

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 						lowercase     (					_lowercase				):


		"""simple docstring"""




		a__       							=	"layoutlm"


		def __init__(					self						,							__snake_case=3_05_22						,							__snake_case=7_68						,							__snake_case=12						,							__snake_case=12						,							__snake_case=30_72						,							__snake_case="gelu"						,							__snake_case=0.1						,							__snake_case=0.1						,							__snake_case=5_12						,							__snake_case=2						,							__snake_case=0.0_2						,							__snake_case=1e-12						,							__snake_case=0						,							__snake_case="absolute"						,							__snake_case=True						,							__snake_case=10_24						,							**__snake_case						,							):
			super().__init__(pad_token_id=__snake_case						,							**__snake_case)
			_UpperCamelCase :		List[str]								=    vocab_size
			_UpperCamelCase :		Any								=    hidden_size
			_UpperCamelCase :		Optional[Any]								=    num_hidden_layers
			_UpperCamelCase :		Optional[Any]								=    num_attention_heads
			_UpperCamelCase :		List[str]								=    hidden_act
			_UpperCamelCase :		int								=    intermediate_size
			_UpperCamelCase :		Any								=    hidden_dropout_prob
			_UpperCamelCase :		List[str]								=    attention_probs_dropout_prob
			_UpperCamelCase :		Tuple								=    max_position_embeddings
			_UpperCamelCase :		int								=    type_vocab_size
			_UpperCamelCase :		str								=    initializer_range
			_UpperCamelCase :		Union[str, Any]								=    layer_norm_eps
			_UpperCamelCase :		Optional[Any]								=    position_embedding_type
			_UpperCamelCase :		Dict								=    use_cache
			_UpperCamelCase :		Tuple								=    max_ad_position_embeddings







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case = "default"						,							__snake_case = None						,							):
			super().__init__(__snake_case						,							task=__snake_case						,							patching_specs=__snake_case)
			_UpperCamelCase :		Optional[Any]								=    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						,							__snake_case						,							__snake_case = -1						,							__snake_case = -1						,							__snake_case = False						,							__snake_case = None						,							):
			_UpperCamelCase :		int								=    super().generate_dummy_inputs(
			    __snake_case						,							batch_size=__snake_case						,							seq_length=__snake_case						,							is_pair=__snake_case						,							framework=__snake_case)

			# Generate a dummy bbox
			_UpperCamelCase :		Any								=    [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 :		Optional[Any]								=    input_dict['input_ids'].shape
			_UpperCamelCase :		List[str]								=    torch.tensor([*[box] * seq_length]).tile(__snake_case						,							1						,							1)
			return input_dict







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__             =		{"""configuration_vit_hybrid""": ["""VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTHybridConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """ViTHybridForImageClassification""",
							    """ViTHybridModel""",
							    """ViTHybridPreTrainedModel""",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		["""ViTHybridImageProcessor"""]


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




