





"""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


A 			=  logging.get_logger(__name__)

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




class 				__lowercase			(			UpperCamelCase_							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''layoutlmv3'''







					def __init__(						self	,			_UpperCAmelCase=50265	,			_UpperCAmelCase=768	,			_UpperCAmelCase=12	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3072	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=512	,			_UpperCAmelCase=2	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1e-5	,			_UpperCAmelCase=1	,			_UpperCAmelCase=0	,			_UpperCAmelCase=2	,			_UpperCAmelCase=1024	,			_UpperCAmelCase=128	,			_UpperCAmelCase=128	,			_UpperCAmelCase=True	,			_UpperCAmelCase=32	,			_UpperCAmelCase=128	,			_UpperCAmelCase=64	,			_UpperCAmelCase=256	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=True	,			_UpperCAmelCase=224	,			_UpperCAmelCase=3	,			_UpperCAmelCase=16	,			_UpperCAmelCase=None	,			**_UpperCAmelCase	,			):
									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__	,			)
									__a :						Union[str, Any]			       =  max_ad_position_embeddings
									__a :						List[Any]			       =  coordinate_size
									__a :						List[str]			       =  shape_size
									__a :						Optional[int]			       =  has_relative_attention_bias
									__a :						List[Any]			       =  rel_pos_bins
									__a :						str			       =  max_rel_pos
									__a :						Any			       =  has_spatial_attention_bias
									__a :						Union[str, Any]			       =  rel_ad_pos_bins
									__a :						Union[str, Any]			       =  max_rel_ad_pos
									__a :						Union[str, Any]			       =  text_embed
									__a :						List[str]			       =  visual_embed
									__a :						Optional[Any]			       =  input_size
									__a :						Optional[Any]			       =  num_channels
									__a :						List[Any]			       =  patch_size
									__a :						List[Any]			       =  classifier_dropout






class 				__lowercase			(			UpperCamelCase_							):







					'''simple docstring'''




					__lowerCAmelCase  =  version.parse('''1.12'''							)







					@property
					def  _lowerCamelCase		(						self     ):





									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     ):
									return 1e-5







					@property
					def  _lowerCamelCase		(						self     ):
									return 12







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = -1	,			_UpperCAmelCase = -1	,			_UpperCAmelCase = False	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 3	,			_UpperCAmelCase = 40	,			_UpperCAmelCase = 40	,			):
									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
									__a :						Any			       =  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
									__a :						List[Any]			       =  processor.tokenizer.num_special_tokens_to_add(UpperCamelCase__     )
									__a :						Tuple			       =  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
									__a :						Union[str, Any]			       =  [[''' '''.join([processor.tokenizer.unk_token]     ) * seq_length]] * batch_size

									# Generate dummy bounding boxes
									__a :						Any			       =  [[[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)
									__a :						List[Any]			       =  self._generate_dummy_images(UpperCamelCase__	,			UpperCamelCase__	,			UpperCamelCase__	,			UpperCamelCase__     )

									__a :						Optional[int]			       =  dict(
									    processor(
									        UpperCamelCase__	,			text=UpperCamelCase__	,			boxes=UpperCamelCase__	,			return_tensors=UpperCamelCase__	,			)     )

									return inputs






"""simple docstring"""






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A 			=  abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)

def     __A      (			a_						:Tuple)		->		Dict:
				from transformers.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(a_)






def     __A      (			a_						:Any)		->		int:
				from transformers.testing_utils import pytest_terminal_summary_main

				__a :						str			       =  terminalreporter.config.getoption('''--make-reports''')
				if make_reports:
								pytest_terminal_summary_main(a_   ,       id=a_)