
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							:List[str]										=    logging.get_logger(__name__)

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





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"layoutlmv3"






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





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					version.parse("1.12"  )






	@property
	def     UpperCamelCase_  (	self :			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     UpperCamelCase_  (	self :			Optional[Any]			):
					return 1E-5






	@property
	def     UpperCamelCase_  (	self :			Any			):
					return 12







	def     UpperCamelCase_  (	self :			Dict							,A :			"ProcessorMixin"							,A :			int = -1							,A :			int = -1							,A :			bool = False							,A :			Optional["TensorType"] = None							,A :			int = 3							,A :			int = 40							,A :			int = 40							,):
					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
					__A             =			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
					__A             =			processor.tokenizer.num_special_tokens_to_add(A			)
					__A             =			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
					__A             =			[[" ".join([processor.tokenizer.unk_token]			) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					__A             =			[[[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)
					__A             =			self._generate_dummy_images(A							,A							,A							,A			)

					__A             =			dict(
					    processor(
					        A							,text=A							,boxes=A							,return_tensors=A							,)			)

					return inputs


from __future__ import annotations


def 						UpperCAmelCase      (     a_ = 4						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			abs(a_						) or 4
				return [[1 + x + y * row_size for x in range(a_						)] for y in range(a_						)]


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				return reverse_row(transpose(a_						)						)
				# OR.. transpose(reverse_column(matrix))


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				return reverse_row(reverse_column(a_						)						)
				# OR.. reverse_column(reverse_row(matrix))


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				return reverse_column(transpose(a_						)						)
				# OR.. transpose(reverse_row(matrix))


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			[list(a_						) for x in zip(*a_						)]
				return matrix


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			matrix[::-1]
				return matrix


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			[x[::-1] for x in matrix]
				return matrix


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				for i in matrix:
								print(*a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    make_matrix()
					print('\norigin:\n')
					print_matrix(matrix)
					print('\nrotate 90 counterclockwise:\n')
					print_matrix(rotate_aa(matrix))

					SCREAMING_SNAKE_CASE							:Tuple										=    make_matrix()
					print('\norigin:\n')
					print_matrix(matrix)
					print('\nrotate 180:\n')
					print_matrix(rotate_aaa(matrix))

					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    make_matrix()
					print('\norigin:\n')
					print_matrix(matrix)
					print('\nrotate 270 counterclockwise:\n')
					print_matrix(rotate_aaa(matrix))
