


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

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 		__a				(    unittest.TestCase      ):
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      0
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'						)
									self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									with tempfile.TemporaryDirectory() as tmpdirname:
																_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						) / 'preprocessor_config.json'
																_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						) / 'config.json'
																json.dump(
																    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}      ,   open(_SCREAMING_SNAKE_CASE      ,   'w'						)      ,   )
																json.dump({'model_type': 'clip'}      ,   open(_SCREAMING_SNAKE_CASE      ,   'w'						)						)

																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									with tempfile.TemporaryDirectory() as tmpdirname:
																_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						) / 'preprocessor_config.json'
																_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						) / 'config.json'
																json.dump(
																    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}      ,   open(_SCREAMING_SNAKE_CASE      ,   'w'						)      ,   )
																json.dump({'model_type': 'clip'}      ,   open(_SCREAMING_SNAKE_CASE      ,   'w'						)						)

																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									with tempfile.TemporaryDirectory() as tmpdirname:
																_UpperCAmelCase					=      CLIPConfig()

																# Create a dummy config file with image_proceesor_type
																_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						) / 'preprocessor_config.json'
																_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						) / 'config.json'
																json.dump(
																    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}      ,   open(_SCREAMING_SNAKE_CASE      ,   'w'						)      ,   )
																json.dump({'model_type': 'clip'}      ,   open(_SCREAMING_SNAKE_CASE      ,   'w'						)						)

																# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE						).to_dict()

																config_dict.pop('image_processor_type'						)
																_UpperCAmelCase					=      CLIPImageProcessor(**_SCREAMING_SNAKE_CASE						)

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

																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE						)

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

									self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									with tempfile.TemporaryDirectory() as tmpdirname:
																_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						) / 'preprocessor_config.json'
																json.dump(
																    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}      ,   open(_SCREAMING_SNAKE_CASE      ,   'w'						)      ,   )

																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									with self.assertRaisesRegex(
									    _SCREAMING_SNAKE_CASE      ,   'clip-base is not a local folder and is not a valid model identifier'						):
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained('clip-base'						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									with self.assertRaisesRegex(
									    _SCREAMING_SNAKE_CASE      ,   R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'						):
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE      ,   revision='aaaaaa'						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									with self.assertRaisesRegex(
									    _SCREAMING_SNAKE_CASE      ,   'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'      ,   ):
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									with self.assertRaises(_SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'						)
									# If remote code is disabled, we can't load this config.
									with self.assertRaises(_SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(
																    'hf-internal-testing/test_dynamic_image_processor'      ,   trust_remote_code=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(
									    'hf-internal-testing/test_dynamic_image_processor'      ,   trust_remote_code=_SCREAMING_SNAKE_CASE						)
									self.assertEqual(image_processor.__class__.__name__      ,   'NewImageProcessor'						)

									# Test image processor can be reloaded.
									with tempfile.TemporaryDirectory() as tmp_dir:
																image_processor.save_pretrained(_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE      ,   trust_remote_code=_SCREAMING_SNAKE_CASE						)
									self.assertEqual(reloaded_image_processor.__class__.__name__      ,   'NewImageProcessor'						)
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									try:
																AutoConfig.register('custom'      ,   _SCREAMING_SNAKE_CASE						)
																AutoImageProcessor.register(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																# Trying to register something existing in the Transformers library will raise an error
																with self.assertRaises(_SCREAMING_SNAKE_CASE						):
																							AutoImageProcessor.register(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						) / 'preprocessor_config.json'
																							_UpperCAmelCase					=      Path(_SCREAMING_SNAKE_CASE						) / 'config.json'
																							json.dump(
																							    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}      ,   open(_SCREAMING_SNAKE_CASE      ,   'w'						)      ,   )
																							json.dump({'model_type': 'clip'}      ,   open(_SCREAMING_SNAKE_CASE      ,   'w'						)						)

																							_UpperCAmelCase					=      CustomImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE						)

																# Now that the config is registered, it can be used as any other config with the auto-API
																with tempfile.TemporaryDirectory() as tmp_dir:
																							image_processor.save_pretrained(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(_SCREAMING_SNAKE_CASE						)
																							self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

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






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									class 		__a				(    UpperCAmelCase      ):
											_a     :  int           =     True

									try:
																AutoConfig.register('custom'      ,   _SCREAMING_SNAKE_CASE						)
																AutoImageProcessor.register(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																# If remote code is not set, the default is to use local
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'						)
																self.assertEqual(image_processor.__class__.__name__      ,   'NewImageProcessor'						)
																self.assertTrue(image_processor.is_local						)

																# If remote code is disabled, we load the local one.
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(
																    'hf-internal-testing/test_dynamic_image_processor'      ,   trust_remote_code=_SCREAMING_SNAKE_CASE						)
																self.assertEqual(image_processor.__class__.__name__      ,   'NewImageProcessor'						)
																self.assertTrue(image_processor.is_local						)

																# If remote is enabled, we load from the Hub
																_UpperCAmelCase					=      AutoImageProcessor.from_pretrained(
																    'hf-internal-testing/test_dynamic_image_processor'      ,   trust_remote_code=_SCREAMING_SNAKE_CASE						)
																self.assertEqual(image_processor.__class__.__name__      ,   'NewImageProcessor'						)
																self.assertTrue(not hasattr(_SCREAMING_SNAKE_CASE      ,   'is_local'						)						)

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




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__":
		lowerCAmelCase__  :Dict             =							pd.read_csv('''sample_data.csv''', header=None)
		lowerCAmelCase__  :int             =							df.shape[:1][0]
		# If you're using some other dataset input the target column
		lowerCAmelCase__  :Union[str, Any]             =							df.iloc[:, 1:2]
		lowerCAmelCase__  :Optional[int]             =							actual_data.values.reshape(len_data, 1)
		lowerCAmelCase__  :Tuple             =							MinMaxScaler().fit_transform(actual_data)
		lowerCAmelCase__  :str             =							1_0
		lowerCAmelCase__  :Optional[Any]             =							5
		lowerCAmelCase__  :List[str]             =							2_0
		lowerCAmelCase__  :Any             =							len_data - periods * look_back
		lowerCAmelCase__  :Union[str, Any]             =							actual_data[:division]
		lowerCAmelCase__  :Tuple             =							actual_data[division - look_back :]
		lowerCAmelCase__      ,  lowerCAmelCase__  :Optional[int]             =							[], []
		lowerCAmelCase__      ,  lowerCAmelCase__  :str             =							[], []

		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])
		lowerCAmelCase__  :Optional[Any]             =							np.array(train_x)
		lowerCAmelCase__  :Any             =							np.array(test_x)
		lowerCAmelCase__  :Dict             =							np.array([list(i.ravel()) for i in train_y])
		lowerCAmelCase__  :Tuple             =							np.array([list(i.ravel()) for i in test_y])

		lowerCAmelCase__  :Optional[int]             =							Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss='''mean_squared_error''', optimizer='''adam''')
		lowerCAmelCase__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
