
"""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


_lowercase							=				logging.get_logger(__name__)

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

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			int          =   """layoutlmv3"""
							def __init__(     self  ,    _lowerCamelCase=50_265  ,    _lowerCamelCase=768  ,    _lowerCamelCase=12  ,    _lowerCamelCase=12  ,    _lowerCamelCase=3_072  ,    _lowerCamelCase="gelu"  ,    _lowerCamelCase=0.1  ,    _lowerCamelCase=0.1  ,    _lowerCamelCase=512  ,    _lowerCamelCase=2  ,    _lowerCamelCase=0.02  ,    _lowerCamelCase=1e-5  ,    _lowerCamelCase=1  ,    _lowerCamelCase=0  ,    _lowerCamelCase=2  ,    _lowerCamelCase=1_024  ,    _lowerCamelCase=128  ,    _lowerCamelCase=128  ,    _lowerCamelCase=True  ,    _lowerCamelCase=32  ,    _lowerCamelCase=128  ,    _lowerCamelCase=64  ,    _lowerCamelCase=256  ,    _lowerCamelCase=True  ,    _lowerCamelCase=True  ,    _lowerCamelCase=True  ,    _lowerCamelCase=224  ,    _lowerCamelCase=3  ,    _lowerCamelCase=16  ,    _lowerCamelCase=None  ,    **_lowerCamelCase  ,    )			->							List[str]:




										'''simple docstring'''
										super().__init__(
										    vocab_size=_lowerCamelCase  ,    hidden_size=_lowerCamelCase  ,    num_hidden_layers=_lowerCamelCase  ,    num_attention_heads=_lowerCamelCase  ,    intermediate_size=_lowerCamelCase  ,    hidden_act=_lowerCamelCase  ,    hidden_dropout_prob=_lowerCamelCase  ,    attention_probs_dropout_prob=_lowerCamelCase  ,    max_position_embeddings=_lowerCamelCase  ,    type_vocab_size=_lowerCamelCase  ,    initializer_range=_lowerCamelCase  ,    layer_norm_eps=_lowerCamelCase  ,    pad_token_id=_lowerCamelCase  ,    bos_token_id=_lowerCamelCase  ,    eos_token_id=_lowerCamelCase  ,    **_lowerCamelCase  ,    )
										__lowercase       =       max_ad_position_embeddings
										__lowercase       =       coordinate_size
										__lowercase       =       shape_size
										__lowercase       =       has_relative_attention_bias
										__lowercase       =       rel_pos_bins
										__lowercase       =       max_rel_pos
										__lowercase       =       has_spatial_attention_bias
										__lowercase       =       rel_ad_pos_bins
										__lowercase       =       max_rel_ad_pos
										__lowercase       =       text_embed
										__lowercase       =       visual_embed
										__lowercase       =       input_size
										__lowercase       =       num_channels
										__lowercase       =       patch_size
										__lowercase       =       classifier_dropout





class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[Any]          =   version.parse("""1.12"""       )
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Mapping[str, Mapping[int, str]]:




										'''simple docstring'''
										# 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   SCREAMING_SNAKE_CASE						(     self       )			->							float:




										'''simple docstring'''
										return 1e-5
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return 12





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = False  ,    _lowerCamelCase = None  ,    _lowerCamelCase = 3  ,    _lowerCamelCase = 40  ,    _lowerCamelCase = 40  ,    )			->							Mapping[str, Any]:




										'''simple docstring'''
										setattr(processor.image_processor  ,    "apply_ocr"  ,    _lowerCamelCase       )

										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										__lowercase       =       compute_effective_axis_dimension(
										    _lowerCamelCase  ,    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
										__lowercase       =       processor.tokenizer.num_special_tokens_to_add(_lowerCamelCase       )
										__lowercase       =       compute_effective_axis_dimension(
										    _lowerCamelCase  ,    fixed_dimension=OnnxConfig.default_fixed_sequence  ,    num_token_to_add=_lowerCamelCase       )
										# Generate dummy inputs according to compute batch and sequence
										__lowercase       =       [[" ".join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

										# Generate dummy bounding boxes
										__lowercase       =       [[[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)
										__lowercase       =       self._generate_dummy_images(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )

										__lowercase       =       dict(
										    processor(
										        _lowerCamelCase  ,    text=_lowerCamelCase  ,    boxes=_lowerCamelCase  ,    return_tensors=_lowerCamelCase  ,    )       )

										return inputs







"""simple docstring"""






import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__      )]      )
			__lowercase       =       np.array(__magic_name__      )
			__lowercase       =       np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,   __magic_name__      )      )			,   x.transpose()      )			,   __magic_name__      )
			return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       (1, 2, 1)
			__lowercase       =       (1, 1, 0, 7)
			__lowercase       =       SARIMAX(
			    __magic_name__			,   exog=__magic_name__			,   order=__magic_name__			,   seasonal_order=__magic_name__      )
			__lowercase       =       model.fit(disp=__magic_name__			,   maxiter=6_0_0			,   method="nm"      )
			__lowercase       =       model_fit.predict(1			,   len(__magic_name__      )			,   exog=[test_match]      )
			return result[0]



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       SVR(kernel="rbf"			,   C=1			,   gamma=0.1			,   epsilon=0.1      )
			regressor.fit(__magic_name__			,   __magic_name__      )
			__lowercase       =       regressor.predict(__magic_name__      )
			return y_pred[0]



def      lowerCAmelCase__			(		__magic_name__      )						->float:
			train_user.sort()
			__lowercase       =       np.percentile(__magic_name__			,   2_5      )
			__lowercase       =       np.percentile(__magic_name__			,   7_5      )
			__lowercase       =       qa - qa
			__lowercase       =       qa - (iqr * 0.1)
			return low_lim



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->bool:
			__lowercase       =       0
			__lowercase       =       0
			for i in list_vote:
						if i > actual_result:
									__lowercase       =       not_safe + 1
						else:
									if abs(abs(__magic_name__      ) - abs(__magic_name__      )      ) <= 0.1:
												safe += 1
									else:
												not_safe += 1
			return safe > not_safe


if __name__ == "__main__":
					# data_input_df = pd.read_csv("ex_data.csv", header=None)
					_lowercase							=				[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
					_lowercase							=				pd.DataFrame(
					    data_input, columns=['''total_user''', '''total_even''', '''days''']
					)
					_lowercase							=				Normalizer().fit_transform(data_input_df.values)
					# split data
					_lowercase							=				normalize_df[:, 2].tolist()
					_lowercase							=				normalize_df[:, 0].tolist()
					_lowercase							=				normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					_lowercase							=				normalize_df[:, [1, 2]].tolist()
					_lowercase							=				x[: len(x) - 1]
					_lowercase							=				x[len(x) - 1 :]

					# for linear regression & sarimax
					_lowercase							=				total_date[: len(total_date) - 1]
					_lowercase							=				total_user[: len(total_user) - 1]
					_lowercase							=				total_match[: len(total_match) - 1]

					_lowercase							=				total_date[len(total_date) - 1 :]
					_lowercase							=				total_user[len(total_user) - 1 :]
					_lowercase							=				total_match[len(total_match) - 1 :]

					# voting system with forecasting
					_lowercase							=				[
					    linear_regression_prediction(
					        trn_date, trn_user, trn_match, tst_date, tst_match
					    ),
					    sarimax_predictor(trn_user, trn_match, tst_match),
					    support_vector_regressor(x_train, x_test, trn_user),
					]

					# check the safety of today's data
					_lowercase							=				'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
					print('''Today\'s data is {not_str}safe.''')





