






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(770)


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      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Dict=False   ):
				A	 =      model_type
				if use_small:
								key += "_small"
				return os.path.join(UpperCAmelCase,						REMOTE_MODEL_PATHS[key]['file_name']   )







def      snake_case		(							UpperCAmelCase	:       Any,						UpperCAmelCase	:       Optional[Any]   ):
				os.makedirs(UpperCAmelCase,						exist_ok=UpperCAmelCase   )
				hf_hub_download(repo_id=UpperCAmelCase,						filename=UpperCAmelCase,						local_dir=UpperCAmelCase   )







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Dict=False,						UpperCAmelCase	:       Union[str, Any]="text"   ):
				if model_type == "text":
								A	 =      BarkSemanticModel
								A	 =      BarkSemanticConfig
								A	 =      BarkSemanticGenerationConfig
				elif model_type == "coarse":
								A	 =      BarkCoarseModel
								A	 =      BarkCoarseConfig
								A	 =      BarkCoarseGenerationConfig
				elif model_type == "fine":
								A	 =      BarkFineModel
								A	 =      BarkFineConfig
								A	 =      BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				A	 =      f'{model_type}_small' if use_small else model_type
				A	 =      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']   )
				A	 =      torch.load(UpperCAmelCase,						map_location=UpperCAmelCase   )
				# this is a hack
				A	 =      checkpoint['model_args']
				if "input_vocab_size" not in model_args:
								A	 =      model_args['vocab_size']
								A	 =      model_args['vocab_size']
								del model_args["vocab_size"]

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

				A	 =      ConfigClass(**checkpoint['model_args']   )
				A	 =      ModelClass(config=UpperCAmelCase   )
				A	 =      GenerationConfigClass()

				A	 =      model_generation_config
				A	 =      checkpoint['model']
				# fixup checkpoint
				A	 =      '_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
												A	 =      k[len(UpperCAmelCase   ) :]
												for old_layer_name in new_layer_name_dict:
																A	 =      new_k.replace(UpperCAmelCase,						new_layer_name_dict[old_layer_name]   )

												A	 =      state_dict.pop(UpperCAmelCase   )

				A	 =      set(state_dict.keys()   ) - set(model.state_dict().keys()   )
				A	 =      {k for k in extra_keys if not k.endswith('.attn.bias'   )}
				A	 =      set(model.state_dict().keys()   ) - set(state_dict.keys()   )
				A	 =      {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   )
				A	 =      model.num_parameters(exclude_embeddings=UpperCAmelCase   )
				A	 =      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      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       int=False,						UpperCAmelCase	:       List[Any]="text"   ):
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				A	 =      'cpu'  # do conversion on cpu

				A	 =      _get_ckpt_path(UpperCAmelCase,						use_small=UpperCAmelCase   )
				A	 =      _load_model(UpperCAmelCase,						UpperCAmelCase,						model_type=UpperCAmelCase,						use_small=UpperCAmelCase   )

				# load bark initial model
				A	 =      _bark_load_model(UpperCAmelCase,						'cpu',						model_type=UpperCAmelCase,						use_small=UpperCAmelCase   )

				if model_type == "text":
								A	 =      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
				A	 =      5
				A	 =      10

				if model_type in ["text", "coarse"]:
								A	 =      torch.randint(2_56,						(batch_size, sequence_length),						dtype=torch.int   )
								A	 =      bark_model(UpperCAmelCase   )[0]

								A	 =      model(UpperCAmelCase   )

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

				else:
								A	 =      3
								A	 =      8
								A	 =      torch.randint(2_56,						(batch_size, sequence_length, n_codes_total),						dtype=torch.int   )

								A	 =      model(UpperCAmelCase,						UpperCAmelCase   )
								A	 =      bark_model(UpperCAmelCase,						UpperCAmelCase   )

								A	 =      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      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Any,						UpperCAmelCase	:       List[str],						UpperCAmelCase	:       List[str],						):
				A	 =      os.path.join(UpperCAmelCase,						UpperCAmelCase   )

