






import numpy

# List of input, output pairs
__snake_case    :    List[str]         =     (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
__snake_case    :    str         =     (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
__snake_case    :    List[str]         =     [2, 4, 1, 5]
__snake_case    :    str         =     len(train_data)
__snake_case    :    Any         =     0.009







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Dict="train"		)  ->   str:

						"""simple docstring"""



						return calculate_hypothesis_value(UpperCamelCase_    ,       UpperCamelCase_		) - output(
						    UpperCamelCase_    ,       UpperCamelCase_		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]		)  ->   List[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						0
						for i in range(len(UpperCamelCase_		) - 1		):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val







def 	_UpperCamelCase   (    UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					Dict		)  ->   List[Any]:

						"""simple docstring"""



						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					Union[str, Any]		)  ->   int:

						"""simple docstring"""



						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]		)
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]		)
						return None







def 	_UpperCamelCase   (    UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					List[Any]=m		)  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						0
						for i in range(UpperCamelCase_		):
												if index == -1:
																		summation_value += _error(UpperCamelCase_		)
												else:
																		summation_value += _error(UpperCamelCase_		) * train_data[i][0][index]
						return summation_value







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]		)  ->   Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						summation_of_cost_derivative(UpperCamelCase_    ,       UpperCamelCase_		) / m
						return cost_derivative_value







def 	_UpperCamelCase   (    )  ->   Optional[int]:

						"""simple docstring"""



						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						lowerCAmelCase__    						=						0.00_0002
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						while True:
												j += 1
												lowerCAmelCase__    						=						[0, 0, 0, 0]
												for i in range(0    ,       len(UpperCamelCase_		)		):
																		lowerCAmelCase__    						=						get_cost_derivative(i - 1		)
																		lowerCAmelCase__    						=						(
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       atol=UpperCamelCase_    ,       rtol=UpperCamelCase_    ,       ):
																		break
												lowerCAmelCase__    						=						temp_parameter_vector
						print(('Number of iterations:', j)		)







def 	_UpperCamelCase   (    )  ->   Dict:

						"""simple docstring"""



						for i in range(len(UpperCamelCase_		)		):
												print(('Actual output value:', output(UpperCamelCase_    ,       'test'		))		)
												print(('Hypothesis output:', calculate_hypothesis_value(UpperCamelCase_    ,       'test'		))		)


if __name__ == "__main__":
					run_gradient_descent()
					print("""\nTesting gradient descent for a linear hypothesis function.\n""")
					test_gradient_descent()













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()
__snake_case    :    Any         =     logging.get_logger(__name__)

set_seed(7_70)


__snake_case    :    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""",
}


__snake_case    :    int         =     {
    """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""",
    },
}

__snake_case    :    Optional[int]         =     os.path.dirname(os.path.abspath(__file__))
__snake_case    :    Union[str, Any]         =     os.path.join(os.path.expanduser("""~"""), """.cache""")
__snake_case    :    Any         =     os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					int=False		)  ->   Dict:

						"""simple docstring"""



						lowerCAmelCase__    						=						model_type
						if use_small:
												key += "_small"
						return os.path.join(UpperCamelCase_    ,       REMOTE_MODEL_PATHS[key]['file_name']		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[int]		)  ->   Union[str, Any]:

						"""simple docstring"""



						os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_		)
						hf_hub_download(repo_id=UpperCamelCase_    ,       filename=UpperCamelCase_    ,       local_dir=UpperCamelCase_		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Any=False    ,       UpperCamelCase_       :					Union[str, Any]="text"		)  ->   Optional[Any]:

						"""simple docstring"""



						if model_type == "text":
												lowerCAmelCase__    						=						BarkSemanticModel
												lowerCAmelCase__    						=						BarkSemanticConfig
												lowerCAmelCase__    						=						BarkSemanticGenerationConfig
						elif model_type == "coarse":
												lowerCAmelCase__    						=						BarkCoarseModel
												lowerCAmelCase__    						=						BarkCoarseConfig
												lowerCAmelCase__    						=						BarkCoarseGenerationConfig
						elif model_type == "fine":
												lowerCAmelCase__    						=						BarkFineModel
												lowerCAmelCase__    						=						BarkFineConfig
												lowerCAmelCase__    						=						BarkFineGenerationConfig
						else:
												raise NotImplementedError()
						lowerCAmelCase__    						=						F"{model_type}_small" if use_small else model_type
						lowerCAmelCase__    						=						REMOTE_MODEL_PATHS[model_key]
						if not os.path.exists(UpperCamelCase_		):
												logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."		)
												_download(model_info['repo_id']    ,       model_info['file_name']		)
						lowerCAmelCase__    						=						torch.load(UpperCamelCase_    ,       map_location=UpperCamelCase_		)
						# this is a hack
						lowerCAmelCase__    						=						checkpoint['model_args']
						if "input_vocab_size" not in model_args:
												lowerCAmelCase__    						=						model_args['vocab_size']
												lowerCAmelCase__    						=						model_args['vocab_size']
												del model_args["vocab_size"]

