'''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


a=	logging.get_logger(__name__)

a=	{
    '''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       __lowercase		(    _lowerCamelCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    '''layoutlm'''







     def __init__(		self  ,      _lowerCamelCase=3_0_5_2_2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=3_0_7_2  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1E-1_2  ,      _lowerCamelCase=0  ,      _lowerCamelCase="absolute"  ,      _lowerCamelCase=True  ,      _lowerCamelCase=1_0_2_4  ,      **_lowerCamelCase  ,      ):
         super().__init__(pad_token_id=_lowerCamelCase  ,      **_lowerCamelCase     )
         __UpperCamelCase						:						str		     =	vocab_size
         __UpperCamelCase						:						List[Any]		     =	hidden_size
         __UpperCamelCase						:						Union[str, Any]		     =	num_hidden_layers
         __UpperCamelCase						:						Dict		     =	num_attention_heads
         __UpperCamelCase						:						str		     =	hidden_act
         __UpperCamelCase						:						Optional[int]		     =	intermediate_size
         __UpperCamelCase						:						str		     =	hidden_dropout_prob
         __UpperCamelCase						:						List[Any]		     =	attention_probs_dropout_prob
         __UpperCamelCase						:						List[Any]		     =	max_position_embeddings
         __UpperCamelCase						:						int		     =	type_vocab_size
         __UpperCamelCase						:						Dict		     =	initializer_range
         __UpperCamelCase						:						Optional[int]		     =	layer_norm_eps
         __UpperCamelCase						:						Union[str, Any]		     =	position_embedding_type
         __UpperCamelCase						:						Tuple		     =	use_cache
         __UpperCamelCase						:						Union[str, Any]		     =	max_ad_position_embeddings






class       __lowercase		(    _lowerCamelCase    ):







     """simple docstring"""






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







     @property
     def lowerCAmelCase  (		self     ):
         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 lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = -1  ,      _lowerCamelCase = False  ,      _lowerCamelCase = None  ,      ):
         __UpperCamelCase						:						List[Any]		     =	super().generate_dummy_inputs(
             _lowerCamelCase  ,      batch_size=_lowerCamelCase  ,      seq_length=_lowerCamelCase  ,      is_pair=_lowerCamelCase  ,      framework=_lowerCamelCase     )

         # Generate a dummy bbox
         __UpperCamelCase						:						Union[str, Any]		     =	[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

         __UpperCamelCase     ,       __UpperCamelCase						:						List[Any]		     =	input_dict['input_ids'].shape
         __UpperCamelCase						:						Union[str, Any]		     =	torch.tensor([*[box] * seq_length]     ).tile(_lowerCamelCase  ,      1  ,      1     )
         return input_dict




'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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







def 			_UpperCamelCase			(  _a						:						int					,  _a						:						bool = False      ):




    """simple docstring"""



    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(_a      )
    np.random.seed(_a      )
    torch.manual_seed(_a      )
    if is_xpu_available():
        torch.xpu.manual_seed_all(_a      )
    elif is_npu_available():
        torch.npu.manual_seed_all(_a      )
    else:
        torch.cuda.manual_seed_all(_a      )
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(_a      )







def 			_UpperCamelCase			(  _a						:						Optional[RNGType] = None					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    if rng_type == RNGType.TORCH:
        __UpperCamelCase						:						Union[str, Any]		     =	torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __UpperCamelCase						:						int		     =	torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        __UpperCamelCase						:						Dict		     =	torch.tensor(xm.get_rng_state()      )
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        __UpperCamelCase						:						Optional[Any]		     =	torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        __UpperCamelCase						:						str		     =	torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __UpperCamelCase						:						List[Any]		     =	generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __UpperCamelCase						:						List[Any]		     =	AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __UpperCamelCase						:						Union[str, Any]		     =	rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __UpperCamelCase						:						str		     =	rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        __UpperCamelCase						:						List[Any]		     =	rng_state.to(state.device      )
        torch.distributed.broadcast(_a					,  0      )
        __UpperCamelCase						:						Optional[Any]		     =	rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(_a					,  0      )

    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(_a      )
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(_a      )
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(_a      )
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(_a      )
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item()      )
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(_a      )







def 			_UpperCamelCase			(  _a						:						List[Union[str, RNGType]]					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    for rng_type in rng_types:
        synchronize_rng_state(RNGType(_a      )					,  generator=_a      )



