




from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = "mctct"


    def __init__(			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=8_065  ,      SCREAMING_SNAKE_CASE__	:	Tuple=1_536  ,      SCREAMING_SNAKE_CASE__	:	int=36  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=6_144  ,      SCREAMING_SNAKE_CASE__	:	Any=4  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=384  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=920  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=1e-5  ,      SCREAMING_SNAKE_CASE__	:	str=0.3  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]="relu"  ,      SCREAMING_SNAKE_CASE__	:	Dict=0.02  ,      SCREAMING_SNAKE_CASE__	:	str=0.3  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=0.3  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=1  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=0  ,      SCREAMING_SNAKE_CASE__	:	str=2  ,      SCREAMING_SNAKE_CASE__	:	Dict=1  ,      SCREAMING_SNAKE_CASE__	:	Any=0.3  ,      SCREAMING_SNAKE_CASE__	:	int=1  ,      SCREAMING_SNAKE_CASE__	:	Tuple=(7,)  ,      SCREAMING_SNAKE_CASE__	:	List[str]=(3,)  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=80  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=1  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=None  ,      SCREAMING_SNAKE_CASE__	:	str="sum"  ,      SCREAMING_SNAKE_CASE__	:	Dict=False  ,      **SCREAMING_SNAKE_CASE__	:	str  ,      ) ->			int:
         super().__init__(**SCREAMING_SNAKE_CASE__  ,      pad_token_id=SCREAMING_SNAKE_CASE__  ,      bos_token_id=SCREAMING_SNAKE_CASE__  ,      eos_token_id=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					vocab_size
         lowerCAmelCase__            =					hidden_size
         lowerCAmelCase__            =					num_hidden_layers
         lowerCAmelCase__            =					intermediate_size
         lowerCAmelCase__            =					num_attention_heads
         lowerCAmelCase__            =					attention_head_dim
         lowerCAmelCase__            =					max_position_embeddings
         lowerCAmelCase__            =					layer_norm_eps
         lowerCAmelCase__            =					layerdrop
         lowerCAmelCase__            =					hidden_act
         lowerCAmelCase__            =					initializer_range
         lowerCAmelCase__            =					hidden_dropout_prob
         lowerCAmelCase__            =					attention_probs_dropout_prob
         lowerCAmelCase__            =					pad_token_id
         lowerCAmelCase__            =					bos_token_id
         lowerCAmelCase__            =					eos_token_id
         lowerCAmelCase__            =					conv_glu_dim
         lowerCAmelCase__            =					conv_dropout
         lowerCAmelCase__            =					num_conv_layers
         lowerCAmelCase__            =					input_feat_per_channel
         lowerCAmelCase__            =					input_channels
         lowerCAmelCase__            =					conv_channels
         lowerCAmelCase__            =					ctc_loss_reduction
         lowerCAmelCase__            =					ctc_zero_infinity

         # prevents config testing fail with exporting to json
         lowerCAmelCase__            =					list(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					list(SCREAMING_SNAKE_CASE__  )

         if len(self.conv_kernel  ) != self.num_conv_layers:
              raise ValueError(
                  "Configuration for convolutional module is incorrect. "
                  "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
                  f'but is `len(config.conv_kernel) = {len(self.conv_kernel  )}`, '
                  f'`config.num_conv_layers = {self.num_conv_layers}`.'  )

"""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)