						# convert Bark model arguments to HF Bark model arguments
						lowerCAmelCase__    						=						model_args.pop('n_head'		)
						lowerCAmelCase__    						=						model_args.pop('n_embd'		)
						lowerCAmelCase__    						=						model_args.pop('n_layer'		)

						lowerCAmelCase__    						=						ConfigClass(**checkpoint['model_args']		)
						lowerCAmelCase__    						=						ModelClass(config=UpperCamelCase_		)
						lowerCAmelCase__    						=						GenerationConfigClass()

						lowerCAmelCase__    						=						model_generation_config
						lowerCAmelCase__    						=						checkpoint['model']
						# fixup checkpoint
						lowerCAmelCase__    						=						'_orig_mod.'
						for k, v in list(state_dict.items()		):
												if k.startswith(UpperCamelCase_		):
																		# replace part of the key with corresponding layer name in HF implementation
																		lowerCAmelCase__    						=						k[len(UpperCamelCase_		) :]
																		for old_layer_name in new_layer_name_dict:
																								lowerCAmelCase__    						=						new_k.replace(UpperCamelCase_    ,       new_layer_name_dict[old_layer_name]		)

																		lowerCAmelCase__    						=						state_dict.pop(UpperCamelCase_		)

						lowerCAmelCase__    						=						set(state_dict.keys()		) - set(model.state_dict().keys()		)
						lowerCAmelCase__    						=						{k for k in extra_keys if not k.endswith('.attn.bias'		)}
						lowerCAmelCase__    						=						set(model.state_dict().keys()		) - set(state_dict.keys()		)
						lowerCAmelCase__    						=						{k for k in missing_keys if not k.endswith('.attn.bias'		)}
						if len(UpperCamelCase_		) != 0:
												raise ValueError(F"extra keys found: {extra_keys}"		)
						if len(UpperCamelCase_		) != 0:
												raise ValueError(F"missing keys: {missing_keys}"		)
						model.load_state_dict(UpperCamelCase_    ,       strict=UpperCamelCase_		)
						lowerCAmelCase__    						=						model.num_parameters(exclude_embeddings=UpperCamelCase_		)
						lowerCAmelCase__    						=						checkpoint['best_val_loss'].item()
						logger.info(F"model loaded: {round(n_params/1e6    ,       1		)}M params, {round(UpperCamelCase_    ,       3		)} loss"		)
						model.eval()
						model.to(UpperCamelCase_		)
						del checkpoint, state_dict

						return model







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Any=False    ,       UpperCamelCase_       :					Optional[int]="text"		)  ->   Dict:

						"""simple docstring"""



						if model_type not in ("text", "coarse", "fine"):
												raise NotImplementedError()

						lowerCAmelCase__    						=						'cpu'  # do conversion on cpu

						lowerCAmelCase__    						=						_get_ckpt_path(UpperCamelCase_    ,       use_small=UpperCamelCase_		)
						lowerCAmelCase__    						=						_load_model(UpperCamelCase_    ,       UpperCamelCase_    ,       model_type=UpperCamelCase_    ,       use_small=UpperCamelCase_		)

						# load bark initial model
						lowerCAmelCase__    						=						_bark_load_model(UpperCamelCase_    ,       'cpu'    ,       model_type=UpperCamelCase_    ,       use_small=UpperCamelCase_		)

						if model_type == "text":
												lowerCAmelCase__    						=						bark_model['model']

						if model.num_parameters(exclude_embeddings=UpperCamelCase_		) != 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
						lowerCAmelCase__    						=						5
						lowerCAmelCase__    						=						10

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

												lowerCAmelCase__    						=						model(UpperCamelCase_		)

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

						else:
												lowerCAmelCase__    						=						3
												lowerCAmelCase__    						=						8
												lowerCAmelCase__    						=						torch.randint(256    ,       (batch_size, sequence_length, n_codes_total)    ,       dtype=torch.int		)

												lowerCAmelCase__    						=						model(UpperCamelCase_    ,       UpperCamelCase_		)
												lowerCAmelCase__    						=						bark_model(UpperCamelCase_    ,       UpperCamelCase_		)

												lowerCAmelCase__    						=						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(UpperCamelCase_		).mkdir(exist_ok=UpperCamelCase_		)
						model.save_pretrained(UpperCamelCase_		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					int    ,       UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					Union[str, Any]    ,       )  ->   Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       UpperCamelCase_		)

						lowerCAmelCase__    						=						BarkSemanticConfig.from_pretrained(os.path.join(UpperCamelCase_    ,       'config.json'		)		)
						lowerCAmelCase__    						=						BarkCoarseConfig.from_pretrained(os.path.join(UpperCamelCase_    ,       'config.json'		)		)
						lowerCAmelCase__    						=						BarkFineConfig.from_pretrained(os.path.join(UpperCamelCase_    ,       'config.json'		)		)
						lowerCAmelCase__    						=						EncodecConfig.from_pretrained('facebook/encodec_24khz'		)

						lowerCAmelCase__    						=						BarkSemanticModel.from_pretrained(UpperCamelCase_		)
						lowerCAmelCase__    						=						BarkCoarseModel.from_pretrained(UpperCamelCase_		)
						lowerCAmelCase__    						=						BarkFineModel.from_pretrained(UpperCamelCase_		)
						lowerCAmelCase__    						=						EncodecModel.from_pretrained('facebook/encodec_24khz'		)

						lowerCAmelCase__    						=						BarkConfig.from_sub_model_configs(
						    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						lowerCAmelCase__    						=						BarkGenerationConfig.from_sub_model_configs(
						    semantic.generation_config    ,       coarseAcoustic.generation_config    ,       fineAcoustic.generation_config		)

						lowerCAmelCase__    						=						BarkModel(UpperCamelCase_		)

						lowerCAmelCase__    						=						semantic
						lowerCAmelCase__    						=						coarseAcoustic
						lowerCAmelCase__    						=						fineAcoustic
						lowerCAmelCase__    						=						codec

						lowerCAmelCase__    						=						bark_generation_config

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


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     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.""")

					__snake_case    :    str         =     parser.parse_args()

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





