
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


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

lowercase__							:							Tuple													=     {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
				import detectrona






class     SCREAMING_SNAKE_CASE__      (							A__      ):

							"""simple docstring"""






							_snake_case       						=					'layoutlmv2'



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_=30522       ,		SCREAMING_SNAKE_CASE_=768       ,		SCREAMING_SNAKE_CASE_=12       ,		SCREAMING_SNAKE_CASE_=12       ,		SCREAMING_SNAKE_CASE_=3072       ,		SCREAMING_SNAKE_CASE_="gelu"       ,		SCREAMING_SNAKE_CASE_=0.1       ,		SCREAMING_SNAKE_CASE_=0.1       ,		SCREAMING_SNAKE_CASE_=512       ,		SCREAMING_SNAKE_CASE_=2       ,		SCREAMING_SNAKE_CASE_=0.0_2       ,		SCREAMING_SNAKE_CASE_=1E-12       ,		SCREAMING_SNAKE_CASE_=0       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=128       ,		SCREAMING_SNAKE_CASE_=32       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=256       ,		SCREAMING_SNAKE_CASE_=64       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=[7, 7, 256]       ,		SCREAMING_SNAKE_CASE_=128       ,		SCREAMING_SNAKE_CASE_=128       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=True       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=None       ,		**SCREAMING_SNAKE_CASE_       ,		)->				Union[str, Any]:





													'''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       ,		**_A       ,		)
													__UpperCamelCase       					=     max_ad_position_embeddings
													__UpperCamelCase       					=     max_rel_pos
													__UpperCamelCase       					=     rel_pos_bins
													__UpperCamelCase       					=     fast_qkv
													__UpperCamelCase       					=     max_rel_ad_pos
													__UpperCamelCase       					=     rel_ad_pos_bins
													__UpperCamelCase       					=     convert_sync_batchnorm
													__UpperCamelCase       					=     image_feature_pool_shape
													__UpperCamelCase       					=     coordinate_size
													__UpperCamelCase       					=     shape_size
													__UpperCamelCase       					=     has_relative_attention_bias
													__UpperCamelCase       					=     has_spatial_attention_bias
													__UpperCamelCase       					=     has_visual_segment_embedding
													__UpperCamelCase       					=     (
													    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
													)



							@classmethod
							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													return {
													    "MODEL.MASK_ON": True,
													    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
													    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
													    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
													    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
													    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
													    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
													    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
													    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
													    "MODEL.POST_NMS_TOPK_TEST": 1000,
													    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
													    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
													    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
													    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
													    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
													    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
													    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
													    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
													    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
													    "MODEL.RESNETS.DEPTH": 101,
													    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
													    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
													    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
													    "MODEL.RESNETS.NUM_GROUPS": 32,
													    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
													    "MODEL.RESNETS.STRIDE_IN_1X1": False,
													}



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     detectrona.config.get_cfg()
													for k, v in self.detectrona_config_args.items():
																			__UpperCamelCase       					=     k.split('''.'''     )
																			__UpperCamelCase       					=     detectrona_config
																			for attribute in attributes[:-1]:
																									__UpperCamelCase       					=     getattr(_A       ,		_A     )
																			setattr(_A       ,		attributes[-1]       ,		_A     )

													return detectrona_config






from typing import Any

import numpy as np




def 		A_	(							snake_case							:				np.ndarray	) ->      bool:



						'''simple docstring'''

						assert np.shape(snake_case	)[0] == np.shape(snake_case	)[1]

						# If matrix not symmetric, exit right away.
						if np.allclose(snake_case	, matrix.T	) is False:
												return False

						# Get eigenvalues and eignevectors for a symmetric matrix.
						__UpperCamelCase					,							__UpperCamelCase       					=     np.linalg.eigh(snake_case	)

						# Check sign of all eigenvalues.
						# np.all returns a value of type np.bool_
						return bool(np.all(eigen_values > 0	)	)




def 		A_	(							snake_case							:				int	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     np.random.randn(snake_case	, snake_case	)
						__UpperCamelCase       					=     np.dot(snake_case	, random_matrix.T	)
						assert _is_matrix_spd(snake_case	)
						return spd_matrix




def 		A_	(							snake_case							:				np.ndarray	, snake_case							:				np.ndarray	, snake_case							:				int = 1000	, snake_case							:				float = 1e-8	, ) ->      Any:



						'''simple docstring'''

						assert np.shape(snake_case	)[0] == np.shape(snake_case	)[1]
						assert np.shape(snake_case	)[0] == np.shape(snake_case	)[0]
						assert _is_matrix_spd(snake_case	)

						# Initialize solution guess, residual, search direction.
						__UpperCamelCase       					=     np.zeros((np.shape(snake_case	)[0], 1)	)
						__UpperCamelCase       					=     np.copy(snake_case	)
						__UpperCamelCase       					=     np.copy(snake_case	)

						# Set initial errors in solution guess and residual.
						__UpperCamelCase       					=     1e9
						__UpperCamelCase       					=     1e9
						__UpperCamelCase       					=     1e9

						# Set iteration counter to threshold number of iterations.
						__UpperCamelCase       					=     0

						while error > tol:
												# Save this value so we only calculate the matrix-vector product once.
												__UpperCamelCase       					=     np.dot(snake_case	, snake_case	)

												# The main algorithm.

												# Update search direction magnitude.
												__UpperCamelCase       					=     np.dot(ra.T	, snake_case	) / np.dot(pa.T	, snake_case	)
												# Update solution guess.
												__UpperCamelCase       					=     xa + alpha * pa
												# Calculate new residual.
												__UpperCamelCase       					=     ra - alpha * w
												# Calculate new Krylov subspace scale.
												__UpperCamelCase       					=     np.dot(r.T	, snake_case	) / np.dot(ra.T	, snake_case	)
												# Calculate new A conjuage search direction.
												__UpperCamelCase       					=     r + beta * pa

												# Calculate errors.
												__UpperCamelCase       					=     np.linalg.norm(r - ra	)
												__UpperCamelCase       					=     np.linalg.norm(x - xa	)
												__UpperCamelCase       					=     np.maximum(snake_case	, snake_case	)

												# Update variables.
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)
												__UpperCamelCase       					=     np.copy(snake_case	)

												# Update number of iterations.
												iterations += 1
												if iterations > max_iterations:
																		break

						return x




def 		A_	(							) ->      None:



						'''simple docstring'''

						__UpperCamelCase       					=     3
						__UpperCamelCase       					=     _create_spd_matrix(snake_case	)
						__UpperCamelCase       					=     np.random.randn(snake_case	, 1	)
						__UpperCamelCase       					=     np.dot(snake_case	, snake_case	)

						# Numpy solution.
						__UpperCamelCase       					=     np.linalg.solve(snake_case	, snake_case	)

						# Our implementation.
						__UpperCamelCase       					=     conjugate_gradient(snake_case	, snake_case	)

						# Ensure both solutions are close to x_true (and therefore one another).
						assert np.linalg.norm(x_numpy - x_true	) <= 1e-6
						assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1e-6


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				test_conjugate_gradient()




