


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
			from ...processing_utils import ProcessorMixin
			from ...utils import TensorType


lowerCAmelCase			=				logging.get_logger(__name__)

lowerCAmelCase			=				{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     List[str]     =					'''layoutlmv3'''

							def __init__(				self:      Optional[Any]       ,		UpperCamelCase_:      Union[str, Any]=50_265       ,		UpperCamelCase_:      Tuple=768       ,		UpperCamelCase_:      int=12       ,		UpperCamelCase_:      List[str]=12       ,		UpperCamelCase_:      List[Any]=3_072       ,		UpperCamelCase_:      Any="gelu"       ,		UpperCamelCase_:      Any=0.1       ,		UpperCamelCase_:      str=0.1       ,		UpperCamelCase_:      str=512       ,		UpperCamelCase_:      Optional[Any]=2       ,		UpperCamelCase_:      str=0.02       ,		UpperCamelCase_:      int=1E-5       ,		UpperCamelCase_:      List[Any]=1       ,		UpperCamelCase_:      List[str]=0       ,		UpperCamelCase_:      str=2       ,		UpperCamelCase_:      Any=1_024       ,		UpperCamelCase_:      List[str]=128       ,		UpperCamelCase_:      List[str]=128       ,		UpperCamelCase_:      Tuple=True       ,		UpperCamelCase_:      Any=32       ,		UpperCamelCase_:      Any=128       ,		UpperCamelCase_:      Optional[int]=64       ,		UpperCamelCase_:      str=256       ,		UpperCamelCase_:      Dict=True       ,		UpperCamelCase_:      Optional[int]=True       ,		UpperCamelCase_:      Optional[Any]=True       ,		UpperCamelCase_:      Optional[int]=224       ,		UpperCamelCase_:      Any=3       ,		UpperCamelCase_:      int=16       ,		UpperCamelCase_:      List[Any]=None       ,		**UpperCamelCase_:      List[str]       ,		)     ->  Optional[int]:





									"""simple docstring"""







									super().__init__(
									    vocab_size=UpperCamelCase_       ,		hidden_size=UpperCamelCase_       ,		num_hidden_layers=UpperCamelCase_       ,		num_attention_heads=UpperCamelCase_       ,		intermediate_size=UpperCamelCase_       ,		hidden_act=UpperCamelCase_       ,		hidden_dropout_prob=UpperCamelCase_       ,		attention_probs_dropout_prob=UpperCamelCase_       ,		max_position_embeddings=UpperCamelCase_       ,		type_vocab_size=UpperCamelCase_       ,		initializer_range=UpperCamelCase_       ,		layer_norm_eps=UpperCamelCase_       ,		pad_token_id=UpperCamelCase_       ,		bos_token_id=UpperCamelCase_       ,		eos_token_id=UpperCamelCase_       ,		**UpperCamelCase_       ,		)
									lowercase__				= max_ad_position_embeddings
									lowercase__				= coordinate_size
									lowercase__				= shape_size
									lowercase__				= has_relative_attention_bias
									lowercase__				= rel_pos_bins
									lowercase__				= max_rel_pos
									lowercase__				= has_spatial_attention_bias
									lowercase__				= rel_ad_pos_bins
									lowercase__				= max_rel_ad_pos
									lowercase__				= text_embed
									lowercase__				= visual_embed
									lowercase__				= input_size
									lowercase__				= num_channels
									lowercase__				= patch_size
									lowercase__				= classifier_dropout


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     List[str]     =					version.parse('''1.12'''     )

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





									"""simple docstring"""







									if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
											        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
											        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
											    ]				)
									else:
											return OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
											        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
											        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
											    ]				)

							@property
							def  lowerCamelCase_					(				self:      Any				)     ->  float:





									"""simple docstring"""







									return 1E-5

							@property
							def  lowerCamelCase_					(				self:      str				)     ->  int:





									"""simple docstring"""







									return 12


							def  lowerCamelCase_					(				self:      List[str]       ,		UpperCamelCase_:      "ProcessorMixin"       ,		UpperCamelCase_:      int = -1       ,		UpperCamelCase_:      int = -1       ,		UpperCamelCase_:      bool = False       ,		UpperCamelCase_:      Optional["TensorType"] = None       ,		UpperCamelCase_:      int = 3       ,		UpperCamelCase_:      int = 40       ,		UpperCamelCase_:      int = 40       ,		)     ->  Mapping[str, Any]:





									"""simple docstring"""







									setattr(processor.image_processor       ,		'''apply_ocr'''       ,		UpperCamelCase_				)

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									lowercase__				= compute_effective_axis_dimension(
									    UpperCamelCase_       ,		fixed_dimension=OnnxConfig.default_fixed_batch       ,		num_token_to_add=0				)
									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									lowercase__				= processor.tokenizer.num_special_tokens_to_add(UpperCamelCase_				)
									lowercase__				= compute_effective_axis_dimension(
									    UpperCamelCase_       ,		fixed_dimension=OnnxConfig.default_fixed_sequence       ,		num_token_to_add=UpperCamelCase_				)
									# Generate dummy inputs according to compute batch and sequence
									lowercase__				= [[''' '''.join([processor.tokenizer.unk_token]				) * seq_length]] * batch_size

									# Generate dummy bounding boxes
									lowercase__				= [[[48, 84, 73, 128]]] * batch_size

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
									lowercase__				= self._generate_dummy_images(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_				)

									lowercase__				= dict(
									    processor(
									        UpperCamelCase_       ,		text=UpperCamelCase_       ,		boxes=UpperCamelCase_       ,		return_tensors=UpperCamelCase_       ,		)				)

									return inputs





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					List[Any]						=			{
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GitForCausalLM',
        'GitModel',
        'GitPreTrainedModel',
        'GitVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)