"""simple docstring"""






from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


__lowerCAmelCase       :			str   	=logging.get_logger(__name__)





class 					_A		(			lowerCAmelCase      ):

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		):
                  lowercase								=      [label.strip() for label in labels.split(""","""		) if label.strip()]
            return labels







      def __call__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            if len(__lowerCAmelCase		) == 0 or len(__lowerCAmelCase		) == 0:
                  raise ValueError("""You must include at least one label and at least one sequence."""		)
            if hypothesis_template.format(labels[0]		) == hypothesis_template:
                  raise ValueError(
                      (
                          """The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. """
                          """Make sure the passed template includes formatting syntax such as {{}} where the label should go."""
                      ).format(__lowerCAmelCase		)		)

            if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		):
                  lowercase								=      [sequences]

            lowercase								=      []
            for sequence in sequences:
                  sequence_pairs.extend([[sequence, hypothesis_template.format(__lowerCAmelCase		)] for label in labels]		)

            return sequence_pairs, sequences






@add_end_docstrings(lowerCAmelCase      )
class 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase=ZeroShotClassificationArgumentHandler()						,			*__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      args_parser
            super().__init__(*__lowerCAmelCase						,			**__lowerCAmelCase		)
            if self.entailment_id == -1:
                  logger.warning(
                      """Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to """
                      """-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."""		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            for label, ind in self.model.config.labelaid.items():
                  if label.lower().startswith("""entail"""		):
                        return ind
            return -1

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=TruncationStrategy.ONLY_FIRST						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.framework
            if self.tokenizer.pad_token is None:
                  # Override for tokenizers not supporting padding
                  logger.error(
                      """Tokenizer was not supporting padding necessary for zero-shot, attempting to use """
                      """ `pad_token=eos_token`"""		)
                  lowercase								=      self.tokenizer.eos_token
            try:
                  lowercase								=      self.tokenizer(
                      __lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase						,			return_tensors=__lowerCAmelCase						,			padding=__lowerCAmelCase						,			truncation=__lowerCAmelCase						,			)
            except Exception as e:
                  if "too short" in str(__lowerCAmelCase		):
                        # tokenizers might yell that we want to truncate
                        # to a value that is not even reached by the input.
                        # In that case we don't want to truncate.
                        # It seems there's not a really better way to catch that
                        # exception.

                        lowercase								=      self.tokenizer(
                            __lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase						,			return_tensors=__lowerCAmelCase						,			padding=__lowerCAmelCase						,			truncation=TruncationStrategy.DO_NOT_TRUNCATE						,			)
                  else:
                        raise e

            return inputs

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            if kwargs.get("""multi_class"""						,			__lowerCAmelCase		) is not None:
                  lowercase								=      kwargs["""multi_class"""]
                  logger.warning(
                      """The `multi_class` argument has been deprecated and renamed to `multi_label`. """
                      """`multi_class` will be removed in a future version of Transformers."""		)
            lowercase								=      {}
            if "candidate_labels" in kwargs:
                  lowercase								=      self._args_parser._parse_labels(kwargs["""candidate_labels"""]		)
            if "hypothesis_template" in kwargs:
                  lowercase								=      kwargs["""hypothesis_template"""]

            lowercase								=      {}
            if "multi_label" in kwargs:
                  lowercase								=      kwargs["""multi_label"""]
            return preprocess_params, {}, postprocess_params

      def __call__(		self						,			__lowerCAmelCase						,			*__lowerCAmelCase						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if len(__lowerCAmelCase		) == 0:
                  pass
            elif len(__lowerCAmelCase		) == 1 and "candidate_labels" not in kwargs:
                  lowercase								=      args[0]
            else:
                  raise ValueError(f'Unable to understand extra arguments {args}'		)

            return super().__call__(__lowerCAmelCase						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=None						,			__lowerCAmelCase="This example is {}."		):







            """simple docstring"""



            lowercase				,				lowercase								=      self._args_parser(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)

            for i, (candidate_label, sequence_pair) in enumerate(zip(__lowerCAmelCase						,			__lowerCAmelCase		)		):
                  lowercase								=      self._parse_and_tokenize([sequence_pair]		)

                  yield {
                      "candidate_label": candidate_label,
                      "sequence": sequences[0],
                      "is_last": i == len(__lowerCAmelCase		) - 1,
                      **model_input,
                  }

