





'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[
      "encoder.version",
      "decoder.version",
      "model.encoder.version",
      "model.decoder.version",
      "decoder.output_projection.weight",
      "_float_tensor",
      "encoder.embed_positions._float_tensor",
      "decoder.embed_positions._float_tensor",
  ]
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case		,		_snake_case     		=				emb.weight.shape
  _snake_case     		=				nn.Linear(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	bias=SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				emb.weight.data
  return lin_layer




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				torch.load(SCREAMING_SNAKE_CASE__					,	map_location="cpu"       )
  _snake_case     		=				mam_aaa["args"] or mam_aaa["cfg"]["model"]
  _snake_case     		=				mam_aaa["model"]
  remove_ignore_keys_(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				state_dict["encoder.embed_tokens.weight"].shape[0]

  _snake_case     		=				MaMaaaConfig(
      vocab_size=SCREAMING_SNAKE_CASE__					,	max_position_embeddings=10_24					,	encoder_layers=args.encoder_layers					,	decoder_layers=args.decoder_layers					,	encoder_attention_heads=args.encoder_attention_heads					,	decoder_attention_heads=args.decoder_attention_heads					,	encoder_ffn_dim=args.encoder_ffn_embed_dim					,	decoder_ffn_dim=args.decoder_ffn_embed_dim					,	d_model=args.encoder_embed_dim					,	encoder_layerdrop=args.encoder_layerdrop					,	decoder_layerdrop=args.decoder_layerdrop					,	dropout=args.dropout					,	attention_dropout=args.attention_dropout					,	activation_dropout=args.activation_dropout					,	activation_function="relu"					,	)

  _snake_case     		=				state_dict["decoder.embed_tokens.weight"]
  _snake_case     		=				MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE__       )
  model.model.load_state_dict(SCREAMING_SNAKE_CASE__					,	strict=SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				make_linear_from_emb(model.model.shared       )

  return model


if __name__ == "__main__":
 __magic_name__   :						Tuple    =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
 parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 __magic_name__   :						int    =			parser.parse_args()
 __magic_name__   :						Any    =			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)









'''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)


