




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__			:       Optional[int]         =							logging.get_logger(__name__)

set_seed(770)


lowerCamelCase__			:       List[str]         =							{
    '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__			:       Union[str, Any]         =							{
    '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__			:       Any         =							os.path.join(os.path.expanduser('~'), '.cache')
lowerCamelCase__			:       List[Any]         =							os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Tuple      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     List[str]=False      ,      __UpperCAmelCase						:     Optional[Any]="text"							) ->			Any:
						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						:     str      ,      __UpperCAmelCase						:     Union[str, Any]=False      ,      __UpperCAmelCase						:     int="text"							) ->			str:
						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						:     List[Any]      ,      __UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Tuple      ,      ) ->			Optional[Any]:
						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__			:       Tuple         =							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__			:       str         =							parser.parse_args()

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





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor





class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def __init__(							self  :		str    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Any=7    ,    _lowerCAmelCase  :		Tuple=3    ,    _lowerCAmelCase  :		List[str]=18    ,    _lowerCAmelCase  :		Any=30    ,    _lowerCAmelCase  :		List[Any]=400    ,    _lowerCAmelCase  :		Dict=True    ,    _lowerCAmelCase  :		Tuple=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		List[Any]=[0.5, 0.5, 0.5]    ,    _lowerCAmelCase  :		Union[str, Any]=[0.5, 0.5, 0.5]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      size if size is not None else {'shortest_edge': 18}
       SCREAMING_SNAKE_CASE_     						=      crop_size if crop_size is not None else {'height': 18, 'width': 18}
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      image_size
       SCREAMING_SNAKE_CASE_     						=      min_resolution
       SCREAMING_SNAKE_CASE_     						=      max_resolution
       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      size
       SCREAMING_SNAKE_CASE_     						=      do_center_crop
       SCREAMING_SNAKE_CASE_     						=      crop_size
       SCREAMING_SNAKE_CASE_     						=      do_normalize
       SCREAMING_SNAKE_CASE_     						=      image_mean
       SCREAMING_SNAKE_CASE_     						=      image_std




 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }





@require_torch
@require_vision
class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   LevitImageProcessor if is_vision_available() else None



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      LevitImageProcessingTester(self						)



 @property
 def 				lowerCAmelCase_  (							self  :		Dict						):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_mean'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'image_std'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_normalize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_resize'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'do_center_crop'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'size'						)						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 18}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 18, 'width': 18}						)

       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict    ,    size=42    ,    crop_size=84						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 42}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 84, 'width': 84}						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       pass



 def 				lowerCAmelCase_  (							self  :		int						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    Image.Image						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )



 def 				lowerCAmelCase_  (							self  :		str						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase    ,    numpify=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    np.ndarray						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )




 def 				lowerCAmelCase_  (							self  :		Tuple						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase    ,    torchify=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    torch.Tensor						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      image_processing(image_inputs[0]    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )

       # Test batched
       SCREAMING_SNAKE_CASE_     						=      image_processing(_lowerCAmelCase    ,    return_tensors='pt'						).pixel_values
       self.assertEqual(
           encoded_images.shape    ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )    ,    )