"""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__			=    1_0
    UpperCAmelCase__			=    5
    UpperCAmelCase__			=    2_0
    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(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""")
    UpperCAmelCase__			=    model.fit(
        x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
    )
    UpperCAmelCase__			=    model.predict(x_test)

"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	int  				=				'dpt'


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	Optional[Any]=768 ,					__lowerCAmelCase						:	str=12 ,					__lowerCAmelCase						:	Optional[int]=12 ,					__lowerCAmelCase						:	Dict=3072 ,					__lowerCAmelCase						:	List[Any]="gelu" ,					__lowerCAmelCase						:	Tuple=0.0 ,					__lowerCAmelCase						:	str=0.0 ,					__lowerCAmelCase						:	List[Any]=0.02 ,					__lowerCAmelCase						:	Optional[Any]=1e-1_2 ,					__lowerCAmelCase						:	List[str]=384 ,					__lowerCAmelCase						:	Tuple=16 ,					__lowerCAmelCase						:	List[str]=3 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Optional[Any]=True ,					__lowerCAmelCase						:	Optional[Any]=[2, 5, 8, 11] ,					__lowerCAmelCase						:	Any="project" ,					__lowerCAmelCase						:	Dict=[4, 2, 1, 0.5] ,					__lowerCAmelCase						:	int=[96, 192, 384, 768] ,					__lowerCAmelCase						:	Dict=256 ,					__lowerCAmelCase						:	Optional[Any]=-1 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Union[str, Any]=True ,					__lowerCAmelCase						:	List[Any]=0.4 ,					__lowerCAmelCase						:	List[str]=255 ,					__lowerCAmelCase						:	List[Any]=0.1 ,					__lowerCAmelCase						:	Optional[int]=[1, 1024, 24, 24] ,					__lowerCAmelCase						:	List[Any]=[0, 1] ,					__lowerCAmelCase						:	Dict=None ,					**__lowerCAmelCase						:	str ,					):
      super().__init__(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   hidden_size
      _UpperCAmelCase    			=   is_hybrid

      if self.is_hybrid:
        if backbone_config is None:
          logger.info("""Initializing the config with a `BiT` backbone."""       )
          _UpperCAmelCase    			=   {
              """global_padding""": """same""",
              """layer_type""": """bottleneck""",
              """depths""": [3, 4, 9],
              """out_features""": ["""stage1""", """stage2""", """stage3"""],
              """embedding_dynamic_padding""": True,
          }
          _UpperCAmelCase    			=   BitConfig(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          logger.info("""Initializing the config with a `BiT` backbone."""       )
          _UpperCAmelCase    			=   BitConfig(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          _UpperCAmelCase    			=   backbone_config
        else:
          raise ValueError(
              f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''       )

        _UpperCAmelCase    			=   backbone_featmap_shape
        _UpperCAmelCase    			=   neck_ignore_stages

        if readout_type != "project":
          raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""       )
      else:
        _UpperCAmelCase    			=   None
        _UpperCAmelCase    			=   None
        _UpperCAmelCase    			=   []

      _UpperCAmelCase    			=   num_hidden_layers
      _UpperCAmelCase    			=   num_attention_heads
      _UpperCAmelCase    			=   intermediate_size
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   layer_norm_eps
      _UpperCAmelCase    			=   image_size
      _UpperCAmelCase    			=   patch_size
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   qkv_bias
      _UpperCAmelCase    			=   backbone_out_indices
      if readout_type not in ["ignore", "add", "project"]:
        raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""       )
      _UpperCAmelCase    			=   readout_type
      _UpperCAmelCase    			=   reassemble_factors
      _UpperCAmelCase    			=   neck_hidden_sizes
      _UpperCAmelCase    			=   fusion_hidden_size
      _UpperCAmelCase    			=   head_in_index
      _UpperCAmelCase    			=   use_batch_norm_in_fusion_residual
      # auxiliary head attributes (semantic segmentation)
      _UpperCAmelCase    			=   use_auxiliary_head
      _UpperCAmelCase    			=   auxiliary_loss_weight
      _UpperCAmelCase    			=   semantic_loss_ignore_index
      _UpperCAmelCase    			=   semantic_classifier_dropout


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   copy.deepcopy(self.__dict__       )

      if output["backbone_config"] is not None:
        _UpperCAmelCase    			=   self.backbone_config.to_dict()

      _UpperCAmelCase    			=   self.__class__.model_type
      return output
