

'''simple docstring'''


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf

					from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      Dict  ):



								'''simple docstring'''



								for model_result in results.values():
										for batch_size, sequence_length in zip(model_result['''bs''']			,			model_result['''ss''']  ):
												snake_case__						:       str       						=  model_result['''result'''][batch_size][sequence_length]
												self.assertIsNotNone(snake_case_  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  '''sshleifer/tiny-gpt2'''
								snake_case__						:       Optional[Any]       						=  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]			,			training=snake_case_			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			eager_mode=snake_case_			,			multi_process=snake_case_			,			)
								snake_case__						:       Any       						=  TensorFlowBenchmark(snake_case_  )
								snake_case__						:       List[str]       						=  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  '''sgugger/tiny-distilbert-classification'''
								snake_case__						:       str       						=  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]			,			training=snake_case_			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			multi_process=snake_case_			,			only_pretrain_model=snake_case_			,			)
								snake_case__						:       Optional[int]       						=  TensorFlowBenchmark(snake_case_  )
								snake_case__						:       Dict       						=  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  '''sshleifer/tiny-gpt2'''
								snake_case__						:       str       						=  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]			,			training=snake_case_			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			multi_process=snake_case_			,			)
								snake_case__						:       Optional[int]       						=  TensorFlowBenchmark(snake_case_  )
								snake_case__						:       Any       						=  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  '''sshleifer/tiny-gpt2'''
								snake_case__						:       str       						=  AutoConfig.from_pretrained(snake_case_  )
								snake_case__						:       List[str]       						=  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]			,			training=snake_case_			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			eager_mode=snake_case_			,			multi_process=snake_case_			,			)
								snake_case__						:       List[str]       						=  TensorFlowBenchmark(snake_case_			,			[config]  )
								snake_case__						:       Optional[Any]       						=  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  '''sshleifer/tiny-gpt2'''
								snake_case__						:       List[str]       						=  AutoConfig.from_pretrained(snake_case_  )
								snake_case__						:       int       						=  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]			,			training=snake_case_			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			multi_process=snake_case_			,			)
								snake_case__						:       List[str]       						=  TensorFlowBenchmark(snake_case_			,			[config]  )
								snake_case__						:       Optional[Any]       						=  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  '''sshleifer/tiny-gpt2'''
								snake_case__						:       Dict       						=  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]			,			training=snake_case_			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			multi_process=snake_case_			,			)
								snake_case__						:       Tuple       						=  TensorFlowBenchmark(snake_case_  )
								snake_case__						:       Tuple       						=  benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result  )
								self.check_results_dict_not_empty(results.memory_train_result  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  '''sshleifer/tiny-gpt2'''
								snake_case__						:       List[Any]       						=  AutoConfig.from_pretrained(snake_case_  )
								snake_case__						:       Dict       						=  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]			,			training=snake_case_			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			multi_process=snake_case_			,			)
								snake_case__						:       int       						=  TensorFlowBenchmark(snake_case_			,			[config]  )
								snake_case__						:       Tuple       						=  benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result  )
								self.check_results_dict_not_empty(results.memory_train_result  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  '''patrickvonplaten/t5-tiny-random'''
								snake_case__						:       List[Any]       						=  AutoConfig.from_pretrained(snake_case_  )
								snake_case__						:       List[Any]       						=  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]			,			training=snake_case_			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			multi_process=snake_case_			,			)
								snake_case__						:       Optional[Any]       						=  TensorFlowBenchmark(snake_case_			,			configs=[config]  )
								snake_case__						:       List[Any]       						=  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )







						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''  )  ) == 0			,			'''Cannot do xla on CPU.'''  )
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  '''sshleifer/tiny-gpt2'''
								snake_case__						:       List[str]       						=  TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]			,			training=snake_case_			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			use_xla=snake_case_			,			multi_process=snake_case_			,			)
								snake_case__						:       Optional[Any]       						=  TensorFlowBenchmark(snake_case_  )
								snake_case__						:       Optional[int]       						=  benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  '''sshleifer/tiny-gpt2'''
								with tempfile.TemporaryDirectory() as tmp_dir:
										snake_case__						:       int       						=  TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]			,			inference=snake_case_			,			save_to_csv=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			inference_time_csv_file=os.path.join(snake_case_			,			'''inf_time.csv'''  )			,			inference_memory_csv_file=os.path.join(snake_case_			,			'''inf_mem.csv'''  )			,			env_info_csv_file=os.path.join(snake_case_			,			'''env.csv'''  )			,			multi_process=snake_case_			,			)
										snake_case__						:       Tuple       						=  TensorFlowBenchmark(snake_case_  )
										benchmark.run()
										self.assertTrue(Path(os.path.join(snake_case_			,			'''inf_time.csv'''  )  ).exists()  )
										self.assertTrue(Path(os.path.join(snake_case_			,			'''inf_mem.csv'''  )  ).exists()  )
										self.assertTrue(Path(os.path.join(snake_case_			,			'''env.csv'''  )  ).exists()  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  '''sshleifer/tiny-gpt2'''

