
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
		from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


lowercase__											=logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase__											=2_56







class   a_ (      __snake_case					):
			lowerCamelCase__	:			Any       =    ["""melgan"""]




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   ):
				super().__init__()

				# From MELGAN
				a_      				=						math.log(1e-5	)  # Matches MelGAN training.
				a_      				=						4.0  # Largest value for most examples
				a_      				=						1_28

				self.register_modules(
				    notes_encoder=UpperCAmelCase					,   continuous_encoder=UpperCAmelCase					,   decoder=UpperCAmelCase					,   scheduler=UpperCAmelCase					,   melgan=UpperCAmelCase					,   )




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=(-1.0, 1.0)					,   UpperCAmelCase=False	):
				a_      				=						output_range
				if clip:
					a_      				=						torch.clip(UpperCAmelCase					,   self.min_value					,   self.max_value	)
				# Scale to [0, 1].
				a_      				=						(features - self.min_value) / (self.max_value - self.min_value)
				# Scale to [min_out, max_out].
				return zero_one * (max_out - min_out) + min_out




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=(-1.0, 1.0)					,   UpperCAmelCase=False	):
				a_      				=						input_range
				a_      				=						torch.clip(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	) if clip else outputs
				# Scale to [0, 1].
				a_      				=						(outputs - min_out) / (max_out - min_out)
				# Scale to [self.min_value, self.max_value].
				return zero_one * (self.max_value - self.min_value) + self.min_value




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						input_tokens > 0
				a_      				=						self.notes_encoder(
				    encoder_input_tokens=UpperCAmelCase					,   encoder_inputs_mask=UpperCAmelCase	)

				a_      				=						self.continuous_encoder(
				    encoder_inputs=UpperCAmelCase					,   encoder_inputs_mask=UpperCAmelCase	)

				return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						noise_time
				if not torch.is_tensor(UpperCAmelCase	):
					a_      				=						torch.tensor([timesteps]					,   dtype=torch.long					,   device=input_tokens.device	)
				elif torch.is_tensor(UpperCAmelCase	) and len(timesteps.shape	) == 0:
					a_      				=						timesteps[None].to(input_tokens.device	)

				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
				a_      				=						timesteps * torch.ones(input_tokens.shape[0]					,   dtype=timesteps.dtype					,   device=timesteps.device	)

				a_      				=						self.decoder(
				    encodings_and_masks=UpperCAmelCase					,   decoder_input_tokens=UpperCAmelCase					,   decoder_noise_time=UpperCAmelCase	)
				return logits






			@torch.no_grad()
			def __call__(       self					,   UpperCAmelCase					,   UpperCAmelCase = None					,   UpperCAmelCase = 1_00					,   UpperCAmelCase = True					,   UpperCAmelCase = "numpy"					,   UpperCAmelCase = None					,   UpperCAmelCase = 1					,   ):
				if (callback_steps is None) or (
				    callback_steps is not None and (not isinstance(UpperCAmelCase					,   UpperCAmelCase	) or callback_steps <= 0)
				):
					raise ValueError(
					    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
					    f''' {type(UpperCAmelCase	)}.'''	)

				a_      				=						np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]					,   dtype=np.floataa	)
				a_      				=						np.zeros([1, 0, self.n_dims]					,   np.floataa	)
				a_      				=						torch.ones((1, TARGET_FEATURE_LENGTH)					,   dtype=UpperCAmelCase					,   device=self.device	)

				for i, encoder_input_tokens in enumerate(UpperCAmelCase	):
					if i == 0:
						a_      				=						torch.from_numpy(pred_mel[:1].copy()	).to(
						    device=self.device					,   dtype=self.decoder.dtype	)
						# The first chunk has no previous context.
						a_      				=						torch.zeros((1, TARGET_FEATURE_LENGTH)					,   dtype=UpperCAmelCase					,   device=self.device	)
					else:
						# The full song pipeline does not feed in a context feature, so the mask
						# will be all 0s after the feature converter. Because we know we're
						# feeding in a full context chunk from the previous prediction, set it
						# to all 1s.
						a_      				=						ones

					a_      				=						self.scale_features(
					    UpperCAmelCase					,   output_range=[-1.0, 1.0]					,   clip=UpperCAmelCase	)

					a_      				=						self.encode(
					    input_tokens=torch.IntTensor([encoder_input_tokens]	).to(device=self.device	)					,   continuous_inputs=UpperCAmelCase					,   continuous_mask=UpperCAmelCase					,   )

					# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
					a_      				=						randn_tensor(
					    shape=encoder_continuous_inputs.shape					,   generator=UpperCAmelCase					,   device=self.device					,   dtype=self.decoder.dtype					,   )

					# set step values
					self.scheduler.set_timesteps(UpperCAmelCase	)

					# Denoising diffusion loop
					for j, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
						a_      				=						self.decode(
						    encodings_and_masks=UpperCAmelCase					,   input_tokens=UpperCAmelCase					,   noise_time=t / self.scheduler.config.num_train_timesteps					,   )

						# Compute previous output: x_t -> x_t-1
						a_      				=						self.scheduler.step(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   generator=UpperCAmelCase	).prev_sample

					a_      				=						self.scale_to_features(UpperCAmelCase					,   input_range=[-1.0, 1.0]	)
					a_      				=						mel[:1]
					a_      				=						mel.cpu().float().numpy()

					a_      				=						np.concatenate([full_pred_mel, pred_mel[:1]]					,   axis=1	)

					# call the callback, if provided
					if callback is not None and i % callback_steps == 0:
						callback(UpperCAmelCase					,   UpperCAmelCase	)

					logger.info("""Generated segment"""					,   UpperCAmelCase	)

				if output_type == "numpy" and not is_onnx_available():
					raise ValueError(
					    """Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."""	)
				elif output_type == "numpy" and self.melgan is None:
					raise ValueError(
					    """Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."""	)

				if output_type == "numpy":
					a_      				=						self.melgan(input_features=full_pred_mel.astype(np.floataa	)	)
				else:
					a_      				=						full_pred_mel

				if not return_dict:
					return (output,)

				return AudioPipelineOutput(audios=UpperCAmelCase	)








"""simple docstring"""







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()
_lowerCAmelCase            =	logging.get_logger(__name__)

set_seed(7_70)


_lowerCAmelCase            =	{
    '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',
}


_lowerCAmelCase            =	{
    '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',
    },
}

_lowerCAmelCase            =	os.path.dirname(os.path.abspath(__file__))
_lowerCAmelCase            =	os.path.join(os.path.expanduser('~'), '.cache')
_lowerCAmelCase            =	os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')


def      UpperCamelCase       ( _A  ,				_A=False )     ->       Dict:
				lowercase    :      Union[str, Any]       = model_type
				if use_small:
								key += "_small"
				return os.path.join(_A  ,				REMOTE_MODEL_PATHS[key]["""file_name"""] )


def      UpperCamelCase       ( _A  ,				_A )     ->       List[Any]:
				os.makedirs(_A  ,				exist_ok=_A )
				hf_hub_download(repo_id=_A  ,				filename=_A  ,				local_dir=_A )


def      UpperCamelCase       ( _A  ,				_A  ,				_A=False  ,				_A="text" )     ->       List[Any]:
				if model_type == "text":
								lowercase    :      Any       = BarkSemanticModel
								lowercase    :      List[Any]       = BarkSemanticConfig
								lowercase    :      List[Any]       = BarkSemanticGenerationConfig
				elif model_type == "coarse":
								lowercase    :      Tuple       = BarkCoarseModel
								lowercase    :      str       = BarkCoarseConfig
								lowercase    :      Optional[int]       = BarkCoarseGenerationConfig
				elif model_type == "fine":
								lowercase    :      str       = BarkFineModel
								lowercase    :      Optional[Any]       = BarkFineConfig
								lowercase    :      Optional[int]       = BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				lowercase    :      Optional[int]       = F"""{model_type}_small""" if use_small else model_type
				lowercase    :      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"""] )
				lowercase    :      Dict       = torch.load(_A  ,				map_location=_A )
				# this is a hack
				lowercase    :      int       = checkpoint["""model_args"""]
				if "input_vocab_size" not in model_args:
								lowercase    :      str       = model_args["""vocab_size"""]
								lowercase    :      Optional[Any]       = model_args["""vocab_size"""]
								del model_args["vocab_size"]

