from __future__ import annotations

from decimal import Decimal

from numpy import array





def  _lowerCamelCase					(   a_				: list[list[float]]):
			lowerCamelCase						:Tuple						  =     Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(a_) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
						# Calculate the determinant of the matrix
						lowerCamelCase						:Optional[int]						  =     float(
						    d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
						if determinant == 0:
									raise ValueError('''This matrix has no inverse.''')

						# Creates a copy of the matrix with swapped positions of the elements
						lowerCamelCase						:List[Any]						  =     [[0.0, 0.0], [0.0, 0.0]]
						lowerCamelCase  ,    lowerCamelCase						:Union[str, Any]						  =     matrix[1][1], matrix[0][0]
						lowerCamelCase  ,    lowerCamelCase						:Optional[int]						  =     -matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(a_)) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(a_) == 3
			    and len(matrix[0]) == 3
			    and len(matrix[1]) == 3
			    and len(matrix[2]) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						lowerCamelCase						:List[Any]						  =     float(
						    (
						        (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
						        + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
						        + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
						    )
						    - (
						        (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
						        + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
						        + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
						    ))
						if determinant == 0:
									raise ValueError('''This matrix has no inverse.''')

						# Creating cofactor matrix
						lowerCamelCase						:Any						  =     [
						    [d(0.0), d(0.0), d(0.0)],
						    [d(0.0), d(0.0), d(0.0)],
						    [d(0.0), d(0.0), d(0.0)],
						]
						lowerCamelCase						:int						  =     (d(matrix[1][1]) * d(matrix[2][2])) - (
						    d(matrix[1][2]) * d(matrix[2][1])
						)
						lowerCamelCase						:Any						  =     -(
						    (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
						)
						lowerCamelCase						:List[Any]						  =     (d(matrix[1][0]) * d(matrix[2][1])) - (
						    d(matrix[1][1]) * d(matrix[2][0])
						)
						lowerCamelCase						:List[Any]						  =     -(
						    (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
						)
						lowerCamelCase						:Optional[int]						  =     (d(matrix[0][0]) * d(matrix[2][2])) - (
						    d(matrix[0][2]) * d(matrix[2][0])
						)
						lowerCamelCase						:Optional[Any]						  =     -(
						    (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
						)
						lowerCamelCase						:Dict						  =     (d(matrix[0][1]) * d(matrix[1][2])) - (
						    d(matrix[0][2]) * d(matrix[1][1])
						)
						lowerCamelCase						:Tuple						  =     -(
						    (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
						)
						lowerCamelCase						:List[str]						  =     (d(matrix[0][0]) * d(matrix[1][1])) - (
						    d(matrix[0][1]) * d(matrix[1][0])
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						lowerCamelCase						:int						  =     array(a_)
						for i in range(3):
									for j in range(3):
												lowerCamelCase						:List[Any]						  =     cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						lowerCamelCase						:Optional[Any]						  =     array(a_)
						for i in range(3):
									for j in range(3):
												inverse_matrix[i][j] /= d(a_)

        # Calculate the inverse of the matrix
						return [[float(d(a_)) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''')




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A__           =							{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2TokenizerFast"""]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2FeatureExtractor"""]
			A__           =							["""LayoutLMv2ImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """LayoutLMv2ForQuestionAnswering""",
			    """LayoutLMv2ForSequenceClassification""",
			    """LayoutLMv2ForTokenClassification""",
			    """LayoutLMv2Layer""",
			    """LayoutLMv2Model""",
			    """LayoutLMv2PreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
			from .processing_layoutlmva import LayoutLMvaProcessor
			from .tokenization_layoutlmva import LayoutLMvaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaLayer,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)
else:
			import sys

			A__           =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



