



"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def __init__(       self:							Union[str, Any]						,     snake_case:							Optional[Any]						,     snake_case:							List[str]=7						,     snake_case:							List[str]=3						,     snake_case:							Tuple=18						,     snake_case:							Optional[int]=30						,     snake_case:							Optional[Any]=400						,     snake_case:							Tuple=True						,     snake_case:							int=None						,     snake_case:							Optional[int]=True						,     snake_case:							int=None						,     )   ->	Dict:
											snake_case_ :Optional[Any]						=			size if size is not None else {"""shortest_edge""": 20}
											snake_case_ :int						=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											snake_case_ :List[Any]						=			parent
											snake_case_ :Any						=			batch_size
											snake_case_ :List[Any]						=			num_channels
											snake_case_ :Any						=			image_size
											snake_case_ :Tuple						=			min_resolution
											snake_case_ :Optional[Any]						=			max_resolution
											snake_case_ :Tuple						=			do_resize
											snake_case_ :Any						=			size
											snake_case_ :List[str]						=			do_center_crop
											snake_case_ :Dict						=			crop_size






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[str]:
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}

@require_torch
@require_vision
class 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			Any						=     MobileNetVaImageProcessor if is_vision_available() else None






					def    lowerCAmelCase_				(       self:							str						)   ->	Dict:
											snake_case_ :Any						=			MobileNetVaImageProcessingTester(self						)






					@property
					def    lowerCAmelCase_				(       self:							Dict						)   ->	str:
											return self.image_processor_tester.prepare_image_processor_dict()






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[Any]:
											snake_case_ :Tuple						=			self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(snake_case						,     """do_resize"""						)						)
											self.assertTrue(hasattr(snake_case						,     """size"""						)						)
											self.assertTrue(hasattr(snake_case						,     """do_center_crop"""						)						)
											self.assertTrue(hasattr(snake_case						,     """crop_size"""						)						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Tuple:
											snake_case_ :Optional[int]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size						,     {"""shortest_edge""": 20}						)
											self.assertEqual(image_processor.crop_size						,     {"""height""": 18, """width""": 18}						)

											snake_case_ :Union[str, Any]						=			self.image_processing_class.from_dict(self.image_processor_dict						,     size=42						,     crop_size=84						)
											self.assertEqual(image_processor.size						,     {"""shortest_edge""": 42}						)
											self.assertEqual(image_processor.crop_size						,     {"""height""": 84, """width""": 84}						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Tuple:
											pass






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	List[Any]:
											# Initialize image_processing
											snake_case_ :str						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case_ :int						=			prepare_image_inputs(self.image_processor_tester						,     equal_resolution=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,     Image.Image						)

											# Test not batched input
											snake_case_ :int						=			image_processing(image_inputs[0]						,     return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,     (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )						,     )

											# Test batched
											snake_case_ :List[Any]						=			image_processing(snake_case						,     return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )						,     )






					def    lowerCAmelCase_				(       self:							Any						)   ->	Tuple:
											# Initialize image_processing
											snake_case_ :List[str]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case_ :Tuple						=			prepare_image_inputs(self.image_processor_tester						,     equal_resolution=snake_case						,     numpify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,     np.ndarray						)

											# Test not batched input
											snake_case_ :Tuple						=			image_processing(image_inputs[0]						,     return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,     (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )						,     )

											# Test batched
											snake_case_ :int						=			image_processing(snake_case						,     return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )						,     )






					def    lowerCAmelCase_				(       self:							str						)   ->	List[str]:
											# Initialize image_processing
											snake_case_ :Union[str, Any]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case_ :Optional[int]						=			prepare_image_inputs(self.image_processor_tester						,     equal_resolution=snake_case						,     torchify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,     torch.Tensor						)

											# Test not batched input
											snake_case_ :List[Any]						=			image_processing(image_inputs[0]						,     return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,     (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )						,     )

											# Test batched
											snake_case_ :int						=			image_processing(snake_case						,     return_tensors="""pt"""						).pixel_values
											self.assertEqual(
											    encoded_images.shape						,     (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )						,     )





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

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

						__a      =				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")
						__a      =				model.fit(
						    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
						)
						__a      =				model.predict(x_test)
