
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


__lowerCamelCase								=					logging.get_logger(__name__)

__lowerCamelCase								=					{
    '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    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						Optional[int]				   =      """layoutlm"""

						def __init__(					self		:			Optional[Any]	,						_lowerCamelCase		:			Optional[Any]=3_05_22	,						_lowerCamelCase		:			Optional[int]=7_68	,						_lowerCamelCase		:			Any=12	,						_lowerCamelCase		:			List[str]=12	,						_lowerCamelCase		:			Any=30_72	,						_lowerCamelCase		:			Any="gelu"	,						_lowerCamelCase		:			Optional[Any]=0.1	,						_lowerCamelCase		:			Optional[int]=0.1	,						_lowerCamelCase		:			List[str]=5_12	,						_lowerCamelCase		:			Union[str, Any]=2	,						_lowerCamelCase		:			Optional[int]=0.02	,						_lowerCamelCase		:			str=1E-12	,						_lowerCamelCase		:			Dict=0	,						_lowerCamelCase		:			Optional[int]="absolute"	,						_lowerCamelCase		:			List[str]=True	,						_lowerCamelCase		:			Union[str, Any]=10_24	,						**_lowerCamelCase		:			int	,						):
													super().__init__(pad_token_id=_lowerCamelCase	,						**_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		vocab_size
													_UpperCAmelCase  :			List[Any]	       =		hidden_size
													_UpperCAmelCase  :			List[Any]	       =		num_hidden_layers
													_UpperCAmelCase  :			Any	       =		num_attention_heads
													_UpperCAmelCase  :			int	       =		hidden_act
													_UpperCAmelCase  :			Optional[Any]	       =		intermediate_size
													_UpperCAmelCase  :			List[Any]	       =		hidden_dropout_prob
													_UpperCAmelCase  :			List[Any]	       =		attention_probs_dropout_prob
													_UpperCAmelCase  :			Dict	       =		max_position_embeddings
													_UpperCAmelCase  :			List[Any]	       =		type_vocab_size
													_UpperCAmelCase  :			str	       =		initializer_range
													_UpperCAmelCase  :			int	       =		layer_norm_eps
													_UpperCAmelCase  :			Tuple	       =		position_embedding_type
													_UpperCAmelCase  :			str	       =		use_cache
													_UpperCAmelCase  :			Any	       =		max_ad_position_embeddings







class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):

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

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

													# Generate a dummy bbox
													_UpperCAmelCase  :			Optional[int]	       =		[48, 84, 73, 1_28]

													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       ,_UpperCAmelCase  :			List[Any]	       =		input_dict["input_ids"].shape
													_UpperCAmelCase  :			List[str]	       =		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"			)




