





'''simple docstring'''



import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__magic_name__   :						Optional[Any]    =			logging.getLogger(__name__)


class 					__SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self	):
       _snake_case     		=				False

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       if not self.initialized:
         _snake_case     		=				RagRetriever(
             lowerCamelCase						,   question_encoder_tokenizer=lowerCamelCase						,   generator_tokenizer=lowerCamelCase						,   index=lowerCamelCase						,   init_retrieval=lowerCamelCase						,   )
         _snake_case     		=				True

     def 	UpperCamelCase(					self	):
       self.retriever.index.init_index()






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case		,		_snake_case     		=				self.retriever._main_retrieve(lowerCamelCase						,   lowerCamelCase	)
       return doc_ids, retrieved_doc_embeds







class 					__SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None	):
       if index is not None and index.is_initialized() and len(lowerCamelCase	) > 0:
         raise ValueError(
             "When using Ray for distributed fine-tuning, "
             "you'll need to provide the paths instead, "
             "as the dataset and the index are loaded "
             "separately. More info in examples/rag/use_own_knowledge_dataset.py "	)
       super().__init__(
           lowerCamelCase						,   question_encoder_tokenizer=lowerCamelCase						,   generator_tokenizer=lowerCamelCase						,   index=lowerCamelCase						,   init_retrieval=lowerCamelCase						,   )
       _snake_case     		=				retrieval_workers
       if len(self.retrieval_workers	) > 0:
         ray.get(
             [
                 worker.create_rag_retriever.remote(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)
                 for worker in self.retrieval_workers
             ]	)

     def 	UpperCamelCase(					self	):
       logger.info("initializing retrieval"	)

       if len(self.retrieval_workers	) > 0:
         ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]	)
       else:
         # Non-distributed training. Load index into this same process.
         self.index.init_index()

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       if len(self.retrieval_workers	) > 0:
         # Select a random retrieval actor.
         _snake_case     		=				self.retrieval_workers[random.randint(0						,   len(self.retrieval_workers	) - 1	)]
         _snake_case		,		_snake_case     		=				ray.get(random_worker.retrieve.remote(lowerCamelCase						,   lowerCamelCase	)	)
       else:
         _snake_case		,		_snake_case     		=				self._main_retrieve(lowerCamelCase						,   lowerCamelCase	)
       return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase	)

     @classmethod
     def 	UpperCamelCase(					cls						,   lowerCamelCase						,   lowerCamelCase=None						,   **lowerCamelCase	):
       return super(lowerCamelCase						,   cls	).get_tokenizers(lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	)






     @classmethod
     def 	UpperCamelCase(					cls						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None						,   **lowerCamelCase	):
       _snake_case     		=				kwargs.pop("config"						,   lowerCamelCase	) or RagConfig.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)
       _snake_case     		=				RagTokenizer.from_pretrained(lowerCamelCase						,   config=lowerCamelCase	)
       _snake_case     		=				rag_tokenizer.question_encoder
       _snake_case     		=				rag_tokenizer.generator
       if indexed_dataset is not None:
         _snake_case     		=				"custom"
         _snake_case     		=				CustomHFIndex(config.retrieval_vector_size						,   lowerCamelCase	)
       else:
         _snake_case     		=				cls._build_index(lowerCamelCase	)
       return cls(
           lowerCamelCase						,   question_encoder_tokenizer=lowerCamelCase						,   generator_tokenizer=lowerCamelCase						,   retrieval_workers=lowerCamelCase						,   index=lowerCamelCase						,   )









'''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__":
 __magic_name__   :						Union[str, Any]    =			pd.read_csv("""sample_data.csv""", header=None)
 __magic_name__   :						Optional[Any]    =			df.shape[:1][0]
 # If you're using some other dataset input the target column
 __magic_name__   :						str    =			df.iloc[:, 1:2]
 __magic_name__   :						Dict    =			actual_data.values.reshape(len_data, 1)
 __magic_name__   :						Tuple    =			MinMaxScaler().fit_transform(actual_data)
 __magic_name__   :						Union[str, Any]    =			10
 __magic_name__   :						Optional[int]    =			5
 __magic_name__   :						Any    =			20
 __magic_name__   :						int    =			len_data - periods * look_back
 __magic_name__   :						Union[str, Any]    =			actual_data[:division]
 __magic_name__   :						int    =			actual_data[division - look_back :]
 __magic_name__   ,    __magic_name__   :						List[str]    =			[], []
 __magic_name__   ,    __magic_name__   :						Tuple    =			[], []

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

 __magic_name__   :						Tuple    =			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""")
 __magic_name__   :						int    =			model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 __magic_name__   :						int    =			model.predict(x_test)


