

import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:					Dict		 =   logging.get_logger(__name__)

set_seed(770)


UpperCAmelCase_							:					Union[str, Any]		 =   {
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


UpperCAmelCase_							:					List[str]		 =   {
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

UpperCAmelCase_							:					List[Any]		 =   os.path.dirname(os.path.abspath(__file__))
UpperCAmelCase_							:					Union[str, Any]		 =   os.path.join(os.path.expanduser('''~'''), '''.cache''')
UpperCAmelCase_							:					Optional[int]		 =   os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict			,a__    :   Union[str, Any]=False       )					->				List[str]:
	__A :			Optional[int]		    =			model_type
	if use_small:
		key += "_small"
	return os.path.join(a__			,REMOTE_MODEL_PATHS[key]["""file_name"""]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				Tuple:
	os.makedirs(a__			,exist_ok=a__       )
	hf_hub_download(repo_id=a__			,filename=a__			,local_dir=a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   str			,a__    :   List[str]=False			,a__    :   int="text"       )					->				Dict:
	if model_type == "text":
		__A :			List[str]		    =			BarkSemanticModel
		__A :			Union[str, Any]		    =			BarkSemanticConfig
		__A :			Union[str, Any]		    =			BarkSemanticGenerationConfig
	elif model_type == "coarse":
		__A :			str		    =			BarkCoarseModel
		__A :			Optional[int]		    =			BarkCoarseConfig
		__A :			Any		    =			BarkCoarseGenerationConfig
	elif model_type == "fine":
		__A :			List[Any]		    =			BarkFineModel
		__A :			Optional[int]		    =			BarkFineConfig
		__A :			str		    =			BarkFineGenerationConfig
	else:
		raise NotImplementedError()
	__A :			List[str]		    =			f"""{model_type}_small""" if use_small else model_type
	__A :			str		    =			REMOTE_MODEL_PATHS[model_key]
	if not os.path.exists(a__       ):
		logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""       )
		_download(model_info["""repo_id"""]			,model_info["""file_name"""]       )
	__A :			Optional[int]		    =			torch.load(a__			,map_location=a__       )
	# this is a hack
	__A :			str		    =			checkpoint["""model_args"""]
	if "input_vocab_size" not in model_args:
		__A :			Tuple		    =			model_args["""vocab_size"""]
		__A :			Any		    =			model_args["""vocab_size"""]
		del model_args["vocab_size"]

	# convert Bark model arguments to HF Bark model arguments
	__A :			Any		    =			model_args.pop("""n_head"""       )
	__A :			Any		    =			model_args.pop("""n_embd"""       )
	__A :			str		    =			model_args.pop("""n_layer"""       )

	__A :			Optional[Any]		    =			ConfigClass(**checkpoint["""model_args"""]       )
	__A :			Union[str, Any]		    =			ModelClass(config=a__       )
	__A :			List[Any]		    =			GenerationConfigClass()

	__A :			List[Any]		    =			model_generation_config
	__A :			Union[str, Any]		    =			checkpoint["""model"""]
	# fixup checkpoint
	__A :			List[str]		    =			"""_orig_mod."""
	for k, v in list(state_dict.items()       ):
		if k.startswith(a__       ):
			# replace part of the key with corresponding layer name in HF implementation
			__A :			List[Any]		    =			k[len(a__       ) :]
			for old_layer_name in new_layer_name_dict:
				__A :			List[str]		    =			new_k.replace(a__			,new_layer_name_dict[old_layer_name]       )

			__A :			Union[str, Any]		    =			state_dict.pop(a__       )

	__A :			str		    =			set(state_dict.keys()       ) - set(model.state_dict().keys()       )
	__A :			int		    =			{k for k in extra_keys if not k.endswith(""".attn.bias"""       )}
	__A :			Union[str, Any]		    =			set(model.state_dict().keys()       ) - set(state_dict.keys()       )
	__A :			Any		    =			{k for k in missing_keys if not k.endswith(""".attn.bias"""       )}
	if len(a__       ) != 0:
		raise ValueError(f"""extra keys found: {extra_keys}"""       )
	if len(a__       ) != 0:
		raise ValueError(f"""missing keys: {missing_keys}"""       )
	model.load_state_dict(a__			,strict=a__       )
	__A :			List[str]		    =			model.num_parameters(exclude_embeddings=a__       )
	__A :			Optional[Any]		    =			checkpoint["""best_val_loss"""].item()
	logger.info(f"""model loaded: {round(n_params/1E6			,1       )}M params, {round(a__			,3       )} loss"""       )
	model.eval()
	model.to(a__       )
	del checkpoint, state_dict

	return model
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   Optional[Any]=False			,a__    :   int="text"       )					->				Dict:
	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	__A :			int		    =			"""cpu"""  # do conversion on cpu

	__A :			Union[str, Any]		    =			_get_ckpt_path(a__			,use_small=a__       )
	__A :			Union[str, Any]		    =			_load_model(a__			,a__			,model_type=a__			,use_small=a__       )

