




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a__		     =     logging.get_logger(__name__)

a__		     =     {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}





def 				A__ (snake_case   :		Optional[int]		,	snake_case   :		str		,	snake_case   :		Dict		,	snake_case   :		Union[str, Any]		,	snake_case   :		List[str]				)     ->						Optional[int]:
					for attribute in key.split("""."""				):
										__UpperCamelCase					:	str             =      getattr(snake_case		,	snake_case				)

					if weight_type is not None:
										__UpperCamelCase					:	str             =      getattr(snake_case		,	snake_case				).shape
					else:
										__UpperCamelCase					:	List[Any]             =      hf_pointer.shape

					assert hf_shape == value.shape, (
					    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
					    F''' {value.shape} for {full_name}'''
					)

					if weight_type == "weight":
										__UpperCamelCase					:	Dict             =      value
					elif weight_type == "weight_g":
										__UpperCamelCase					:	List[str]             =      value
					elif weight_type == "weight_v":
										__UpperCamelCase					:	Optional[Any]             =      value
					elif weight_type == "bias":
										__UpperCamelCase					:	List[str]             =      value
					else:
										__UpperCamelCase					:	Optional[Any]             =      value

					logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''				)





def 				A__ (snake_case   :		Optional[int]		,	snake_case   :		Tuple		,	snake_case   :		Tuple				)     ->						Optional[int]:
					__UpperCamelCase					:	Dict             =      []
					__UpperCamelCase					:	Tuple             =      fairseq_model.state_dict()

					__UpperCamelCase					:	Union[str, Any]             =      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										__UpperCamelCase					:	Dict             =      False
										if "conv_layers" in name:
															load_conv_layer(
															    snake_case		,	snake_case		,	snake_case		,	snake_case		,	hf_model.config.feat_extract_norm == """group"""		,	)
															__UpperCamelCase					:	Optional[Any]             =      True
										else:
															for key, mapped_key in MAPPING.items():
																				__UpperCamelCase					:	List[str]             =      """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

																				if key in name or key.split("""w2v_model."""				)[-1] == name.split("""."""				)[0]:
																									__UpperCamelCase					:	Any             =      True
																									if "*" in mapped_key:
																														__UpperCamelCase					:	str             =      name.split(snake_case				)[0].split("""."""				)[-2]
																														__UpperCamelCase					:	Any             =      mapped_key.replace("""*"""		,	snake_case				)
																									if "weight_g" in name:
																														__UpperCamelCase					:	List[str]             =      """weight_g"""
																									elif "weight_v" in name:
																														__UpperCamelCase					:	str             =      """weight_v"""
																									elif "weight" in name:
																														__UpperCamelCase					:	Tuple             =      """weight"""
																									elif "bias" in name:
																														__UpperCamelCase					:	Dict             =      """bias"""
																									else:
																														__UpperCamelCase					:	Any             =      None
																									set_recursively(snake_case		,	snake_case		,	snake_case		,	snake_case		,	snake_case				)
																				continue
										if not is_used:
															unused_weights.append(snake_case				)

					logger.warning(F'''Unused weights: {unused_weights}'''				)





def 				A__ (snake_case   :		Dict		,	snake_case   :		Tuple		,	snake_case   :		Optional[int]		,	snake_case   :		List[Any]		,	snake_case   :		List[Any]				)     ->						List[str]:
					__UpperCamelCase					:	List[Any]             =      full_name.split("""conv_layers."""				)[-1]
					__UpperCamelCase					:	Union[str, Any]             =      name.split("""."""				)
					__UpperCamelCase					:	List[Any]             =      int(items[0]				)
					__UpperCamelCase					:	Optional[Any]             =      int(items[1]				)

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
															)
															__UpperCamelCase					:	Optional[int]             =      value
															logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
															)
															__UpperCamelCase					:	Union[str, Any]             =      value
															logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
															    " found."
															)
															__UpperCamelCase					:	str             =      value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
															)
															__UpperCamelCase					:	Optional[Any]             =      value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
					else:
										unused_weights.append(snake_case				)





def 				A__ (snake_case   :		Tuple		,	snake_case   :		Optional[Any]				)     ->						List[Any]:
					__UpperCamelCase					:	Tuple             =      SEWConfig()
					if is_finetuned:
										__UpperCamelCase					:	Optional[Any]             =      model.wav_encoder.wav_model.cfg
					else:
										__UpperCamelCase					:	str             =      model.cfg

