






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       Dict				):
			__a						:  Tuple									=     AutoConfig.from_pretrained(lowerCamelCase_				)
			__a						:  Optional[int]									=     FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCamelCase_				)
			__a						:  Union[str, Any]									=     checkpoints.load_tax_checkpoint(lowerCamelCase_				)

			__a						:  str									=     'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

			if config.model_type == "t5":
						__a						:  Dict									=     'SelfAttention'
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						__a						:  int									=     'LocalSelfAttention'
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__a						:  Any									=     'TransientGlobalSelfAttention'
			else:
						raise ValueError(
						    'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
						    ' attribute with a value from [\'local\', \'transient-global].'				)

			# Encoder
			for layer_index in range(config.num_layers				):
						__a						:  List[Any]									=     f'''layers_{str(lowerCamelCase_				)}'''

						# Self-Attention
						__a						:  str									=     tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
						__a						:  List[str]									=     tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
						__a						:  int									=     tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
						__a						:  List[Any]									=     tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									__a						:  str									=     tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

						# Layer Normalization
						__a						:  Union[str, Any]									=     tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

						if split_mlp_wi:
									__a						:  str									=     tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
									__a						:  List[str]									=     tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
						else:
									__a						:  int									=     tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

						__a						:  int									=     tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

						# Layer Normalization
						__a						:  Optional[Any]									=     tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

						# Assigning
						__a						:  int									=     flax_model.params['encoder']['block'][str(lowerCamelCase_				)]['layer']
						__a						:  str									=     tax_attention_key
						__a						:  Dict									=     tax_attention_out
						__a						:  Union[str, Any]									=     tax_attention_query
						__a						:  List[str]									=     tax_attention_value

						__a						:  str									=     tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									__a						:  Any									=     tax_global_layer_norm

						if split_mlp_wi:
									__a						:  Any									=     tax_mlp_wi_a
									__a						:  Optional[int]									=     tax_mlp_wi_a
						else:
									__a						:  Union[str, Any]									=     tax_mlp_wi

						__a						:  List[Any]									=     tax_mlp_wo
						__a						:  List[Any]									=     tax_mlp_layer_norm

						__a						:  Optional[Any]									=     flax_model_encoder_layer_block

			# Only for layer 0:
			__a						:  Dict									=     tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
			__a						:  Optional[int]									=     tax_encoder_rel_embedding

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__a						:  Union[str, Any]									=     tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
						__a						:  Optional[Any]									=     tax_encoder_global_rel_embedding

			# Assigning
			__a						:  List[str]									=     tax_model['target']['encoder']['encoder_norm']['scale']
			__a						:  Tuple									=     tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers				):
						__a						:  Dict									=     f'''layers_{str(lowerCamelCase_				)}'''

						# Self-Attention
						__a						:  Any									=     tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
						__a						:  Any									=     tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
						__a						:  List[Any]									=     tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
						__a						:  Optional[Any]									=     tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

						# Layer Normalization
						__a						:  int									=     tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
						    'scale'
						]

						# Encoder-Decoder-Attention
						__a						:  str									=     tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
						__a						:  Union[str, Any]									=     tax_enc_dec_attention_module['key']['kernel']
						__a						:  List[Any]									=     tax_enc_dec_attention_module['out']['kernel']
						__a						:  Optional[int]									=     tax_enc_dec_attention_module['query']['kernel']
						__a						:  List[Any]									=     tax_enc_dec_attention_module['value']['kernel']

						# Layer Normalization
						__a						:  int									=     tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

						# MLP
						if split_mlp_wi:
									__a						:  Tuple									=     tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
									__a						:  Tuple									=     tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
						else:
									__a						:  Dict									=     tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

						__a						:  int									=     tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

						# Layer Normalization
						__a						:  List[str]									=     tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

						# Assigning
						__a						:  Union[str, Any]									=     flax_model.params['decoder']['block'][str(lowerCamelCase_				)]['layer']
						__a						:  List[str]									=     tax_attention_key
						__a						:  Any									=     tax_attention_out
						__a						:  List[Any]									=     tax_attention_query
						__a						:  Optional[Any]									=     tax_attention_value

						__a						:  Any									=     tax_pre_attention_layer_norm

						__a						:  int									=     tax_enc_dec_attention_key
						__a						:  Optional[Any]									=     tax_enc_dec_attention_out
						__a						:  List[str]									=     tax_enc_dec_attention_query
						__a						:  str									=     tax_enc_dec_attention_value

						__a						:  Dict									=     tax_cross_layer_norm

						if split_mlp_wi:
									__a						:  Union[str, Any]									=     tax_mlp_wi_a
									__a						:  int									=     tax_mlp_wi_a
						else:
									__a						:  int									=     tax_mlp_wi

						__a						:  Dict									=     tax_mlp_wo

						__a						:  Union[str, Any]									=     txa_mlp_layer_norm

						__a						:  Optional[int]									=     flax_model_decoder_layer_block

			# Decoder Normalization
			__a						:  Tuple									=     tax_model['target']['decoder']['decoder_norm']['scale']
			__a						:  Optional[Any]									=     txa_decoder_norm

			# Only for layer 0:
			__a						:  Optional[int]									=     tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
			__a						:  Optional[Any]									=     tax_decoder_rel_embedding

			# Token Embeddings
			__a						:  Tuple									=     tax_model['target']['token_embedder']['embedding']
			__a						:  List[Any]									=     txa_token_embeddings

			# LM Head (only in v1.1 and LongT5 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						__a						:  str									=     tax_model['target']['decoder']['logits_dense']['kernel']

			flax_model.save_pretrained(lowerCamelCase_				)
			print('T5X Model was sucessfully converted!'				)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
							)
							parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
							parser.add_argument(
							    '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
							)
							SCREAMING_SNAKE_CASE__				     =			parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)











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(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''')
	a__		     =     model.fit(
	    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
	)
	a__		     =     model.predict(x_test)