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      inputs["""candidate_label"""]
            lowercase								=      inputs["""sequence"""]
            lowercase								=      {k: inputs[k] for k in self.tokenizer.model_input_names}
            lowercase								=      self.model(**__lowerCAmelCase		)

            lowercase								=      {
                """candidate_label""": candidate_label,
                """sequence""": sequence,
                """is_last""": inputs["""is_last"""],
                **outputs,
            }
            return model_outputs







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=False		):







            """simple docstring"""



            lowercase								=      [outputs["""candidate_label"""] for outputs in model_outputs]
            lowercase								=      [outputs["""sequence"""] for outputs in model_outputs]
            lowercase								=      np.concatenate([output["""logits"""].numpy() for output in model_outputs]		)
            lowercase								=      logits.shape[0]
            lowercase								=      len(__lowerCAmelCase		)
            lowercase								=      N // n
            lowercase								=      logits.reshape((num_sequences, n, -1)		)

            if multi_label or len(__lowerCAmelCase		) == 1:
                  # softmax over the entailment vs. contradiction dim for each label independently
                  lowercase								=      self.entailment_id
                  lowercase								=      -1 if entailment_id == 0 else 0
                  lowercase								=      reshaped_outputs[..., [contradiction_id, entailment_id]]
                  lowercase								=      np.exp(__lowerCAmelCase		) / np.exp(__lowerCAmelCase		).sum(-1						,			keepdims=__lowerCAmelCase		)
                  lowercase								=      scores[..., 1]
            else:
                  # softmax the "entailment" logits over all candidate labels
                  lowercase								=      reshaped_outputs[..., self.entailment_id]
                  lowercase								=      np.exp(__lowerCAmelCase		) / np.exp(__lowerCAmelCase		).sum(-1						,			keepdims=__lowerCAmelCase		)

            lowercase								=      list(reversed(scores[0].argsort()		)		)
            return {
                "sequence": sequences[0],
                "labels": [candidate_labels[i] for i in top_inds],
                "scores": scores[0, top_inds].tolist(),
            }




"""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__":
       __lowerCAmelCase       :			int   	=pd.read_csv("""sample_data.csv""", header=None)
       __lowerCAmelCase       :			Optional[Any]   	=df.shape[:1][0]
       # If you're using some other dataset input the target column
       __lowerCAmelCase       :			Optional[int]   	=df.iloc[:, 1:2]
       __lowerCAmelCase       :			List[str]   	=actual_data.values.reshape(len_data, 1)
       __lowerCAmelCase       :			int   	=MinMaxScaler().fit_transform(actual_data)
       __lowerCAmelCase       :			List[Any]   	=1_0
       __lowerCAmelCase       :			int   	=5
       __lowerCAmelCase       :			str   	=2_0
       __lowerCAmelCase       :			Union[str, Any]   	=len_data - periods * look_back
       __lowerCAmelCase       :			Dict   	=actual_data[:division]
       __lowerCAmelCase       :			List[str]   	=actual_data[division - look_back :]
       __lowerCAmelCase	,	__lowerCAmelCase       :			List[str]   	=[], []
       __lowerCAmelCase	,	__lowerCAmelCase       :			Optional[int]   	=[], []

       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       :			int   	=np.array(train_x)
       __lowerCAmelCase       :			List[str]   	=np.array(test_x)
       __lowerCAmelCase       :			Dict   	=np.array([list(i.ravel()) for i in train_y])
       __lowerCAmelCase       :			str   	=np.array([list(i.ravel()) for i in test_y])

       __lowerCAmelCase       :			Optional[Any]   	=Sequential()
       model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
       model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
       model.add(Dense(forward_days))
       model.compile(loss="""mean_squared_error""", optimizer="""adam""")
       __lowerCAmelCase       :			Optional[int]   	=model.fit(
           x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
       )
       __lowerCAmelCase       :			Dict   	=model.predict(x_test)



