





'''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     ):

							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     str   )					->       Any:







													"""simple docstring"""
													for model_result in results.values():
																			for batch_size, sequence_length in zip(model_result['''bs''']       ,     model_result['''ss''']   ):
																									UpperCAmelCase_			:      Optional[Any]     						=  model_result['''result'''][batch_size][sequence_length]
																									self.assertIsNotNone(__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      str     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     eager_mode=__magic_name__       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      Dict     						=  TensorFlowBenchmark(__magic_name__   )
													UpperCAmelCase_			:      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     UpperCAmelCase__	(							self							:     List[Any]   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  '''sgugger/tiny-distilbert-classification'''
													UpperCAmelCase_			:      int     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=__magic_name__       ,     only_pretrain_model=__magic_name__       ,     )
													UpperCAmelCase_			:      Any     						=  TensorFlowBenchmark(__magic_name__   )
													UpperCAmelCase_			:      int     						=  benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result   )
													self.check_results_dict_not_empty(results.memory_inference_result   )

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       List[Any]:







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

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      List[str]     						=  AutoConfig.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     eager_mode=__magic_name__       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      str     						=  TensorFlowBenchmark(__magic_name__       ,     [config]   )
													UpperCAmelCase_			:      Dict     						=  benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result   )
													self.check_results_dict_not_empty(results.memory_inference_result   )

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Dict:







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

							def     UpperCAmelCase__	(							self							:     Any   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      Dict     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      Any     						=  TensorFlowBenchmark(__magic_name__   )
													UpperCAmelCase_			:      str     						=  benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result   )
													self.check_results_dict_not_empty(results.memory_train_result   )

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      Dict     						=  AutoConfig.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      List[str]     						=  TensorFlowBenchmark(__magic_name__       ,     [config]   )
													UpperCAmelCase_			:      str     						=  benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result   )
													self.check_results_dict_not_empty(results.memory_train_result   )

							def     UpperCAmelCase__	(							self							:     Dict   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  '''patrickvonplaten/t5-tiny-random'''
													UpperCAmelCase_			:      Union[str, Any]     						=  AutoConfig.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      Union[str, Any]     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      List[Any]     						=  TensorFlowBenchmark(__magic_name__       ,     configs=[config]   )
													UpperCAmelCase_			:      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     UpperCAmelCase__	(							self							:     int   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  '''sshleifer/tiny-gpt2'''
													UpperCAmelCase_			:      Tuple     						=  TensorFlowBenchmarkArguments(
													    models=[MODEL_ID]       ,     training=__magic_name__       ,     inference=__magic_name__       ,     sequence_lengths=[8]       ,     batch_sizes=[1]       ,     use_xla=__magic_name__       ,     multi_process=__magic_name__       ,     )
													UpperCAmelCase_			:      Dict     						=  TensorFlowBenchmark(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result   )
													self.check_results_dict_not_empty(results.memory_inference_result   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       Dict:







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



							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  '''sshleifer/tiny-gpt2'''

													def _check_summary_is_not_empty(__magic_name__							:     str   ):
																			self.assertTrue(hasattr(__magic_name__       ,     '''sequential'''   )   )
																			self.assertTrue(hasattr(__magic_name__       ,     '''cumulative'''   )   )
																			self.assertTrue(hasattr(__magic_name__       ,     '''current'''   )   )
																			self.assertTrue(hasattr(__magic_name__       ,     '''total'''   )   )

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












'''simple docstring'''







import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
					snake_case_     :       List[Any]						=  pd.read_csv("sample_data.csv", header=None)
					snake_case_     :       Optional[Any]						=  df.shape[:1][0]
					# If you're using some other dataset input the target column
					snake_case_     :       Any						=  df.iloc[:, 1:2]
					snake_case_     :       str						=  actual_data.values.reshape(len_data, 1)
					snake_case_     :       Optional[Any]						=  MinMaxScaler().fit_transform(actual_data)
					snake_case_     :       List[str]						=  10
					snake_case_     :       Any						=  5
					snake_case_     :       Any						=  20
					snake_case_     :       Tuple						=  len_data - periods * look_back
					snake_case_     :       str						=  actual_data[:division]
					snake_case_     :       Optional[int]						=  actual_data[division - look_back :]
					snake_case_			,snake_case_     :       Any						=  [], []
					snake_case_			,snake_case_     :       Union[str, Any]						=  [], []

					for i in range(0, len(train_data) - forward_days - look_back + 1):
										train_x.append(train_data[i : i + look_back])
										train_y.append(train_data[i + look_back : i + look_back + forward_days])
					for i in range(0, len(test_data) - forward_days - look_back + 1):
										test_x.append(test_data[i : i + look_back])
										test_y.append(test_data[i + look_back : i + look_back + forward_days])
					snake_case_     :       Any						=  np.array(train_x)
					snake_case_     :       Optional[Any]						=  np.array(test_x)
					snake_case_     :       Optional[Any]						=  np.array([list(i.ravel()) for i in train_y])
					snake_case_     :       List[str]						=  np.array([list(i.ravel()) for i in test_y])

					snake_case_     :       List[Any]						=  Sequential()
					model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
					model.add(LSTM(64, input_shape=(1_28, 1)))
					model.add(Dense(forward_days))
					model.compile(loss="mean_squared_error", optimizer="adam")
					snake_case_     :       Dict						=  model.fit(
					    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
					)
					snake_case_     :       Optional[Any]						=  model.predict(x_test)





