


"""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


SCREAMING_SNAKE_CASE :    int									=				logging.get_logger(__name__)

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



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='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_				,       ):





			'''simple docstring'''

			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_				,       )
			__snake_case	:						str						=				max_ad_position_embeddings
			__snake_case	:						Optional[Any]						=				coordinate_size
			__snake_case	:						str						=				shape_size
			__snake_case	:						Union[str, Any]						=				has_relative_attention_bias
			__snake_case	:						Dict						=				rel_pos_bins
			__snake_case	:						Union[str, Any]						=				max_rel_pos
			__snake_case	:						Union[str, Any]						=				has_spatial_attention_bias
			__snake_case	:						List[Any]						=				rel_ad_pos_bins
			__snake_case	:						List[Any]						=				max_rel_ad_pos
			__snake_case	:						Dict						=				text_embed
			__snake_case	:						Optional[Any]						=				visual_embed
			__snake_case	:						Dict						=				input_size
			__snake_case	:						Union[str, Any]						=				num_channels
			__snake_case	:						Any						=				patch_size
			__snake_case	:						Union[str, Any]						=				classifier_dropout



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=version.parse('1.12'				)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 1E-5



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 12



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = -1				,       a_ = -1				,       a_ = False				,       a_ = None				,       a_ = 3				,       a_ = 40				,       a_ = 40				,       ):





			'''simple docstring'''

			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
			__snake_case	:						List[str]						=				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
			__snake_case	:						Any						=				processor.tokenizer.num_special_tokens_to_add(a_					)
			__snake_case	:						Tuple						=				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
			__snake_case	:						Optional[int]						=				[[''' '''.join([processor.tokenizer.unk_token]					) * seq_length]] * batch_size

			# Generate dummy bounding boxes
			__snake_case	:						Any						=				[[[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)
			__snake_case	:						int						=				self._generate_dummy_images(a_				,       a_				,       a_				,       a_					)

			__snake_case	:						Tuple						=				dict(
			    processor(
			        a_				,       text=a_				,       boxes=a_				,       return_tensors=a_				,       )					)

			return inputs








import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def   __SCREAMING_SNAKE_CASE					(*SCREAMING_SNAKE_CASE__       ):
						if not isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	list(SCREAMING_SNAKE_CASE__       )
						for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
												snake_case_   =	None
						gc.collect()
						if is_xpu_available():
												torch.xpu.empty_cache()
						elif is_npu_available():
												torch.npu.empty_cache()
						else:
												torch.cuda.empty_cache()
						return objects


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[
						    '''CUDA out of memory.''',  # CUDA OOM
						    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
						    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
						]
						if isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ) and len(exception.args       ) == 1:
												return any(err in exception.args[0] for err in _statements       )
						return False


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = None  ,		SCREAMING_SNAKE_CASE__ = 128       ):
						if function is None:
												return functools.partial(SCREAMING_SNAKE_CASE__  ,		starting_batch_size=SCREAMING_SNAKE_CASE__       )

						snake_case_   =	starting_batch_size

						def decorator(*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       ):
												nonlocal batch_size
												gc.collect()
												if is_xpu_available():
																		torch.xpu.empty_cache()
												elif is_npu_available():
																		torch.npu.empty_cache()
												else:
																		torch.cuda.empty_cache()
												snake_case_   =	list(inspect.signature(SCREAMING_SNAKE_CASE__       ).parameters.keys()       )
												# Guard against user error
												if len(SCREAMING_SNAKE_CASE__       ) < (len(SCREAMING_SNAKE_CASE__       ) + 1):
																		snake_case_   =	''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]  ,		args[1:]       )]       )
																		raise TypeError(
																		    F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
																		    F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''       )
												while True:
																		if batch_size == 0:
																								raise RuntimeError('''No executable batch size found, reached zero.'''       )
																		try:
																								return function(SCREAMING_SNAKE_CASE__  ,		*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )
																		except Exception as e:
																								if should_reduce_batch_size(SCREAMING_SNAKE_CASE__       ):
																														gc.collect()
																														if is_xpu_available():
																																				torch.xpu.empty_cache()
																														elif is_npu_available():
																																				torch.npu.empty_cache()
																														else:
																																				torch.cuda.empty_cache()
																														batch_size //= 2
																								else:
																														raise

						return decorator