
"""simple docstring"""






import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_lowercase							=				get_tests_dir('''fixtures''')
_lowercase							=				get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
_lowercase							=				get_tests_dir('''fixtures/dummy-config.json''')

class 					__a     ( unittest.TestCase       ):



							'''simple docstring'''







							def   SCREAMING_SNAKE_CASE						(     self       )			->							Any:




										'''simple docstring'''
										__lowercase       =       0
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"       )
										self.assertIsInstance(_lowerCamelCase  ,    _lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       AutoFeatureExtractor.from_pretrained(_lowerCamelCase       )
										self.assertIsInstance(_lowerCamelCase  ,    _lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowercase       =       WavaVecaConfig()

													# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
													__lowercase       =       AutoFeatureExtractor.from_pretrained(_lowerCamelCase       ).to_dict()

													config_dict.pop("feature_extractor_type"       )
													__lowercase       =       WavaVecaFeatureExtractor(**_lowerCamelCase       )

													# save in new folder
													model_config.save_pretrained(_lowerCamelCase       )
													config.save_pretrained(_lowerCamelCase       )

													__lowercase       =       AutoFeatureExtractor.from_pretrained(_lowerCamelCase       )

													# make sure private variable is not incorrectly saved
													__lowercase       =       json.loads(config.to_json_string()       )
													self.assertTrue("_processor_class" not in dict_as_saved       )

										self.assertIsInstance(_lowerCamelCase  ,    _lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										__lowercase       =       AutoFeatureExtractor.from_pretrained(_lowerCamelCase       )
										self.assertIsInstance(_lowerCamelCase  ,    _lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Union[str, Any]:




										'''simple docstring'''
										with self.assertRaisesRegex(
										    _lowerCamelCase  ,    "bert-base is not a local folder and is not a valid model identifier"       ):
													__lowercase       =       AutoFeatureExtractor.from_pretrained("bert-base"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[int]:




										'''simple docstring'''
										with self.assertRaisesRegex(
										    _lowerCamelCase  ,    R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"       ):
													__lowercase       =       AutoFeatureExtractor.from_pretrained(_lowerCamelCase  ,    revision="aaaaaa"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										with self.assertRaisesRegex(
										    _lowerCamelCase  ,    "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."  ,    ):
													__lowercase       =       AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[str]:




										'''simple docstring'''
										# If remote code is not set, we will time out when asking whether to load the model.
										with self.assertRaises(_lowerCamelCase       ):
													__lowercase       =       AutoFeatureExtractor.from_pretrained(
													    "hf-internal-testing/test_dynamic_feature_extractor"       )
										# If remote code is disabled, we can't load this config.
										with self.assertRaises(_lowerCamelCase       ):
													__lowercase       =       AutoFeatureExtractor.from_pretrained(
													    "hf-internal-testing/test_dynamic_feature_extractor"  ,    trust_remote_code=_lowerCamelCase       )

										__lowercase       =       AutoFeatureExtractor.from_pretrained(
										    "hf-internal-testing/test_dynamic_feature_extractor"  ,    trust_remote_code=_lowerCamelCase       )
										self.assertEqual(feature_extractor.__class__.__name__  ,    "NewFeatureExtractor"       )

										# Test feature extractor can be reloaded.
										with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(_lowerCamelCase       )
													__lowercase       =       AutoFeatureExtractor.from_pretrained(_lowerCamelCase  ,    trust_remote_code=_lowerCamelCase       )
										self.assertEqual(reloaded_feature_extractor.__class__.__name__  ,    "NewFeatureExtractor"       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										try:
													AutoConfig.register("custom"  ,    _lowerCamelCase       )
													AutoFeatureExtractor.register(_lowerCamelCase  ,    _lowerCamelCase       )
													# Trying to register something existing in the Transformers library will raise an error
													with self.assertRaises(_lowerCamelCase       ):
																AutoFeatureExtractor.register(_lowerCamelCase  ,    _lowerCamelCase       )

													# Now that the config is registered, it can be used as any other config with the auto-API
													__lowercase       =       CustomFeatureExtractor.from_pretrained(_lowerCamelCase       )
													with tempfile.TemporaryDirectory() as tmp_dir:
																feature_extractor.save_pretrained(_lowerCamelCase       )
																__lowercase       =       AutoFeatureExtractor.from_pretrained(_lowerCamelCase       )
																self.assertIsInstance(_lowerCamelCase  ,    _lowerCamelCase       )

										finally:
													if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
													if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''

										class 					__a     ( __a       ):



																	'''simple docstring'''







																	_lowerCamelCase					:			str          =   True

										try:
													AutoConfig.register("custom"  ,    _lowerCamelCase       )
													AutoFeatureExtractor.register(_lowerCamelCase  ,    _lowerCamelCase       )
													# If remote code is not set, the default is to use local
													__lowercase       =       AutoFeatureExtractor.from_pretrained(
													    "hf-internal-testing/test_dynamic_feature_extractor"       )
													self.assertEqual(feature_extractor.__class__.__name__  ,    "NewFeatureExtractor"       )
													self.assertTrue(feature_extractor.is_local       )

													# If remote code is disabled, we load the local one.
													__lowercase       =       AutoFeatureExtractor.from_pretrained(
													    "hf-internal-testing/test_dynamic_feature_extractor"  ,    trust_remote_code=_lowerCamelCase       )
													self.assertEqual(feature_extractor.__class__.__name__  ,    "NewFeatureExtractor"       )
													self.assertTrue(feature_extractor.is_local       )

													# If remote is enabled, we load from the Hub
													__lowercase       =       AutoFeatureExtractor.from_pretrained(
													    "hf-internal-testing/test_dynamic_feature_extractor"  ,    trust_remote_code=_lowerCamelCase       )
													self.assertEqual(feature_extractor.__class__.__name__  ,    "NewFeatureExtractor"       )
													self.assertTrue(not hasattr(_lowerCamelCase  ,    "is_local"       )       )

										finally:
													if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
													if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







"""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__":
					_lowercase							=				pd.read_csv('''sample_data.csv''', header=None)
					_lowercase							=				df.shape[:1][0]
					# If you're using some other dataset input the target column
					_lowercase							=				df.iloc[:, 1:2]
					_lowercase							=				actual_data.values.reshape(len_data, 1)
					_lowercase							=				MinMaxScaler().fit_transform(actual_data)
					_lowercase							=				10
					_lowercase							=				5
					_lowercase							=				20
					_lowercase							=				len_data - periods * look_back
					_lowercase							=				actual_data[:division]
					_lowercase							=				actual_data[division - look_back :]
					_lowercase			,       _lowercase							=				[], []
					_lowercase			,       _lowercase							=				[], []

					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])
					_lowercase							=				np.array(train_x)
					_lowercase							=				np.array(test_x)
					_lowercase							=				np.array([list(i.ravel()) for i in train_y])
					_lowercase							=				np.array([list(i.ravel()) for i in test_y])

					_lowercase							=				Sequential()
					model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
					model.add(LSTM(64, input_shape=(128, 1)))
					model.add(Dense(forward_days))
					model.compile(loss='''mean_squared_error''', optimizer='''adam''')
					_lowercase							=				model.fit(
					    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
					)
					_lowercase							=				model.predict(x_test)





