

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 			=		logging.get_logger(__name__)

set_seed(770)


_UpperCamelCase 			=		{
    "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 			=		{
    "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 			=		os.path.dirname(os.path.abspath(__file__))
_UpperCamelCase 			=		os.path.join(os.path.expanduser("~"), ".cache")
_UpperCamelCase 			=		os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")







def 		_lowercase (     lowercase__   ,	lowercase__=False						):
      __lowerCAmelCase					:						Dict					=					model_type
      if use_small:
            key += "_small"
      return os.path.join(lowercase__   ,	REMOTE_MODEL_PATHS[key]['''file_name''']						)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      os.makedirs(lowercase__   ,	exist_ok=lowercase__						)
      hf_hub_download(repo_id=lowercase__   ,	filename=lowercase__   ,	local_dir=lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=False   ,	lowercase__="text"						):
      if model_type == "text":
            __lowerCAmelCase					:						str					=					BarkSemanticModel
            __lowerCAmelCase					:						Union[str, Any]					=					BarkSemanticConfig
            __lowerCAmelCase					:						int					=					BarkSemanticGenerationConfig
      elif model_type == "coarse":
            __lowerCAmelCase					:						str					=					BarkCoarseModel
            __lowerCAmelCase					:						List[Any]					=					BarkCoarseConfig
            __lowerCAmelCase					:						List[Any]					=					BarkCoarseGenerationConfig
      elif model_type == "fine":
            __lowerCAmelCase					:						Union[str, Any]					=					BarkFineModel
            __lowerCAmelCase					:						Tuple					=					BarkFineConfig
            __lowerCAmelCase					:						Any					=					BarkFineGenerationConfig
      else:
            raise NotImplementedError()
      __lowerCAmelCase					:						Tuple					=					f"""{model_type}_small""" if use_small else model_type
      __lowerCAmelCase					:						Optional[int]					=					REMOTE_MODEL_PATHS[model_key]
      if not os.path.exists(lowercase__						):
            logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""						)
            _download(model_info['''repo_id''']   ,	model_info['''file_name''']						)
      __lowerCAmelCase					:						Tuple					=					torch.load(lowercase__   ,	map_location=lowercase__						)
      # this is a hack
      __lowerCAmelCase					:						Optional[Any]					=					checkpoint['''model_args''']
      if "input_vocab_size" not in model_args:
            __lowerCAmelCase					:						Optional[int]					=					model_args['''vocab_size''']
            __lowerCAmelCase					:						Dict					=					model_args['''vocab_size''']
            del model_args["vocab_size"]

      # convert Bark model arguments to HF Bark model arguments
      __lowerCAmelCase					:						int					=					model_args.pop('''n_head'''						)
      __lowerCAmelCase					:						str					=					model_args.pop('''n_embd'''						)
      __lowerCAmelCase					:						Any					=					model_args.pop('''n_layer'''						)

      __lowerCAmelCase					:						Dict					=					ConfigClass(**checkpoint['''model_args''']						)
      __lowerCAmelCase					:						str					=					ModelClass(config=lowercase__						)
      __lowerCAmelCase					:						Optional[int]					=					GenerationConfigClass()

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

                  __lowerCAmelCase					:						str					=					state_dict.pop(lowercase__						)

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

      return model







def 		_lowercase (     lowercase__   ,	lowercase__=False   ,	lowercase__="text"						):
      if model_type not in ("text", "coarse", "fine"):
            raise NotImplementedError()

      __lowerCAmelCase					:						Any					=					'''cpu'''  # do conversion on cpu

      __lowerCAmelCase					:						int					=					_get_ckpt_path(lowercase__   ,	use_small=lowercase__						)
      __lowerCAmelCase					:						List[str]					=					_load_model(lowercase__   ,	lowercase__   ,	model_type=lowercase__   ,	use_small=lowercase__						)

      # load bark initial model
      __lowerCAmelCase					:						str					=					_bark_load_model(lowercase__   ,	'''cpu'''   ,	model_type=lowercase__   ,	use_small=lowercase__						)

      if model_type == "text":
            __lowerCAmelCase					:						Optional[Any]					=					bark_model['''model''']

      if model.num_parameters(exclude_embeddings=lowercase__						) != 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					:						List[Any]					=					5
      __lowerCAmelCase					:						Tuple					=					1_0

      if model_type in ["text", "coarse"]:
            __lowerCAmelCase					:						Dict					=					torch.randint(2_5_6   ,	(batch_size, sequence_length)   ,	dtype=torch.int						)
            __lowerCAmelCase					:						Any					=					bark_model(lowercase__						)[0]

            __lowerCAmelCase					:						Tuple					=					model(lowercase__						)

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

      else:
            __lowerCAmelCase					:						List[str]					=					3
            __lowerCAmelCase					:						int					=					8
            __lowerCAmelCase					:						List[str]					=					torch.randint(2_5_6   ,	(batch_size, sequence_length, n_codes_total)   ,	dtype=torch.int						)