	# load bark initial model
	__A :			Any		    =			_bark_load_model(a__			,"""cpu"""			,model_type=a__			,use_small=a__       )

	if model_type == "text":
		__A :			int		    =			bark_model["""model"""]

	if model.num_parameters(exclude_embeddings=a__       ) != bark_model.get_num_params():
		raise ValueError("""initial and new models don't have the same number of parameters"""       )

	# check if same output as the bark model
	__A :			Optional[Any]		    =			5
	__A :			Optional[int]		    =			10

	if model_type in ["text", "coarse"]:
		__A :			Optional[int]		    =			torch.randint(256			,(batch_size, sequence_length)			,dtype=torch.int       )
		__A :			Optional[int]		    =			bark_model(a__       )[0]

		__A :			Union[str, Any]		    =			model(a__       )

		# take last logits
		__A :			Tuple		    =			output_new_model_total.logits[:, [-1], :]

	else:
		__A :			Dict		    =			3
		__A :			Any		    =			8
		__A :			Optional[Any]		    =			torch.randint(256			,(batch_size, sequence_length, n_codes_total)			,dtype=torch.int       )

		__A :			List[Any]		    =			model(a__			,a__       )
		__A :			Optional[int]		    =			bark_model(a__			,a__       )

		__A :			Any		    =			output_new_model_total.logits

	# output difference should come from the difference of self-attention implementation design
	if output_new_model.shape != output_old_model.shape:
		raise ValueError("""initial and new outputs don't have the same shape"""       )
	if (output_new_model - output_old_model).abs().max().item() > 1E-3:
		raise ValueError("""initial and new outputs are not equal"""       )

	Path(a__       ).mkdir(exist_ok=a__       )
	model.save_pretrained(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[Any]			,a__    :   Any			,a__    :   str			,a__    :   List[str]			,a__    :   int			,a__    :   Optional[int]			,)					->				List[str]:
	__A :			List[str]		    =			os.path.join(a__			,a__       )

	__A :			List[Any]		    =			BarkSemanticConfig.from_pretrained(os.path.join(a__			,"""config.json"""       )       )
	__A :			Union[str, Any]		    =			BarkCoarseConfig.from_pretrained(os.path.join(a__			,"""config.json"""       )       )
	__A :			List[str]		    =			BarkFineConfig.from_pretrained(os.path.join(a__			,"""config.json"""       )       )
	__A :			Optional[int]		    =			EncodecConfig.from_pretrained("""facebook/encodec_24khz"""       )

	__A :			Union[str, Any]		    =			BarkSemanticModel.from_pretrained(a__       )
	__A :			int		    =			BarkCoarseModel.from_pretrained(a__       )
	__A :			Any		    =			BarkFineModel.from_pretrained(a__       )
	__A :			int		    =			EncodecModel.from_pretrained("""facebook/encodec_24khz"""       )

	__A :			Dict		    =			BarkConfig.from_sub_model_configs(
	    a__			,a__			,a__			,a__       )

	__A :			Any		    =			BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config			,coarseAcoustic.generation_config			,fineAcoustic.generation_config       )

	__A :			int		    =			BarkModel(a__       )

	__A :			Optional[int]		    =			semantic
	__A :			Any		    =			coarseAcoustic
	__A :			Optional[Any]		    =			fineAcoustic
	__A :			Optional[Any]		    =			codec

	__A :			Tuple		    =			bark_generation_config

	Path(a__       ).mkdir(exist_ok=a__       )
	bark.save_pretrained(a__			,repo_id=a__			,push_to_hub=a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					int		 =   argparse.ArgumentParser()
			# Required parameters

			parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

			UpperCAmelCase_							:					Optional[Any]		 =   parser.parse_args()

			load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)






"""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__":
       __lowerCAmelCase       :			int   	=pd.read_csv("""sample_data.csv""", header=None)
       __lowerCAmelCase       :			Optional[Any]   	=df.shape[:1][0]
       # If you're using some other dataset input the target column
       __lowerCAmelCase       :			Optional[int]   	=df.iloc[:, 1:2]
       __lowerCAmelCase       :			List[str]   	=actual_data.values.reshape(len_data, 1)
       __lowerCAmelCase       :			int   	=MinMaxScaler().fit_transform(actual_data)
       __lowerCAmelCase       :			List[Any]   	=1_0
       __lowerCAmelCase       :			int   	=5
       __lowerCAmelCase       :			str   	=2_0
       __lowerCAmelCase       :			Union[str, Any]   	=len_data - periods * look_back
       __lowerCAmelCase       :			Dict   	=actual_data[:division]
       __lowerCAmelCase       :			List[str]   	=actual_data[division - look_back :]
       __lowerCAmelCase	,	__lowerCAmelCase       :			List[str]   	=[], []
       __lowerCAmelCase	,	__lowerCAmelCase       :			Optional[int]   	=[], []

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

       __lowerCAmelCase       :			Optional[Any]   	=Sequential()
       model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
       model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
       model.add(Dense(forward_days))
       model.compile(loss="""mean_squared_error""", optimizer="""adam""")
       __lowerCAmelCase       :			Optional[int]   	=model.fit(
           x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
       )
       __lowerCAmelCase       :			Dict   	=model.predict(x_test)



