



"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
						from PIL import Image
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Optional[Any]						=     """Salesforce/blip-image-captioning-base"""
					_A		:			Dict						=     (
					    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
					    """image to caption, and returns a text that contains the description in English."""
					)
					_A		:			int						=     """image_captioner"""
					_A		:			Optional[int]						=     AutoModelForVisionaSeq

					_A		:			List[str]						=     ["""image"""]
					_A		:			Tuple						=     ["""text"""]






					def __init__(       self:							List[Any]						,     *snake_case:							Optional[int]						,     **snake_case:							str						)   ->	Optional[int]:
											requires_backends(self						,     ["""vision"""]						)
											super().__init__(*snake_case						,     **snake_case						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							"Image"						)   ->	Optional[Any]:
											return self.pre_processor(images=snake_case						,     return_tensors="""pt"""						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							List[str]						)   ->	List[str]:
											return self.model.generate(**snake_case						)






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							Tuple						)   ->	Tuple:
											return self.pre_processor.batch_decode(snake_case						,     skip_special_tokens=snake_case						)[0].strip()





"""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)