            __lowerCAmelCase					:						int					=					model(lowercase__   ,	lowercase__						)
            __lowerCAmelCase					:						Union[str, Any]					=					bark_model(lowercase__   ,	lowercase__						)

            __lowerCAmelCase					:						List[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(lowercase__						).mkdir(exist_ok=lowercase__						)
      model.save_pretrained(lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	):
      __lowerCAmelCase					:						str					=					os.path.join(lowercase__   ,	lowercase__						)

      __lowerCAmelCase					:						Union[str, Any]					=					BarkSemanticConfig.from_pretrained(os.path.join(lowercase__   ,	'''config.json'''						)						)
      __lowerCAmelCase					:						List[str]					=					BarkCoarseConfig.from_pretrained(os.path.join(lowercase__   ,	'''config.json'''						)						)
      __lowerCAmelCase					:						List[str]					=					BarkFineConfig.from_pretrained(os.path.join(lowercase__   ,	'''config.json'''						)						)
      __lowerCAmelCase					:						Any					=					EncodecConfig.from_pretrained('''facebook/encodec_24khz'''						)

      __lowerCAmelCase					:						Any					=					BarkSemanticModel.from_pretrained(lowercase__						)
      __lowerCAmelCase					:						List[Any]					=					BarkCoarseModel.from_pretrained(lowercase__						)
      __lowerCAmelCase					:						Optional[int]					=					BarkFineModel.from_pretrained(lowercase__						)
      __lowerCAmelCase					:						Union[str, Any]					=					EncodecModel.from_pretrained('''facebook/encodec_24khz'''						)

      __lowerCAmelCase					:						Any					=					BarkConfig.from_sub_model_configs(
          lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						)

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

      __lowerCAmelCase					:						Optional[Any]					=					BarkModel(lowercase__						)

      __lowerCAmelCase					:						str					=					semantic
      __lowerCAmelCase					:						Dict					=					coarseAcoustic
      __lowerCAmelCase					:						int					=					fineAcoustic
      __lowerCAmelCase					:						Tuple					=					codec

      __lowerCAmelCase					:						Optional[Any]					=					bark_generation_config

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


if __name__ == "__main__":
 _UpperCamelCase 			=		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 			=		parser.parse_args()

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





import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_UpperCamelCase 			=		random.Random()







def 		_lowercase (     lowercase__   ,	lowercase__=1.0   ,	lowercase__=None   ,	lowercase__=None						):
      if rng is None:
            __lowerCAmelCase					:						Optional[Any]					=					global_rng

      __lowerCAmelCase					:						Tuple					=					[]
      for batch_idx in range(shape[0]						):
            values.append([]						)
            for _ in range(shape[1]						):
                  values[-1].append(rng.random() * scale						)

      return values







class   __lowercase      (unittest.TestCase ):
 def __init__(							self      ,							A_      ,							A_=7      ,							A_=400      ,							A_=2000      ,							A_=1      ,							A_=0.0      ,							A_=1_6000      ,							A_=True      ,							A_=True      ,							)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					parent
       __lowerCAmelCase					:						Optional[int]					=					batch_size
       __lowerCAmelCase					:						Any					=					min_seq_length
       __lowerCAmelCase					:						Tuple					=					max_seq_length
       __lowerCAmelCase					:						Tuple					=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       __lowerCAmelCase					:						Dict					=					feature_size
       __lowerCAmelCase					:						Optional[int]					=					padding_value
       __lowerCAmelCase					:						Tuple					=					sampling_rate
       __lowerCAmelCase					:						Union[str, Any]					=					return_attention_mask
       __lowerCAmelCase					:						Dict					=					do_normalize
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       return {
           "feature_size": self.feature_size,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
           "do_normalize": self.do_normalize,
       }





 def 	UpperCamelCase__  (							self      ,							A_=False      ,							A_=False					)					->Union[str, Any]:



       '''simple docstring'''





       def _flatten(A_					):
             return list(itertools.chain(*A_					)					)

       if equal_length:
             __lowerCAmelCase					:						Dict					=					floats_list((self.batch_size, self.max_seq_length)					)
       else:
             # make sure that inputs increase in size
             __lowerCAmelCase					:						Tuple					=					[
                 _flatten(floats_list((x, self.feature_size)					)					)
                 for x in range(self.min_seq_length      ,							self.max_seq_length      ,							self.seq_length_diff					)
             ]

       if numpify:
             __lowerCAmelCase					:						Tuple					=					[np.asarray(A_					) for x in speech_inputs]

       return speech_inputs







class   __lowercase      (_UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  WavaVecaFeatureExtractor
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					WavaVecaFeatureExtractionTester(self					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[Any]:



       '''simple docstring'''





       self.assertTrue(np.all(np.mean(A_      ,							axis=0					) < 1e-3					)					)
       self.assertTrue(np.all(np.abs(np.var(A_      ,							axis=0					) - 1					) < 1e-3					)					)
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       # create three inputs of length 800, 1000, and 1200
       __lowerCAmelCase					:						Optional[Any]					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						Any					=					[np.asarray(A_					) for speech_input in speech_inputs]

