import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				if isinstance(lowercase							,      (list, tuple)							):
								return honor_type(lowercase							,      (move_to_device(lowercase							,      lowercase							) for t in state)							)
				elif isinstance(lowercase							,      lowercase							):
								return type(lowercase							)({k: move_to_device(lowercase							,      lowercase							) for k, v in state.items()}							)
				elif isinstance(lowercase							,      torch.Tensor							):
								return state.to(lowercase							)
				return state




class        _UpperCamelCase				(						torch.optim.Optimizer			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=True				,       lowerCAmelCase__=None						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							optimizer
							__lowercase			      =							scaler
							__lowercase			      =							AcceleratorState()
							__lowercase			      =							GradientState()
							__lowercase			      =							device_placement
							__lowercase			      =							False
							__lowercase			      =							None

							# Handle device placement
							if device_placement:
											__lowercase			      =							self.optimizer.state_dict()
											if self.accelerator_state.distributed_type == DistributedType.TPU:
															xm.send_cpu_data_to_device(lowerCAmelCase__				,       self.accelerator_state.device						)
											else:
															__lowercase			      =							move_to_device(lowerCAmelCase__				,       self.accelerator_state.device						)
											self.optimizer.load_state_dict(lowerCAmelCase__						)





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							return self.optimizer.state





			@state.setter
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							__lowercase			      =							state





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							return self.optimizer.param_groups





			@param_groups.setter
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							param_groups





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							return self.optimizer.defaults





			@defaults.setter
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							defaults





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							self.optimizer.add_param_group(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
											xm.send_cpu_data_to_device(lowerCAmelCase__				,       self.accelerator_state.device						)
							self.optimizer.load_state_dict(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							return self.optimizer.state_dict()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=None						)					-> int:

							'''simple docstring'''


							if self.gradient_state.sync_gradients:
											__lowercase			      =							'''set_to_none''' in inspect.signature(self.optimizer.zero_grad						).parameters
											if accept_arg:
															if set_to_none is None:
																			__lowercase			      =							False
															self.optimizer.zero_grad(set_to_none=lowerCAmelCase__						)
											else:
															if set_to_none is not None:
																			raise ValueError('''`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'''						)
															self.optimizer.zero_grad()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=None						)					-> str:

							'''simple docstring'''


							if self.gradient_state.sync_gradients:
											if self.accelerator_state.distributed_type == DistributedType.TPU:
															__lowercase			      =							{'''closure''': closure} if closure is not None else {}
															xm.optimizer_step(self.optimizer				,       optimizer_args=lowerCAmelCase__						)
											elif self.scaler is not None:
															__lowercase			      =							False
															if self._last_scale is None:
																			# `get_scale` is an async operation requiring full synchronization
																			# on CPU and GPUs before finishing. As a result, we store away
																			# the prior one to reduce the call overhead
																			__lowercase			      =							self.scaler.get_scale()
																			__lowercase			      =							True
															self.scaler.step(self.optimizer				,       lowerCAmelCase__						)
															self.scaler.update()
															__lowercase			      =							self.scaler.get_scale()
															if not new_scale:
																			# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																			__lowercase			      =							scale_after < self._last_scale
															__lowercase			      =							scale_after

											else:
															self.optimizer.step(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							for param_group in self.optimizer.param_groups:
											__lowercase			      =							[parameters_map.get(lowerCAmelCase__				,       lowerCAmelCase__						) for p in param_group['''params''']]





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							warnings.warn(
							    '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
							    '''`optimizer.step_was_skipped` instead.'''				,       lowerCAmelCase__				,       )
							return self._is_overflow





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							return self._is_overflow





			def __getstate__(       self						)					-> Optional[int]:

							'''simple docstring'''


							return self.__dict__.copy()





			def __setstate__(       self				,       lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							self.__dict__.update(lowerCAmelCase__						)
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


__a  :					int          =  logging.get_logger(__name__)

__a  :					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        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''layoutlm'''





			def __init__(       self				,       lowerCAmelCase__=3_05_22				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=12				,       lowerCAmelCase__=12				,       lowerCAmelCase__=30_72				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=2				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=1E-12				,       lowerCAmelCase__=0				,       lowerCAmelCase__="absolute"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=10_24				,       **lowerCAmelCase__				,       )					-> Tuple:

							'''simple docstring'''


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




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = "default"				,       lowerCAmelCase__ = None				,       )					-> str:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__				,       task=lowerCAmelCase__				,       patching_specs=lowerCAmelCase__						)
							__lowercase			      =							config.max_ad_position_embeddings - 1





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, str]]:

							'''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 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							super().generate_dummy_inputs(
							    lowerCAmelCase__				,       batch_size=lowerCAmelCase__				,       seq_length=lowerCAmelCase__				,       is_pair=lowerCAmelCase__				,       framework=lowerCAmelCase__						)

							# Generate a dummy bbox
							__lowercase			      =							[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

							__lowercase      ,					__lowercase			      =							input_dict['''input_ids'''].shape
							__lowercase			      =							torch.tensor([*[box] * seq_length]						).tile(lowerCAmelCase__				,       1				,       1						)
							return input_dict