								def _check_summary_is_not_empty(snake_case_					:      int  ):
										self.assertTrue(hasattr(snake_case_			,			'''sequential'''  )  )
										self.assertTrue(hasattr(snake_case_			,			'''cumulative'''  )  )
										self.assertTrue(hasattr(snake_case_			,			'''current'''  )  )
										self.assertTrue(hasattr(snake_case_			,			'''total'''  )  )

								with tempfile.TemporaryDirectory() as tmp_dir:
										snake_case__						:       List[str]       						=  TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]			,			inference=snake_case_			,			sequence_lengths=[8]			,			batch_sizes=[1]			,			log_filename=os.path.join(snake_case_			,			'''log.txt'''  )			,			log_print=snake_case_			,			trace_memory_line_by_line=snake_case_			,			eager_mode=snake_case_			,			multi_process=snake_case_			,			)
										snake_case__						:       Any       						=  TensorFlowBenchmark(snake_case_  )
										snake_case__						:       Optional[int]       						=  benchmark.run()
										_check_summary_is_not_empty(result.inference_summary  )
										self.assertTrue(Path(os.path.join(snake_case_			,			'''log.txt'''  )  ).exists()  )






'''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 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCAmelCase     )]     )
		snake_case__						:       Dict       						=  np.array(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()   ,							__lowerCAmelCase     )     )   ,							x.transpose()     )   ,							__lowerCAmelCase     )
		return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]     )




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  (1, 2, 1)
		snake_case__						:       Union[str, Any]       						=  (1, 1, 0, 7)
		snake_case__						:       List[str]       						=  SARIMAX(
		    __lowerCAmelCase   ,							exog=__lowerCAmelCase   ,							order=__lowerCAmelCase   ,							seasonal_order=__lowerCAmelCase     )
		snake_case__						:       str       						=  model.fit(disp=__lowerCAmelCase   ,							maxiter=6_00   ,							method='''nm'''     )
		snake_case__						:       List[str]       						=  model_fit.predict(1   ,							len(__lowerCAmelCase     )   ,							exog=[test_match]     )
		return result[0]




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  SVR(kernel='''rbf'''   ,							C=1   ,							gamma=0.1   ,							epsilon=0.1     )
		regressor.fit(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Optional[Any]       						=  regressor.predict(__lowerCAmelCase     )
		return y_pred[0]




def 	_a       (				__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		train_user.sort()
		snake_case__						:       int       						=  np.percentile(__lowerCAmelCase   ,							25     )
		snake_case__						:       List[str]       						=  np.percentile(__lowerCAmelCase   ,							75     )
		snake_case__						:       Dict       						=  qa - qa
		snake_case__						:       Optional[int]       						=  qa - (iqr * 0.1)
		return low_lim




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      float     ):

		"""simple docstring"""

		snake_case__						:       int       						=  0
		snake_case__						:       List[Any]       						=  0
		for i in list_vote:
				if i > actual_result:
						snake_case__						:       Optional[Any]       						=  not_safe + 1
				else:
						if abs(abs(__lowerCAmelCase     ) - abs(__lowerCAmelCase     )     ) <= 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)
					lowerCAmelCase__						:       Optional[int]										=						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
					lowerCAmelCase__						:       List[Any]										=						pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					lowerCAmelCase__						:       Union[str, Any]										=						Normalizer().fit_transform(data_input_df.values)
					# split data
					lowerCAmelCase__						:       Union[str, Any]										=						normalize_df[:, 2].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 0].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					lowerCAmelCase__						:       Dict										=						normalize_df[:, [1, 2]].tolist()
					lowerCAmelCase__						:       Union[str, Any]										=						x[: len(x) - 1]
					lowerCAmelCase__						:       List[str]										=						x[len(x) - 1 :]

					# for linear regression & sarimax
					lowerCAmelCase__						:       Union[str, Any]										=						total_date[: len(total_date) - 1]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[: len(total_user) - 1]
					lowerCAmelCase__						:       List[Any]										=						total_match[: len(total_match) - 1]

					lowerCAmelCase__						:       Optional[int]										=						total_date[len(total_date) - 1 :]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[len(total_user) - 1 :]
					lowerCAmelCase__						:       List[Any]										=						total_match[len(total_match) - 1 :]

					# voting system with forecasting
					lowerCAmelCase__						:       Optional[Any]										=						[
					    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
					lowerCAmelCase__						:       Optional[int]										=						"""""" if data_safety_checker(res_vote, tst_user) else """not """
					print("""Today's data is {not_str}safe.""")



