





import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_snake_case    :       str		       =    datasets.logging.get_logger(__name__)


_snake_case    :       Union[str, Any]		       =    '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

_snake_case    :       List[str]		       =    '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

_snake_case    :       Tuple		       =    '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

_snake_case    :       Union[str, Any]		       =    {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,       _KWARGS_DESCRIPTION       )
class 							_UpperCAmelCase       (       datasets.Metric       ):

  """simple docstring"""


  def 							lowercase						(							self					:						Union[str, Any]	)				->		Optional[Any]:
     return datasets.MetricInfo(
         description=_DESCRIPTION   ,    citation=_CITATION   ,    homepage='https://github.com/google-research/bleurt'   ,    inputs_description=_KWARGS_DESCRIPTION   ,    features=datasets.Features(
             {
                 'predictions': datasets.Value('string'   ,    id='sequence'	),
                 'references': datasets.Value('string'   ,    id='sequence'	),
             }	)   ,    codebase_urls=['https://github.com/google-research/bleurt']   ,    reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']   ,    )






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Tuple	)				->		List[Any]:
     # check that config name specifies a valid BLEURT model
     if self.config_name == "default":
        logger.warning(
            'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
            'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'	)
        __lowerCAmelCase				      =      'bleurt-base-128'

     if self.config_name.lower() in CHECKPOINT_URLS:
        __lowerCAmelCase				      =      self.config_name.lower()

     elif self.config_name.upper() in CHECKPOINT_URLS:
        __lowerCAmelCase				      =      self.config_name.upper()

     else:
        raise KeyError(
            f"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""	)

     # download the model checkpoint specified by self.config_name and set up the scorer
     __lowerCAmelCase				      =      dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]	)
     __lowerCAmelCase				      =      score.BleurtScorer(os.path.join(lowerCAmelCase_   ,    lowerCAmelCase_	)	)






  def 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						List[str]	)				->		List[Any]:
     __lowerCAmelCase				      =      self.scorer.score(references=lowerCAmelCase_   ,    candidates=lowerCAmelCase_	)
     return {"scores": scores}








"""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)




