(_lowerCamelCase	,						_lowerCamelCase	,						_lowerCamelCase			)

						def __init__(					self		:			str	,						_lowerCamelCase		:			Accelerator	,						_lowerCamelCase		:			torch.nn.Module	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			bool = True			):
													if accelerator.distributed_type not in [
													    DistributedType.NO,
													    DistributedType.MULTI_CPU,
													    DistributedType.MULTI_GPU,
													]:
																				raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"			)
													_UpperCAmelCase  :			Any	       =		enabled and accelerator.distributed_type != DistributedType.NO
													_UpperCAmelCase  :			str	       =		0
													if self.enabled:
																				_UpperCAmelCase  :			Tuple	       =		accelerator
																				_UpperCAmelCase  :			List[str]	       =		model
																				_UpperCAmelCase  :			Dict	       =		local_sgd_steps

						def 						a__ (					self		:			Optional[int]			):
													self.num_steps += 1
													if not self.enabled:
																				return

													if self.num_steps % self.local_sgd_steps == 0:
																				self._sync_and_avg_model_params()






						def 						a__ (					self		:			Tuple			):
													self.accelerator.wait_for_everyone()
													with self.accelerator.autocast():
																				for param in self.model.parameters():
																											_UpperCAmelCase  :			Optional[Any]	       =		self.accelerator.reduce(param.data	,						reduction="mean"			)