				# convert Bark model arguments to HF Bark model arguments
				lowercase    :      Optional[int]       = model_args.pop("""n_head""" )
				lowercase    :      Union[str, Any]       = model_args.pop("""n_embd""" )
				lowercase    :      Dict       = model_args.pop("""n_layer""" )

				lowercase    :      List[str]       = ConfigClass(**checkpoint["""model_args"""] )
				lowercase    :      Dict       = ModelClass(config=_A )
				lowercase    :      List[str]       = GenerationConfigClass()

				lowercase    :      int       = model_generation_config
				lowercase    :      List[Any]       = checkpoint["""model"""]
				# fixup checkpoint
				lowercase    :      Union[str, Any]       = """_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
												lowercase    :      List[str]       = k[len(_A ) :]
												for old_layer_name in new_layer_name_dict:
																lowercase    :      int       = new_k.replace(_A  ,				new_layer_name_dict[old_layer_name] )

												lowercase    :      List[Any]       = state_dict.pop(_A )

				lowercase    :      Dict       = set(state_dict.keys() ) - set(model.state_dict().keys() )
				lowercase    :      Dict       = {k for k in extra_keys if not k.endswith(""".attn.bias""" )}
				lowercase    :      Optional[int]       = set(model.state_dict().keys() ) - set(state_dict.keys() )
				lowercase    :      Dict       = {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 )
				lowercase    :      Union[str, Any]       = model.num_parameters(exclude_embeddings=_A )
				lowercase    :      Union[str, 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      UpperCamelCase       ( _A  ,				_A=False  ,				_A="text" )     ->       Dict:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				lowercase    :      Optional[Any]       = """cpu"""  # do conversion on cpu

				lowercase    :      Dict       = _get_ckpt_path(_A  ,				use_small=_A )
				lowercase    :      Any       = _load_model(_A  ,				_A  ,				model_type=_A  ,				use_small=_A )

				# load bark initial model
				lowercase    :      Tuple       = _bark_load_model(_A  ,				"""cpu"""  ,				model_type=_A  ,				use_small=_A )

				if model_type == "text":
								lowercase    :      str       = 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
				lowercase    :      Union[str, Any]       = 5
				lowercase    :      Tuple       = 10

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

								lowercase    :      Tuple       = model(_A )

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

				else:
								lowercase    :      Dict       = 3
								lowercase    :      Any       = 8
								lowercase    :      str       = torch.randint(256  ,				(batch_size, sequence_length, n_codes_total)  ,				dtype=torch.int )

								lowercase    :      str       = model(_A  ,				_A )
								lowercase    :      List[Any]       = bark_model(_A  ,				_A )

								lowercase    :      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      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A  ,				)     ->       Optional[int]:
				lowercase    :      str       = os.path.join(_A  ,				_A )

				lowercase    :      Dict       = BarkSemanticConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = BarkCoarseConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = BarkFineConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )

				lowercase    :      Any       = BarkSemanticModel.from_pretrained(_A )
				lowercase    :      str       = BarkCoarseModel.from_pretrained(_A )
				lowercase    :      int       = BarkFineModel.from_pretrained(_A )
				lowercase    :      Optional[Any]       = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )

				lowercase    :      int       = BarkConfig.from_sub_model_configs(
				    _A  ,				_A  ,				_A  ,				_A )

				lowercase    :      Dict       = BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config  ,				coarseAcoustic.generation_config  ,				fineAcoustic.generation_config )

				lowercase    :      str       = BarkModel(_A )

				lowercase    :      int       = semantic
				lowercase    :      List[Any]       = coarseAcoustic
				lowercase    :      Union[str, Any]       = fineAcoustic
				lowercase    :      Any       = codec

				lowercase    :      int       = bark_generation_config

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


if __name__ == "__main__":
				_lowerCAmelCase            =	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.')

				_lowerCAmelCase            =	parser.parse_args()

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