





"""simple docstring"""





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        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"layoutlmv3"


						def __init__(self   ,							__a=5_02_65   ,							__a=7_68   ,							__a=12   ,							__a=12   ,							__a=30_72   ,							__a="gelu"   ,							__a=0.1   ,							__a=0.1   ,							__a=5_12   ,							__a=2   ,							__a=0.02   ,							__a=1e-5   ,							__a=1   ,							__a=0   ,							__a=2   ,							__a=10_24   ,							__a=1_28   ,							__a=1_28   ,							__a=True   ,							__a=32   ,							__a=1_28   ,							__a=64   ,							__a=2_56   ,							__a=True   ,							__a=True   ,							__a=True   ,							__a=2_24   ,							__a=3   ,							__a=16   ,							__a=None   ,							**__a   ,							) ->   Optional[Any]:
								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   ,							)
								UpperCamelCase   =	max_ad_position_embeddings
								UpperCamelCase   =	coordinate_size
								UpperCamelCase   =	shape_size
								UpperCamelCase   =	has_relative_attention_bias
								UpperCamelCase   =	rel_pos_bins
								UpperCamelCase   =	max_rel_pos
								UpperCamelCase   =	has_spatial_attention_bias
								UpperCamelCase   =	rel_ad_pos_bins
								UpperCamelCase   =	max_rel_ad_pos
								UpperCamelCase   =	text_embed
								UpperCamelCase   =	visual_embed
								UpperCamelCase   =	input_size
								UpperCamelCase   =	num_channels
								UpperCamelCase   =	patch_size
								UpperCamelCase   =	classifier_dropout





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			version.parse("1.12" )


						@property
						def     snake_case_   (self		) ->   Mapping[str, Mapping[int, str]]:
								# 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     snake_case_   (self		) ->   float:
								return 1e-5


						@property
						def     snake_case_   (self		) ->   int:
								return 12




						def     snake_case_   (self   ,							__a   ,							__a = -1   ,							__a = -1   ,							__a = False   ,							__a = None   ,							__a = 3   ,							__a = 40   ,							__a = 40   ,							) ->   Mapping[str, Any]:
								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
								UpperCamelCase   =	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
								UpperCamelCase   =	processor.tokenizer.num_special_tokens_to_add(__a		)
								UpperCamelCase   =	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
								UpperCamelCase   =	[[" ".join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

								# Generate dummy bounding boxes
								UpperCamelCase   =	[[[48, 84, 73, 1_28]]] * 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)
								UpperCamelCase   =	self._generate_dummy_images(__a   ,							__a   ,							__a   ,							__a		)

								UpperCamelCase   =	dict(
								    processor(
								        __a   ,							text=__a   ,							boxes=__a   ,							return_tensors=__a   ,							)		)

								return inputs













"""simple docstring"""





lowerCAmelCase__     			=			'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase__     			=			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase__     			=			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






