




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__ :   Tuple					=							logging.get_logger(__name__)

set_seed(770)


lowerCamelCase__ :   List[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',
}


lowerCamelCase__ :   Dict					=							{
    '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__ :   Dict					=							os.path.dirname(os.path.abspath(__file__))
lowerCamelCase__ :   Tuple					=							os.path.join(os.path.expanduser('~'), '.cache')
lowerCamelCase__ :   Optional[Any]					=							os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]			,			__UpperCAmelCase						:	Optional[Any]=False							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			model_type
      if use_small:
            key += "_small"
      return os.path.join(__UpperCAmelCase			,			REMOTE_MODEL_PATHS[key]['file_name']							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	Dict							)					-> List[Any]:
      os.makedirs(__UpperCAmelCase			,			exist_ok=__UpperCAmelCase							)
      hf_hub_download(repo_id=__UpperCAmelCase			,			filename=__UpperCAmelCase			,			local_dir=__UpperCAmelCase							)

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

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

      SCREAMING_SNAKE_CASE_							=			ConfigClass(**checkpoint['model_args']							)
      SCREAMING_SNAKE_CASE_							=			ModelClass(config=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			GenerationConfigClass()

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

                  SCREAMING_SNAKE_CASE_							=			state_dict.pop(__UpperCAmelCase							)

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

      SCREAMING_SNAKE_CASE_							=			'cpu'  # do conversion on cpu

      SCREAMING_SNAKE_CASE_							=			_get_ckpt_path(__UpperCAmelCase			,			use_small=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			_load_model(__UpperCAmelCase			,			__UpperCAmelCase			,			model_type=__UpperCAmelCase			,			use_small=__UpperCAmelCase							)

      # load bark initial model
      SCREAMING_SNAKE_CASE_							=			_bark_load_model(__UpperCAmelCase			,			'cpu'			,			model_type=__UpperCAmelCase			,			use_small=__UpperCAmelCase							)

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

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

            SCREAMING_SNAKE_CASE_							=			model(__UpperCAmelCase							)

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

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

            SCREAMING_SNAKE_CASE_							=			model(__UpperCAmelCase			,			__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_							=			bark_model(__UpperCAmelCase			,			__UpperCAmelCase							)

            SCREAMING_SNAKE_CASE_							=			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						:	Tuple			,			__UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	Optional[int]			,			__UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Optional[int]			,			__UpperCAmelCase						:	Optional[int]			,			)					-> Optional[int]:
      SCREAMING_SNAKE_CASE_							=			os.path.join(__UpperCAmelCase			,			__UpperCAmelCase							)

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

      SCREAMING_SNAKE_CASE_							=			BarkSemanticModel.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			BarkCoarseModel.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			BarkFineModel.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			EncodecModel.from_pretrained('facebook/encodec_24khz'							)

      SCREAMING_SNAKE_CASE_							=			BarkConfig.from_sub_model_configs(
          __UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase			,			__UpperCAmelCase							)

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

      SCREAMING_SNAKE_CASE_							=			BarkModel(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_							=			semantic
      SCREAMING_SNAKE_CASE_							=			coarseAcoustic
      SCREAMING_SNAKE_CASE_							=			fineAcoustic
      SCREAMING_SNAKE_CASE_							=			codec

      SCREAMING_SNAKE_CASE_							=			bark_generation_config

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


if __name__ == "__main__":
   lowerCamelCase__ :   Dict					=							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__ :   Dict					=							parser.parse_args()

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase__ :   Union[str, Any]					=							{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Any					=							['LayoutLMv2TokenizerFast']

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Dict					=							['LayoutLMv2FeatureExtractor']
   lowerCamelCase__ :   Dict					=							['LayoutLMv2ImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Union[str, Any]					=							[
       'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'LayoutLMv2ForQuestionAnswering',
       'LayoutLMv2ForSequenceClassification',
       'LayoutLMv2ForTokenClassification',
       'LayoutLMv2Layer',
       'LayoutLMv2Model',
       'LayoutLMv2PreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

   lowerCamelCase__ :   Any					=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)