       # Test not batched input
       __lowerCAmelCase					:						Optional[Any]					=					feat_extract(speech_inputs[0]      ,							return_tensors='''np'''					).input_values
       __lowerCAmelCase					:						Dict					=					feat_extract(np_speech_inputs[0]      ,							return_tensors='''np'''					).input_values
       self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)

       # Test batched
       __lowerCAmelCase					:						Dict					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       __lowerCAmelCase					:						Dict					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       for enc_seq_a, enc_seq_a in zip(A_      ,							A_					):
             self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)

       # Test 2-D numpy arrays are batched.
       __lowerCAmelCase					:						int					=					[floats_list((1, x)					)[0] for x in (800, 800, 800)]
       __lowerCAmelCase					:						List[Any]					=					np.asarray(A_					)
       __lowerCAmelCase					:						Any					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       __lowerCAmelCase					:						Union[str, Any]					=					feat_extract(A_      ,							return_tensors='''np'''					).input_values
       for enc_seq_a, enc_seq_a in zip(A_      ,							A_					):
             self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						str					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]

       __lowerCAmelCase					:						str					=					['''longest''', '''max_length''', '''do_not_pad''']
       __lowerCAmelCase					:						str					=					[None, 1600, None]
       for max_length, padding in zip(A_      ,							A_					):
             __lowerCAmelCase					:						Optional[int]					=					feat_extract(A_      ,							padding=A_      ,							max_length=A_      ,							return_tensors='''np'''					)
             __lowerCAmelCase					:						Optional[Any]					=					processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]					)
             self.assertTrue(input_values[0][800:].sum() < 1e-6					)
             self._check_zero_mean_unit_variance(input_values[1][:1000]					)
             self.assertTrue(input_values[0][1000:].sum() < 1e-6					)
             self._check_zero_mean_unit_variance(input_values[2][:1200]					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						Optional[int]					=					range(800      ,							1400      ,							200					)
       __lowerCAmelCase					:						Union[str, Any]					=					[floats_list((1, x)					)[0] for x in lengths]

       __lowerCAmelCase					:						int					=					['''longest''', '''max_length''', '''do_not_pad''']
       __lowerCAmelCase					:						List[str]					=					[None, 1600, None]

       for max_length, padding in zip(A_      ,							A_					):
             __lowerCAmelCase					:						Union[str, Any]					=					feat_extract(A_      ,							max_length=A_      ,							padding=A_					)
             __lowerCAmelCase					:						Union[str, Any]					=					processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]					)
             self._check_zero_mean_unit_variance(input_values[1][:1000]					)
             self._check_zero_mean_unit_variance(input_values[2][:1200]					)
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						Optional[Any]					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						List[str]					=					feat_extract(
           A_      ,							truncation=A_      ,							max_length=1000      ,							padding='''max_length'''      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						int					=					processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]					)
       self._check_zero_mean_unit_variance(input_values[1]					)
       self._check_zero_mean_unit_variance(input_values[2]					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						int					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						int					=					feat_extract(
           A_      ,							truncation=A_      ,							max_length=1000      ,							padding='''longest'''      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						Optional[Any]					=					processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]					)
       self._check_zero_mean_unit_variance(input_values[1, :1000]					)
       self._check_zero_mean_unit_variance(input_values[2]					)

       # make sure that if max_length < longest -> then pad to max_length
       self.assertTrue(input_values.shape == (3, 1000)					)

       __lowerCAmelCase					:						Any					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						Optional[int]					=					feat_extract(
           A_      ,							truncation=A_      ,							max_length=2000      ,							padding='''longest'''      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						List[str]					=					processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]					)
       self._check_zero_mean_unit_variance(input_values[1, :1000]					)
       self._check_zero_mean_unit_variance(input_values[2]					)

       # make sure that if max_length > longest -> then pad to longest
       self.assertTrue(input_values.shape == (3, 1200)					)
 @require_torch
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						Union[str, Any]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						Any					=					np.random.rand(100					).astype(np.floataa					)
       __lowerCAmelCase					:						List[Any]					=					np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
             __lowerCAmelCase					:						Any					=					feature_extractor.pad([{'''input_values''': inputs}]      ,							return_tensors='''np'''					)
             self.assertTrue(np_processed.input_values.dtype == np.floataa					)
             __lowerCAmelCase					:						List[str]					=					feature_extractor.pad([{'''input_values''': inputs}]      ,							return_tensors='''pt'''					)
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)





 @slow
 @require_torch
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
             __lowerCAmelCase					:						Any					=					WavaVecaConfig.from_pretrained(A_					)
             __lowerCAmelCase					:						Tuple					=					WavaVecaFeatureExtractor.from_pretrained(A_					)

             # only "layer" feature extraction norm should make use of
             # attention_mask
             self.assertEqual(feat_extract.return_attention_mask      ,							config.feat_extract_norm == '''layer'''					)


