
"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__magic_name__       =			logging.get_logger(__name__)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		if isinstance(a_  ,				np.ndarray				):
				return list(tensor.shape				)

		__SCREAMING_SNAKE_CASE            = tf.shape(a_				)

		if tensor.shape == tf.TensorShape(a_				):
				return dynamic

		__SCREAMING_SNAKE_CASE            = tensor.shape.as_list()

		return [dynamic[i] if s is None else s for i, s in enumerate(a_				)]



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_ = None  ,				UpperCamelCase_ = None				):
		return tf.nn.softmax(logits=logits + 1e-9  ,				axis=a_  ,				name=a_				)



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=1e-5  ,				UpperCamelCase_=-1				):
		# This is a very simplified functional layernorm, designed to duplicate
		# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
		# models in Transformers.

		if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a_  ,				a_				):
				raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis."""				)

		# Get mean and variance on the axis to be normalized
		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = tf.nn.moments(a_  ,				axes=[axis]  ,				keepdims=a_				)

		if axis != -1:
				# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
				# on every dimension except axis
				__SCREAMING_SNAKE_CASE            = [1] * inputs.shape.rank
				__SCREAMING_SNAKE_CASE            = shape_list(a_				)[axis]
				__SCREAMING_SNAKE_CASE            = tf.reshape(a_  ,				a_				)
				__SCREAMING_SNAKE_CASE            = tf.reshape(a_  ,				a_				)

		# Compute layer normalization using the batch_normalization
		# function.
		__SCREAMING_SNAKE_CASE            = tf.nn.batch_normalization(
		    a_  ,				a_  ,				a_  ,				offset=a_  ,				scale=a_  ,				variance_epsilon=a_  ,				)
		return outputs



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_=0  ,				UpperCamelCase_=-1				):
		# Replicates the behavior of torch.flatten in TF

		# If end_dim or start_dim is negative, count them from the end
		if end_dim < 0:
				end_dim += input.shape.rank
		if start_dim < 0:
				start_dim += input.shape.rank

		if start_dim == end_dim:
				return input

		__SCREAMING_SNAKE_CASE            = tf.shape(a_				)
		__SCREAMING_SNAKE_CASE            = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
		__SCREAMING_SNAKE_CASE            = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]  ,				axis=0				)
		return tf.reshape(a_  ,				a_				)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		if not isinstance(a_  ,				tf.Tensor				):
				__SCREAMING_SNAKE_CASE            = tf.convert_to_tensor(a_				)  # Catches stray NumPy inputs
		if encoder_attention_mask.shape.rank == 3:
				__SCREAMING_SNAKE_CASE            = encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.shape.rank == 2:
				__SCREAMING_SNAKE_CASE            = encoder_attention_mask[:, None, None, :]
		# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
		# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
		# /transformer/transformer_layers.py#L270
		# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
		# encoder_extended_attention_mask.transpose(-1, -2))
		__SCREAMING_SNAKE_CASE            = (
		    tf.cast(1  ,				encoder_attention_mask.dtype				) - encoder_extended_attention_mask
		) * encoder_extended_attention_mask.dtype.min

		return encoder_extended_attention_mask



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_ = "input_ids"				):
		tf.debugging.assert_less(
		    a_  ,				tf.cast(a_  ,				dtype=tensor.dtype				)  ,				message=(
		        f"The maximum value of {tensor_name} ({tf.math.reduce_max(a_				)}) must be smaller than the embedding "
		        f"layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
		    )  ,				)



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = 6_4512
		# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
		# because in that case even chunking the array would not make the saving
		# possible.
		__SCREAMING_SNAKE_CASE            = [x for x in data if len(a_				) > HDF5_OBJECT_HEADER_LIMIT]

		# Expecting this to never be true.
		if bad_attributes:
				raise RuntimeError(
				    """The following attributes cannot be saved to HDF5 file because """
				    f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
				    f"bytes: {bad_attributes}"				)

		__SCREAMING_SNAKE_CASE            = np.asarray(a_				)

		__SCREAMING_SNAKE_CASE            = 1
		__SCREAMING_SNAKE_CASE            = np.array_split(a_  ,				a_				)

		# This will never loop forever thanks to the test above.
		while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data				):
				num_chunks += 1
				__SCREAMING_SNAKE_CASE            = np.array_split(a_  ,				a_				)

		if num_chunks > 1:
				for chunk_id, chunk_data in enumerate(a_				):
						__SCREAMING_SNAKE_CASE            = chunk_data
		else:
				__SCREAMING_SNAKE_CASE            = data



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		if name in group.attrs:
				__SCREAMING_SNAKE_CASE            = [n.decode("""utf8"""				) if hasattr(a_  ,				"""decode"""				) else n for n in group.attrs[name]]
		else:
				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = 0
				while "%s%d" % (name, chunk_id) in group.attrs:
						data.extend(
						    [n.decode("""utf8"""				) if hasattr(a_  ,				"""decode"""				) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]				)
						chunk_id += 1
		return data



def 						_lowerCAmelCase				(				UpperCamelCase_				):

		def _expand_single_ad_tensor(UpperCamelCase_				):
				if isinstance(a_  ,				tf.Tensor				) and t.shape.rank == 1:
						return tf.expand_dims(a_  ,				axis=-1				)
				return t

		return tf.nest.map_structure(_expand_single_ad_tensor  ,				a_				)












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 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_							,     a_):
							snake_case_				=							np.array([[1, item, train_mtch[i]] for i, item in enumerate(a_)])
							snake_case_				=							np.array(a_)
							snake_case_				=							np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()							,     a_))							,     x.transpose())							,     a_)
							return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							(1, 2, 1)
							snake_case_				=							(1, 1, 0, 7)
							snake_case_				=							SARIMAX(
							    a_							,     exog=a_							,     order=a_							,     seasonal_order=a_)
							snake_case_				=							model.fit(disp=a_							,     maxiter=6_00							,     method='nm')
							snake_case_				=							model_fit.predict(1							,     len(a_)							,     exog=[test_match])
							return result[0]





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							SVR(kernel='rbf'							,     C=1							,     gamma=0.1							,     epsilon=0.1)
							regressor.fit(a_							,     a_)
							snake_case_				=							regressor.predict(a_)
							return y_pred[0]





def 						__UpperCAmelCase			(     a_):
							train_user.sort()
							snake_case_				=							np.percentile(a_							,     25)
							snake_case_				=							np.percentile(a_							,     75)
							snake_case_				=							qa - qa
							snake_case_				=							qa - (iqr * 0.1)
							return low_lim





def 						__UpperCAmelCase			(     a_							,     a_):
							snake_case_				=							0
							snake_case_				=							0
							for i in list_vote:
														if i > actual_result:
																					snake_case_				=							not_safe + 1
														else:
																					if abs(abs(a_) - abs(a_)) <= 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  							=					[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 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.")





