






import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available



class 				_A			(     _UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							str				= ['image_processor', 'tokenizer']
    lowerCamelCase	:							int				= 'OwlViTImageProcessor'
    lowerCamelCase	:							Dict				= ('CLIPTokenizer', 'CLIPTokenizerFast')



    def __init__(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     Any=None ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=None ,     **__SCREAMING_SNAKE_CASE      :     Tuple      )      ->							int:
           __UpperCAmelCase     				=None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead.""" ,     __SCREAMING_SNAKE_CASE ,     )
                  __UpperCAmelCase     				=kwargs.pop("""feature_extractor"""      )

           __UpperCAmelCase     				=image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError("""You need to specify an `image_processor`."""      )
           if tokenizer is None:
                  raise ValueError("""You need to specify a `tokenizer`."""      )

           super().__init__(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )



    def __call__(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     List[str]=None ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=None ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=None ,     __SCREAMING_SNAKE_CASE      :     Any="max_length" ,     __SCREAMING_SNAKE_CASE      :     List[Any]="np" ,     **__SCREAMING_SNAKE_CASE      :     Union[str, Any]      )      ->							List[str]:







           if text is None and query_images is None and images is None:
                  raise ValueError(
                      """You have to specify at least one text or query image or image. All three cannot be none."""      )

           if text is not None:
                  if isinstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) or (isinstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) and not isinstance(text[0] ,     __SCREAMING_SNAKE_CASE      )):
                         __UpperCAmelCase     				=[self.tokenizer(__SCREAMING_SNAKE_CASE ,     padding=__SCREAMING_SNAKE_CASE ,     return_tensors=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )]

                  elif isinstance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) and isinstance(text[0] ,     __SCREAMING_SNAKE_CASE      ):
                         __UpperCAmelCase     				=[]

                         # Maximum number of queries across batch
                         __UpperCAmelCase     				=max([len(__SCREAMING_SNAKE_CASE      ) for t in text]      )

                         # Pad all batch samples to max number of text queries
                         for t in text:
                                if len(__SCREAMING_SNAKE_CASE      ) != max_num_queries:
                                       __UpperCAmelCase     				=t + [""" """] * (max_num_queries - len(__SCREAMING_SNAKE_CASE      ))

                                __UpperCAmelCase     				=self.tokenizer(__SCREAMING_SNAKE_CASE ,     padding=__SCREAMING_SNAKE_CASE ,     return_tensors=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )
                                encodings.append(__SCREAMING_SNAKE_CASE      )
                  else:
                         raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""      )

                  if return_tensors == "np":
                         __UpperCAmelCase     				=np.concatenate([encoding["""input_ids"""] for encoding in encodings] ,     axis=0      )
                         __UpperCAmelCase     				=np.concatenate([encoding["""attention_mask"""] for encoding in encodings] ,     axis=0      )

                  elif return_tensors == "jax" and is_flax_available():
                         import jax.numpy as jnp

                         __UpperCAmelCase     				=jnp.concatenate([encoding["""input_ids"""] for encoding in encodings] ,     axis=0      )
                         __UpperCAmelCase     				=jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings] ,     axis=0      )

                  elif return_tensors == "pt" and is_torch_available():
                         import torch

                         __UpperCAmelCase     				=torch.cat([encoding["""input_ids"""] for encoding in encodings] ,     dim=0      )
                         __UpperCAmelCase     				=torch.cat([encoding["""attention_mask"""] for encoding in encodings] ,     dim=0      )

                  elif return_tensors == "tf" and is_tf_available():
                         import tensorflow as tf

                         __UpperCAmelCase     				=tf.stack([encoding["""input_ids"""] for encoding in encodings] ,     axis=0      )
                         __UpperCAmelCase     				=tf.stack([encoding["""attention_mask"""] for encoding in encodings] ,     axis=0      )

                  else:
                         raise ValueError("""Target return tensor type could not be returned"""      )

                  __UpperCAmelCase     				=BatchEncoding()
                  __UpperCAmelCase     				=input_ids
                  __UpperCAmelCase     				=attention_mask

           if query_images is not None:
                  __UpperCAmelCase     				=BatchEncoding()
                  __UpperCAmelCase     				=self.image_processor(
                      __SCREAMING_SNAKE_CASE ,     return_tensors=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ).pixel_values
                  __UpperCAmelCase     				=query_pixel_values

           if images is not None:
                  __UpperCAmelCase     				=self.image_processor(__SCREAMING_SNAKE_CASE ,     return_tensors=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )

           if text is not None and images is not None:
                  __UpperCAmelCase     				=image_features.pixel_values
                  return encoding
           elif query_images is not None and images is not None:
                  __UpperCAmelCase     				=image_features.pixel_values
                  return encoding
           elif text is not None or query_images is not None:
                  return encoding
           else:
                  return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE      ) ,     tensor_type=__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Tuple ,     *__SCREAMING_SNAKE_CASE      :     Optional[Any] ,     **__SCREAMING_SNAKE_CASE      :     int      )      ->							Tuple:
           return self.image_processor.post_process(*__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     List[Any] ,     *__SCREAMING_SNAKE_CASE      :     Dict ,     **__SCREAMING_SNAKE_CASE      :     Optional[int]      )      ->							Optional[Any]:
           return self.image_processor.post_process_object_detection(*__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any] ,     *__SCREAMING_SNAKE_CASE      :     Dict ,     **__SCREAMING_SNAKE_CASE      :     Tuple      )      ->							Optional[int]:
           return self.image_processor.post_process_image_guided_detection(*__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Tuple ,     *__SCREAMING_SNAKE_CASE      :     Tuple ,     **__SCREAMING_SNAKE_CASE      :     Optional[Any]      )      ->							Optional[Any]:
           return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Optional[Any] ,     *__SCREAMING_SNAKE_CASE      :     str ,     **__SCREAMING_SNAKE_CASE      :     Any      )      ->							Optional[int]:
           return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    @property
    def _a							(				self      :     List[str]      )      ->							int:
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,     __SCREAMING_SNAKE_CASE ,     )
           return self.image_processor_class



    @property
    def _a							(				self      :     Dict      )      ->							Optional[int]:
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,     __SCREAMING_SNAKE_CASE ,     )
           return self.image_processor










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






