




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



       __lowerCamelCase   =						[
           """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(_lowercase  ,			_lowercase	)




def 					lowerCamelCase__       (						A__       :					Any	):





       '''simple docstring'''



       __lowerCamelCase   =						list(s_dict.keys()	)
       for key in keys:
              if "transformer_layers" in key:
                     __lowerCamelCase   =						s_dict.pop(_lowercase	)
              elif "subsample" in key:
                     __lowerCamelCase   =						s_dict.pop(_lowercase	)




def 					lowerCamelCase__       (						A__       :					List[str]	):





       '''simple docstring'''



       __lowerCamelCase   =						emb.weight.shape
       __lowerCamelCase   =						nn.Linear(_lowercase  ,			_lowercase  ,			bias=_lowercase	)
       __lowerCamelCase   =						emb.weight.data
       return lin_layer




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					Optional[Any]	):





       '''simple docstring'''



       __lowerCamelCase   =						torch.load(_lowercase  ,			map_location="""cpu"""	)
       __lowerCamelCase   =						mam_aaa["""args"""]
       __lowerCamelCase   =						mam_aaa["""model"""]
       __lowerCamelCase   =						state_dict["""decoder.output_projection.weight"""]

       remove_ignore_keys_(_lowercase	)
       rename_keys(_lowercase	)

       __lowerCamelCase   =						state_dict["""decoder.embed_tokens.weight"""].shape[0]

       __lowerCamelCase   =						args.share_decoder_input_output_embed

       __lowerCamelCase   =						[int(_lowercase	) for i in args.conv_kernel_sizes.split(""","""	)]
       __lowerCamelCase   =						SpeechaTextConfig(
           vocab_size=_lowercase  ,			max_source_positions=args.max_source_positions  ,			max_target_positions=args.max_target_positions  ,			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  ,			dropout=args.dropout  ,			attention_dropout=args.attention_dropout  ,			activation_dropout=args.activation_dropout  ,			activation_function="""relu"""  ,			num_conv_layers=len(_lowercase	)  ,			conv_channels=args.conv_channels  ,			conv_kernel_sizes=_lowercase  ,			input_feat_per_channel=args.input_feat_per_channel  ,			input_channels=args.input_channels  ,			tie_word_embeddings=_lowercase  ,			num_beams=5  ,			max_length=200  ,			use_cache=_lowercase  ,			decoder_start_token_id=2  ,			early_stopping=_lowercase  ,			)

       __lowerCamelCase   =						SpeechaTextForConditionalGeneration(_lowercase	)
       __lowerCamelCase   =						model.model.load_state_dict(_lowercase  ,			strict=_lowercase	)
       if len(_lowercase	) > 0 and not set(_lowercase	) <= {
           "encoder.embed_positions.weights",
           "decoder.embed_positions.weights",
       }:
              raise ValueError(
                  """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                  f' but all the following weights are missing {missing}'	)

       if tie_embeds:
              __lowerCamelCase   =						make_linear_from_emb(model.model.decoder.embed_tokens	)
       else:
              __lowerCamelCase   =						lm_head_weights

       model.save_pretrained(_lowercase	)


if __name__ == "__main__":
       UpperCAmelCase_									=	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       UpperCAmelCase_									=	parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, 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__":
						__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(1_28, input_shape=(look_back, 1), return_sequences=True))
						model.add(LSTM(64, input_shape=(1_28, 1)))
						model.add(Dense(forward_days))
						model.compile(loss="mean_squared_error", optimizer="adam")
						__a      =				model.fit(
						    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
						)
						__a      =				model.predict(x_test)
