
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_    :			Any    =	pd.read_csv("""sample_data.csv""", header=None)
							lowerCamelCase_    :			Union[str, Any]    =	df.shape[:1][0]
							# If you're using some other dataset input the target column
							lowerCamelCase_    :			Optional[int]    =	df.iloc[:, 1:2]
							lowerCamelCase_    :			Tuple    =	actual_data.values.reshape(len_data, 1)
							lowerCamelCase_    :			Optional[int]    =	MinMaxScaler().fit_transform(actual_data)
							lowerCamelCase_    :			List[str]    =	10
							lowerCamelCase_    :			List[str]    =	5
							lowerCamelCase_    :			str    =	20
							lowerCamelCase_    :			Optional[int]    =	len_data - periods * look_back
							lowerCamelCase_    :			str    =	actual_data[:division]
							lowerCamelCase_    :			Union[str, Any]    =	actual_data[division - look_back :]
							lowerCamelCase_ ,							lowerCamelCase_    :			Tuple    =	[], []
							lowerCamelCase_ ,							lowerCamelCase_    :			Optional[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])
							lowerCamelCase_    :			List[Any]    =	np.array(train_x)
							lowerCamelCase_    :			List[Any]    =	np.array(test_x)
							lowerCamelCase_    :			List[str]    =	np.array([list(i.ravel()) for i in train_y])
							lowerCamelCase_    :			int    =	np.array([list(i.ravel()) for i in test_y])

							lowerCamelCase_    :			Union[str, 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""")
							lowerCamelCase_    :			Tuple    =	model.fit(
							    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
							)
							lowerCamelCase_    :			Dict    =	model.predict(x_test)










import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
		import torch

		from transformers import AutoModelForImageClassification

if is_vision_available():
		from transformers import AutoImageProcessor



@require_torch
@require_vision
class 						_UpperCAmelCase      (     unittest.TestCase							):






		@slow
		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
				SCREAMING_SNAKE_CASE_				:int				     =						AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
				model.to(UpperCAmelCase)

				from datasets import load_dataset

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						load_dataset("nielsr/rvlcdip-demo")

				SCREAMING_SNAKE_CASE_				:Dict				     =						dataset["train"][0]["image"].convert("RGB")

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						image_processor(UpperCAmelCase   ,  return_tensors="pt").to(UpperCAmelCase)

				# forward pass
				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model(**UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:List[str]				     =						outputs.logits

				SCREAMING_SNAKE_CASE_				:int				     =						torch.Size((1, 16))
				self.assertEqual(logits.shape   ,  UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.tensor(
				    [-0.4158, -0.4092, -0.4347]   ,  device=UpperCAmelCase   ,  dtype=torch.float   ,  )
				self.assertTrue(torch.allclose(logits[0, :3]   ,  UpperCAmelCase   ,  atol=1E-4))