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

				A	 =      BarkSemanticModel.from_pretrained(UpperCAmelCase   )
				A	 =      BarkCoarseModel.from_pretrained(UpperCAmelCase   )
				A	 =      BarkFineModel.from_pretrained(UpperCAmelCase   )
				A	 =      EncodecModel.from_pretrained('facebook/encodec_24khz'   )

				A	 =      BarkConfig.from_sub_model_configs(
				    UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )

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

				A	 =      BarkModel(UpperCAmelCase   )

				A	 =      semantic
				A	 =      coarseAcoustic
				A	 =      fineAcoustic
				A	 =      codec

				A	 =      bark_generation_config

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


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)













from manim import *
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def       A(  self		:       Dict		)    ->   Tuple:


						'''simple docstring'''
						A	 =      Rectangle(height=0.5						,width=0.5		)
						A	 =      Rectangle(height=0.46						,width=0.46		).set_stroke(width=0		)

						A	 =      [mem.copy() for i in range(6		)]
						A	 =      [mem.copy() for i in range(6		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('CPU'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						cpu.move_to([-2.5, -0.5, 0]		)
						self.add(_SCREAMING_SNAKE_CASE		)

						A	 =      [mem.copy() for i in range(1		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('GPU'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						gpu.align_to(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						gpu.set_x(gpu.get_x() - 1		)

						self.add(_SCREAMING_SNAKE_CASE		)

						A	 =      [mem.copy() for i in range(6		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)

						A	 =      Text('Model'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						model.move_to([3, -1.0, 0]		)

						self.play(
						    Create(_SCREAMING_SNAKE_CASE						,run_time=1		)						,Create(_SCREAMING_SNAKE_CASE						,run_time=1		)						,Create(_SCREAMING_SNAKE_CASE						,run_time=1		)						,)

						A	 =      MarkupText(
						    f'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'						,font_size=2_4						,)

						A	 =      Square(side_length=2.2		)
						key.move_to([-5, 2, 0]		)

						A	 =      MarkupText(
						    f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'						,font_size=1_8						,)

						key_text.move_to([-5, 2.4, 0]		)

						step_a.move_to([2, 2, 0]		)
						self.play(Write(_SCREAMING_SNAKE_CASE						,run_time=2.5		)						,Write(_SCREAMING_SNAKE_CASE		)						,Write(_SCREAMING_SNAKE_CASE		)		)

						self.add(_SCREAMING_SNAKE_CASE		)

						A	 =      []
						A	 =      []
						A	 =      []
						for i, rect in enumerate(_SCREAMING_SNAKE_CASE		):
										A	 =      Rectangle(height=0.46						,width=0.46		).set_stroke(width=0.0		).set_fill(_SCREAMING_SNAKE_CASE						,opacity=0.7		)
										cpu_target.move_to(_SCREAMING_SNAKE_CASE		)
										cpu_target.generate_target()
										A	 =      0.46 / 4
										A	 =      0.46 / 3

										if i == 0:
														cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)						,buff=0.02						,direction=_SCREAMING_SNAKE_CASE		)
														cpu_target.target.set_x(cpu_target.target.get_x() + 0.1		)
										elif i == 3:
														cpu_target.target.next_to(cpu_targs[0].target						,direction=_SCREAMING_SNAKE_CASE						,buff=0.0		)
										else:
														cpu_target.target.next_to(cpu_targs[i - 1].target						,direction=_SCREAMING_SNAKE_CASE						,buff=0.0		)
										cpu_targs.append(_SCREAMING_SNAKE_CASE		)

										first_animations.append(rect.animate(run_time=0.5		).set_stroke(_SCREAMING_SNAKE_CASE		)		)
										second_animations.append(MoveToTarget(_SCREAMING_SNAKE_CASE						,run_time=1.5		)		)

						self.play(*_SCREAMING_SNAKE_CASE		)
						self.play(*_SCREAMING_SNAKE_CASE		)

						self.wait()





