

'''simple docstring'''


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[Any]										=						logging.get_logger(__name__)

set_seed(770)


lowerCAmelCase__						:       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__						:       str										=						{
    """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__						:       Tuple										=						os.path.dirname(os.path.abspath(__file__))
lowerCAmelCase__						:       int										=						os.path.join(os.path.expanduser("""~"""), """.cache""")
lowerCAmelCase__						:       str										=						os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")




def 	_a       (				__lowerCAmelCase		:      List[str]   ,							__lowerCAmelCase		:      List[str]=False     ):

		"""simple docstring"""

		snake_case__						:       str       						=  model_type
		if use_small:
				key += "_small"
		return os.path.join(__lowerCAmelCase   ,							REMOTE_MODEL_PATHS[key]['''file_name''']     )




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Optional[int]     ):

		"""simple docstring"""

		os.makedirs(__lowerCAmelCase   ,							exist_ok=__lowerCAmelCase     )
		hf_hub_download(repo_id=__lowerCAmelCase   ,							filename=__lowerCAmelCase   ,							local_dir=__lowerCAmelCase     )




def 	_a       (				__lowerCAmelCase		:      Optional[Any]   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Optional[int]=False   ,							__lowerCAmelCase		:      List[str]="text"     ):

		"""simple docstring"""

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

		# convert Bark model arguments to HF Bark model arguments
		snake_case__						:       Optional[Any]       						=  model_args.pop('''n_head'''     )
		snake_case__						:       Optional[int]       						=  model_args.pop('''n_embd'''     )
		snake_case__						:       Optional[Any]       						=  model_args.pop('''n_layer'''     )

		snake_case__						:       str       						=  ConfigClass(**checkpoint['''model_args''']     )
		snake_case__						:       Any       						=  ModelClass(config=__lowerCAmelCase     )
		snake_case__						:       str       						=  GenerationConfigClass()

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

						snake_case__						:       Optional[int]       						=  state_dict.pop(__lowerCAmelCase     )

		snake_case__						:       Dict       						=  set(state_dict.keys()     ) - set(model.state_dict().keys()     )
		snake_case__						:       List[str]       						=  {k for k in extra_keys if not k.endswith('''.attn.bias'''     )}
		snake_case__						:       str       						=  set(model.state_dict().keys()     ) - set(state_dict.keys()     )
		snake_case__						:       Tuple       						=  {k for k in missing_keys if not k.endswith('''.attn.bias'''     )}
		if len(__lowerCAmelCase     ) != 0:
				raise ValueError(F"""extra keys found: {extra_keys}"""     )
		if len(__lowerCAmelCase     ) != 0:
				raise ValueError(F"""missing keys: {missing_keys}"""     )
		model.load_state_dict(__lowerCAmelCase   ,							strict=__lowerCAmelCase     )
		snake_case__						:       List[Any]       						=  model.num_parameters(exclude_embeddings=__lowerCAmelCase     )
		snake_case__						:       Tuple       						=  checkpoint["""best_val_loss"""].item()
		logger.info(F"""model loaded: {round(n_params/1e6   ,							1     )}M params, {round(__lowerCAmelCase   ,							3     )} loss"""     )
		model.eval()
		model.to(__lowerCAmelCase     )
		del checkpoint, state_dict

		return model




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Dict=False   ,							__lowerCAmelCase		:      str="text"     ):

		"""simple docstring"""

		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		snake_case__						:       Optional[int]       						=  """cpu"""  # do conversion on cpu

		snake_case__						:       Union[str, Any]       						=  _get_ckpt_path(__lowerCAmelCase   ,							use_small=__lowerCAmelCase     )
		snake_case__						:       Dict       						=  _load_model(__lowerCAmelCase   ,							__lowerCAmelCase   ,							model_type=__lowerCAmelCase   ,							use_small=__lowerCAmelCase     )

		# load bark initial model
		snake_case__						:       Union[str, Any]       						=  _bark_load_model(__lowerCAmelCase   ,							'''cpu'''   ,							model_type=__lowerCAmelCase   ,							use_small=__lowerCAmelCase     )

		if model_type == "text":
				snake_case__						:       Any       						=  bark_model["""model"""]

		if model.num_parameters(exclude_embeddings=__lowerCAmelCase     ) != 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
		snake_case__						:       List[str]       						=  5
		snake_case__						:       Any       						=  10

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

				snake_case__						:       Optional[Any]       						=  model(__lowerCAmelCase     )

				# take last logits
				snake_case__						:       List[str]       						=  output_new_model_total.logits[:, [-1], :]

		else:
				snake_case__						:       str       						=  3
				snake_case__						:       Any       						=  8
				snake_case__						:       Tuple       						=  torch.randint(2_56   ,							(batch_size, sequence_length, n_codes_total)   ,							dtype=torch.int     )

				snake_case__						:       Optional[int]       						=  model(__lowerCAmelCase   ,							__lowerCAmelCase     )
				snake_case__						:       List[Any]       						=  bark_model(__lowerCAmelCase   ,							__lowerCAmelCase     )

				snake_case__						:       Optional[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(__lowerCAmelCase     ).mkdir(exist_ok=__lowerCAmelCase     )
		model.save_pretrained(__lowerCAmelCase     )




def 	_a       (				__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[Any]   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Tuple   ,							):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  os.path.join(__lowerCAmelCase   ,							__lowerCAmelCase     )

		snake_case__						:       List[Any]       						=  BarkSemanticConfig.from_pretrained(os.path.join(__lowerCAmelCase   ,							'''config.json'''     )     )
		snake_case__						:       Optional[int]       						=  BarkCoarseConfig.from_pretrained(os.path.join(__lowerCAmelCase   ,							'''config.json'''     )     )
		snake_case__						:       Tuple       						=  BarkFineConfig.from_pretrained(os.path.join(__lowerCAmelCase   ,							'''config.json'''     )     )
		snake_case__						:       Optional[Any]       						=  EncodecConfig.from_pretrained('''facebook/encodec_24khz'''     )

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

		snake_case__						:       Optional[Any]       						=  BarkConfig.from_sub_model_configs(
		    __lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )

		snake_case__						:       Optional[int]       						=  BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config   ,							coarseAcoustic.generation_config   ,							fineAcoustic.generation_config     )

		snake_case__						:       List[Any]       						=  BarkModel(__lowerCAmelCase     )

		snake_case__						:       Union[str, Any]       						=  semantic
		snake_case__						:       int       						=  coarseAcoustic
		snake_case__						:       str       						=  fineAcoustic
		snake_case__						:       List[Any]       						=  codec

		snake_case__						:       str       						=  bark_generation_config

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


if __name__ == "__main__":
					lowerCAmelCase__						:       Optional[int]										=						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)






'''simple docstring'''


import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  BigBirdConfig.from_json_file(__lowerCAmelCase     )
		print(F"""Building PyTorch model from configuration: {config}"""     )

		if is_trivia_qa:
				snake_case__						:       str       						=  BigBirdForQuestionAnswering(__lowerCAmelCase     )
		else:
				snake_case__						:       Tuple       						=  BigBirdForPreTraining(__lowerCAmelCase     )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(__lowerCAmelCase   ,							__lowerCAmelCase   ,							is_trivia_qa=__lowerCAmelCase     )

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
		model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
					lowerCAmelCase__						:       Union[str, Any]										=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					lowerCAmelCase__						:       Optional[int]										=						parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)



