
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


lowercase__		:List[Any]			  =   logging.get_logger(__name__)

lowercase__		:Any			  =   {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	str	 ='''layoutlmv3'''




							def __init__(    self       ,A__=5_0_2_6_5       ,A__=7_6_8       ,A__=1_2       ,A__=1_2       ,A__=3_0_7_2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=2       ,A__=0.02       ,A__=1E-5       ,A__=1       ,A__=0       ,A__=2       ,A__=1_0_2_4       ,A__=1_2_8       ,A__=1_2_8       ,A__=True       ,A__=3_2       ,A__=1_2_8       ,A__=6_4       ,A__=2_5_6       ,A__=True       ,A__=True       ,A__=True       ,A__=2_2_4       ,A__=3       ,A__=1_6       ,A__=None       ,**A__       ,):
												super().__init__(
												    vocab_size=A__       ,hidden_size=A__       ,num_hidden_layers=A__       ,num_attention_heads=A__       ,intermediate_size=A__       ,hidden_act=A__       ,hidden_dropout_prob=A__       ,attention_probs_dropout_prob=A__       ,max_position_embeddings=A__       ,type_vocab_size=A__       ,initializer_range=A__       ,layer_norm_eps=A__       ,pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__       ,)
												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     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[Any]	 =version.parse('''1.12'''		)




							@property
							def 			A__				(    self):
												# The order of inputs is different for question answering and sequence classification
												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 			A__				(    self):
												return 1E-5




							@property
							def 			A__				(    self):
												return 1_2





							def 			A__				(    self       ,A__       ,A__ = -1       ,A__ = -1       ,A__ = False       ,A__ = None       ,A__ = 3       ,A__ = 4_0       ,A__ = 4_0       ,):
												setattr(processor.image_processor       ,'''apply_ocr'''       ,A__)

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

												# Generate dummy bounding boxes
												lowercase						     =						[[[4_8, 8_4, 7_3, 1_2_8]]] * 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(A__       ,A__       ,A__       ,A__)

												lowercase						     =						dict(
												    processor(
												        A__       ,text=A__       ,boxes=A__       ,return_tensors=A__       ,))

												return inputs






from typing import TYPE_CHECKING

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


lowercase__		:Union[str, Any]			  =   {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:List[Any]			  =   ["DPTFeatureExtractor"]
							lowercase__		:List[Any]			  =   ["DPTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DPTForDepthEstimation",
							    "DPTForSemanticSegmentation",
							    "DPTModel",
							    "DPTPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_dpt import DPTFeatureExtractor
														from .image_processing_dpt import DPTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dpt import (
														    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DPTForDepthEstimation,
														    DPTForSemanticSegmentation,
														    DPTModel,
														    DPTPreTrainedModel,
														)


else:
							import sys

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