					__UpperCamelCase					:	Optional[Any]             =      fs_config.conv_bias
					__UpperCamelCase					:	Tuple             =      eval(fs_config.conv_feature_layers				)
					__UpperCamelCase					:	List[str]             =      [x[0] for x in conv_layers]
					__UpperCamelCase					:	Tuple             =      [x[1] for x in conv_layers]
					__UpperCamelCase					:	int             =      [x[2] for x in conv_layers]
					__UpperCamelCase					:	List[Any]             =      """gelu"""
					__UpperCamelCase					:	List[str]             =      """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
					__UpperCamelCase					:	Optional[Any]             =      0.0
					__UpperCamelCase					:	str             =      fs_config.activation_fn.name
					__UpperCamelCase					:	Optional[Any]             =      fs_config.encoder_embed_dim
					__UpperCamelCase					:	Union[str, Any]             =      0.02
					__UpperCamelCase					:	str             =      fs_config.encoder_ffn_embed_dim
					__UpperCamelCase					:	List[Any]             =      1e-5
					__UpperCamelCase					:	Dict             =      fs_config.encoder_layerdrop
					__UpperCamelCase					:	int             =      fs_config.encoder_attention_heads
					__UpperCamelCase					:	Union[str, Any]             =      fs_config.conv_pos_groups
					__UpperCamelCase					:	Any             =      fs_config.conv_pos
					__UpperCamelCase					:	str             =      len(snake_case				)
					__UpperCamelCase					:	List[Any]             =      fs_config.encoder_layers
					__UpperCamelCase					:	Optional[Any]             =      fs_config.squeeze_factor

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										__UpperCamelCase					:	Union[str, Any]             =      model.cfg
										__UpperCamelCase					:	Optional[Any]             =      fs_config.final_dropout
										__UpperCamelCase					:	Union[str, Any]             =      fs_config.layerdrop
					__UpperCamelCase					:	Union[str, Any]             =      fs_config.activation_dropout
					__UpperCamelCase					:	List[str]             =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					__UpperCamelCase					:	List[str]             =      fs_config.attention_dropout
					__UpperCamelCase					:	Tuple             =      fs_config.dropout_input
					__UpperCamelCase					:	Optional[Any]             =      fs_config.dropout
					__UpperCamelCase					:	List[str]             =      fs_config.mask_channel_length
					__UpperCamelCase					:	str             =      fs_config.mask_channel_prob
					__UpperCamelCase					:	List[Any]             =      fs_config.mask_length
					__UpperCamelCase					:	List[Any]             =      fs_config.mask_prob

					__UpperCamelCase					:	List[str]             =      """Wav2Vec2FeatureExtractor"""
					__UpperCamelCase					:	str             =      """Wav2Vec2CTCTokenizer"""

					return config





@torch.no_grad()
def 				A__ (snake_case   :		Optional[int]		,	snake_case   :		Dict		,	snake_case   :		str=None		,	snake_case   :		Dict=None		,	snake_case   :		Any=True				)     ->						Tuple:

					if is_finetuned:
										__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	List[str]             =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]		,	arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				)}				)
					else:
										__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	List[str]             =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

					if config_path is not None:
										__UpperCamelCase					:	Tuple             =      SEWConfig.from_pretrained(snake_case				)
					else:
										__UpperCamelCase					:	int             =      convert_config(model[0]		,	snake_case				)
					__UpperCamelCase					:	str             =      model[0].eval()

					__UpperCamelCase					:	Tuple             =      True if config.feat_extract_norm == """layer""" else False
					__UpperCamelCase					:	Optional[int]             =      WavaVecaFeatureExtractor(
					    feature_size=1		,	sampling_rate=1_60_00		,	padding_value=0		,	do_normalize=snake_case		,	return_attention_mask=snake_case		,	)

					if is_finetuned:
										if dict_path:
															__UpperCamelCase					:	List[str]             =      Dictionary.load(snake_case				)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__UpperCamelCase					:	Dict             =      target_dict.pad_index
															__UpperCamelCase					:	List[str]             =      target_dict.bos_index
															__UpperCamelCase					:	List[Any]             =      target_dict.pad_index
															__UpperCamelCase					:	Tuple             =      target_dict.bos_index
															__UpperCamelCase					:	Optional[Any]             =      target_dict.eos_index
															__UpperCamelCase					:	Union[str, Any]             =      len(target_dict.symbols				)
															__UpperCamelCase					:	int             =      os.path.join(snake_case		,	"""vocab.json"""				)
															if not os.path.isdir(snake_case				):
																				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case				)				)
																				return
															os.makedirs(snake_case		,	exist_ok=snake_case				)
															with open(snake_case		,	"""w"""		,	encoding="""utf-8"""				) as vocab_handle:
																				json.dump(target_dict.indices		,	snake_case				)
															__UpperCamelCase					:	Tuple             =      WavaVecaCTCTokenizer(
															    snake_case		,	unk_token=target_dict.unk_word		,	pad_token=target_dict.pad_word		,	bos_token=target_dict.bos_word		,	eos_token=target_dict.eos_word		,	word_delimiter_token="""|"""		,	do_lower_case=snake_case		,	)
															__UpperCamelCase					:	int             =      WavaVecaProcessor(feature_extractor=snake_case		,	tokenizer=snake_case				)
															processor.save_pretrained(snake_case				)

										__UpperCamelCase					:	Dict             =      SEWForCTC(snake_case				)
					else:
										__UpperCamelCase					:	Optional[int]             =      SEWModel(snake_case				)
										feature_extractor.save_pretrained(snake_case				)

					recursively_load_weights(snake_case		,	snake_case		,	snake_case				)

					hf_model.save_pretrained(snake_case				)


if __name__ == "__main__":
	a__		     =     argparse.ArgumentParser()
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
	parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
	parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
	parser.add_argument(
	    '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
	)
	a__		     =     parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)












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)






