




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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any	,					__UpperCamelCase      :						int				)  ->							Union[str, Any]:
				os.makedirs(__UpperCamelCase	,					exist_ok=__UpperCamelCase				)
				hf_hub_download(repo_id=__UpperCamelCase	,					filename=__UpperCamelCase	,					local_dir=__UpperCamelCase				)




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

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

				UpperCAmelCase_										=					ConfigClass(**checkpoint['''model_args''']				)
				UpperCAmelCase_										=					ModelClass(config=__UpperCamelCase				)
				UpperCAmelCase_										=					GenerationConfigClass()

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

												UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)

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

				UpperCAmelCase_										=					'''cpu'''  # do conversion on cpu

				UpperCAmelCase_										=					_get_ckpt_path(__UpperCamelCase	,					use_small=__UpperCamelCase				)
				UpperCAmelCase_										=					_load_model(__UpperCamelCase	,					__UpperCamelCase	,					model_type=__UpperCamelCase	,					use_small=__UpperCamelCase				)

				# load bark initial model
				UpperCAmelCase_										=					_bark_load_model(__UpperCamelCase	,					'''cpu'''	,					model_type=__UpperCamelCase	,					use_small=__UpperCamelCase				)

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

				if model_type in ["text", "coarse"]:
								UpperCAmelCase_										=					torch.randint(256	,					(batch_size, sequence_length)	,					dtype=torch.int				)
								UpperCAmelCase_										=					bark_model(__UpperCamelCase				)[0]

								UpperCAmelCase_										=					model(__UpperCamelCase				)

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

				else:
								UpperCAmelCase_										=					3
								UpperCAmelCase_										=					8
								UpperCAmelCase_										=					torch.randint(256	,					(batch_size, sequence_length, n_codes_total)	,					dtype=torch.int				)

								UpperCAmelCase_										=					model(__UpperCamelCase	,					__UpperCamelCase				)
								UpperCAmelCase_										=					bark_model(__UpperCamelCase	,					__UpperCamelCase				)

								UpperCAmelCase_										=					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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						int	,					)  ->							Optional[Any]:
				UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)

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

				UpperCAmelCase_										=					BarkSemanticModel.from_pretrained(__UpperCamelCase				)
				UpperCAmelCase_										=					BarkCoarseModel.from_pretrained(__UpperCamelCase				)
				UpperCAmelCase_										=					BarkFineModel.from_pretrained(__UpperCamelCase				)
				UpperCAmelCase_										=					EncodecModel.from_pretrained('''facebook/encodec_24khz'''				)

				UpperCAmelCase_										=					BarkConfig.from_sub_model_configs(
				    __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)

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

				UpperCAmelCase_										=					BarkModel(__UpperCamelCase				)

				UpperCAmelCase_										=					semantic
				UpperCAmelCase_										=					coarseAcoustic
				UpperCAmelCase_										=					fineAcoustic
				UpperCAmelCase_										=					codec

				UpperCAmelCase_										=					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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase      			=						{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['ConditionalDetrFeatureExtractor']
 _lowerCamelCase      			=						['ConditionalDetrImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
     'ConditionalDetrForObjectDetection',
     'ConditionalDetrForSegmentation',
     'ConditionalDetrModel',
     'ConditionalDetrPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase      			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





