"""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    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   int  							=       '''layoutlm'''

				def __init__(      self	, _lowerCamelCase=30522	, _lowerCamelCase=768	, _lowerCamelCase=12	, _lowerCamelCase=12	, _lowerCamelCase=3072	, _lowerCamelCase="gelu"	, _lowerCamelCase=0.1	, _lowerCamelCase=0.1	, _lowerCamelCase=512	, _lowerCamelCase=2	, _lowerCamelCase=0.0_2	, _lowerCamelCase=1E-12	, _lowerCamelCase=0	, _lowerCamelCase="absolute"	, _lowerCamelCase=True	, _lowerCamelCase=1024	, **_lowerCamelCase	, ):
										super().__init__(pad_token_id=_lowerCamelCase	, **_lowerCamelCase      )
										lowerCamelCase__					=vocab_size
										lowerCamelCase__					=hidden_size
										lowerCamelCase__					=num_hidden_layers
										lowerCamelCase__					=num_attention_heads
										lowerCamelCase__					=hidden_act
										lowerCamelCase__					=intermediate_size
										lowerCamelCase__					=hidden_dropout_prob
										lowerCamelCase__					=attention_probs_dropout_prob
										lowerCamelCase__					=max_position_embeddings
										lowerCamelCase__					=type_vocab_size
										lowerCamelCase__					=initializer_range
										lowerCamelCase__					=layer_norm_eps
										lowerCamelCase__					=position_embedding_type
										lowerCamelCase__					=use_cache
										lowerCamelCase__					=max_ad_position_embeddings






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase = "default"	, _lowerCamelCase = None	, ):
										super().__init__(_lowerCamelCase	, task=_lowerCamelCase	, patching_specs=_lowerCamelCase      )
										lowerCamelCase__					=config.max_ad_position_embeddings - 1

				@property
				def 		_a	(      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 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = -1	, _lowerCamelCase = -1	, _lowerCamelCase = False	, _lowerCamelCase = None	, ):
										lowerCamelCase__					=super().generate_dummy_inputs(
										    _lowerCamelCase	, batch_size=_lowerCamelCase	, seq_length=_lowerCamelCase	, is_pair=_lowerCamelCase	, framework=_lowerCamelCase      )

										# Generate a dummy bbox
										lowerCamelCase__					=[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

										lowerCamelCase__					,      lowerCamelCase__					=input_dict["input_ids"].shape
										lowerCamelCase__					=torch.tensor([*[box] * seq_length]      ).tile(_lowerCamelCase	, 1	, 1      )
										return input_dict



"""simple docstring"""


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a							=logging.get_logger(__name__)






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Any  							=       '''hybrid-clip'''
				A__      :   Optional[Any]  							=       True

				def __init__(      self	, _lowerCamelCase=512	, **_lowerCamelCase      ):
										super().__init__(**_lowerCamelCase      )

										if "text_config" not in kwargs:
																raise ValueError("`text_config` can not be `None`."      )

										if "vision_config" not in kwargs:
																raise ValueError("`vision_config` can not be `None`."      )

										lowerCamelCase__					=kwargs.pop("text_config"      )
										lowerCamelCase__					=kwargs.pop("vision_config"      )

										lowerCamelCase__					=text_config.pop("model_type"      )
										lowerCamelCase__					=vision_config.pop("model_type"      )

										from transformers import AutoConfig

										lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										if vision_model_type == "clip":
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      ).vision_config
										elif vision_model_type == "clip_vision_model":
																from transformers import CLIPVisionConfig

																lowerCamelCase__					=CLIPVisionConfig(**_lowerCamelCase      )
										else:
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=projection_dim
										lowerCamelCase__					=1.0

				@classmethod
				def 		_a	(      cls	, _lowerCamelCase	, _lowerCamelCase	, **_lowerCamelCase      ):
										return cls(text_config=text_config.to_dict()	, vision_config=vision_config.to_dict()	, **_lowerCamelCase      )






				def 		_a	(      self      ):
										lowerCamelCase__					=copy.deepcopy(self.__dict__      )
										lowerCamelCase__					=self.text_config.to_dict()
										lowerCamelCase__					=self.vision_config.to_dict()
										lowerCamelCase__					=self.__class__.model_type
										return output


