






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

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

  _UpperCamelCase					      =  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""")
  _UpperCamelCase					      =  model.fit(
      x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
  )
  _UpperCamelCase					      =  model.predict(x_test)











"""simple docstring"""







import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:







  class  lowerCamelCase__ :






      @staticmethod
      def     _UpperCamelCase							(			*A     ,**A ):
            pass

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	hashlib.mda(image.tobytes()							)
      return m.hexdigest()[:10]

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	np.array(_snake_case							)
      UpperCAmelCase          =	npimg.shape
      return {"hash": hashimage(_snake_case							), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      dict(
        (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
    SCREAMING_SNAKE_CASE		=      dict(
        (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	MaskGenerationPipeline(model=A     ,image_processor=A )
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]






    def     _UpperCamelCase							(			self     ,A     ,A ):
          pass






    @require_tf
    @unittest.skip("""Image segmentation not implemented in TF""" )
    def     _UpperCamelCase							(			self ):
          pass






    @slow
    @require_torch
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	pipeline("""mask-generation"""     ,model="""facebook/sam-vit-huge""" )

          UpperCAmelCase          =	image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""     ,points_per_batch=256 )

          # Shortening by hashing
          UpperCAmelCase          =	[]
          for i, o in enumerate(outputs["""masks"""] ):
                new_outupt += [{"mask": mask_to_test_readable(A ), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
                  {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
                  {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
                  {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
                  {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
                  {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9967},
                  {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
                  {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9909},
                  {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9879},
                  {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9834},
                  {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9716},
                  {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9612},
                  {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9599},
                  {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9552},
                  {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9532},
                  {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9516},
                  {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9499},
                  {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9483},
                  {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9464},
                  {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
                  {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
                  {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9408},
                  {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9335},
                  {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9326},
                  {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9262},
                  {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8999},
                  {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8986},
                  {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8984},
                  {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8873},
                  {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8871}
              ]     ,)
          # fmt: on






    @require_torch
    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""facebook/sam-vit-huge"""
          UpperCAmelCase          =	pipeline("""mask-generation"""     ,model=A )

          UpperCAmelCase          =	image_segmenter(
              """http://images.cocodataset.org/val2017/000000039769.jpg"""     ,pred_iou_thresh=1     ,points_per_batch=256 )

          # Shortening by hashing
          UpperCAmelCase          =	[]
          for i, o in enumerate(outputs["""masks"""] ):
                new_outupt += [{"mask": mask_to_test_readable(A ), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
                  {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0210},
                  {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
                  {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
                  {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
              ]     ,)



