


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       :       int				   =			logging.get_logger(__name__)

__lowerCamelCase       :       List[str]				   =			{
    "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 			a      (      UpperCamelCase_   ):
		__lowercase													=		"""layoutlm"""






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


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







class 			a      (      UpperCamelCase_   ):






		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase = "default"	,     __UpperCamelCase = None	,     )->			List[str]:
						'''simple docstring'''


						super().__init__(__UpperCamelCase	,     task=__UpperCamelCase	,     patching_specs=__UpperCamelCase							)
						A__     :							Any   				=config.max_ad_position_embeddings - 1






		@property
		def       lowerCAmelCase_  (  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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = -1	,     __UpperCamelCase = -1	,     __UpperCamelCase = False	,     __UpperCamelCase = None	,     )->			Mapping[str, Any]:
						'''simple docstring'''


						A__     :							Dict   				=super().generate_dummy_inputs(
						    __UpperCamelCase	,     batch_size=__UpperCamelCase	,     seq_length=__UpperCamelCase	,     is_pair=__UpperCamelCase	,     framework=__UpperCamelCase							)

						# Generate a dummy bbox
						A__     :							Tuple   				=[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

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







from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available



@dataclass
class 			a      (      UpperCamelCase_   ):
		__lowercase													=		{
		    """sld_warmup_steps""": 15,
		    """sld_guidance_scale""": 20,
		    """sld_threshold""": 0.0,
		    """sld_momentum_scale""": 0.0,
		    """sld_mom_beta""": 0.0,
		}
		__lowercase													=		{
		    """sld_warmup_steps""": 10,
		    """sld_guidance_scale""": 1_000,
		    """sld_threshold""": 0.01,
		    """sld_momentum_scale""": 0.3,
		    """sld_mom_beta""": 0.4,
		}
		__lowercase													=		{
		    """sld_warmup_steps""": 7,
		    """sld_guidance_scale""": 2_000,
		    """sld_threshold""": 0.0_25,
		    """sld_momentum_scale""": 0.5,
		    """sld_mom_beta""": 0.7,
		}
		__lowercase													=		{
		    """sld_warmup_steps""": 0,
		    """sld_guidance_scale""": 5_000,
		    """sld_threshold""": 1.0,
		    """sld_momentum_scale""": 0.5,
		    """sld_mom_beta""": 0.7,
		}







@dataclass
class 			a      (      UpperCamelCase_   ):
		__lowercase													=		42
		__lowercase													=		42
		__lowercase													=		42
		__lowercase													=		42


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker



