

'''simple docstring'''

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowercase__         =      [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]




def 				UpperCamelCase(					UpperCAmelCase_     ) ->    Dict:
 for pegasus_name, hf_name in PATTERNS:
  UpperCAmelCase	:     Dict						 =			k.replace(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return k




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ) ->    List[Any]:
 UpperCAmelCase	:     Dict						 =			DEFAULTS.copy()
 cfg_kwargs.update(UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			PegasusConfig(**UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			PegasusForConditionalGeneration(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			torch_model.model.state_dict()
 UpperCAmelCase	:     Tuple						 =			{}
 for k, v in tf_weights.items():
  UpperCAmelCase	:     List[Any]						 =			rename_state_dict_key(UpperCAmelCase_     )
  if new_k not in sd:
   raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""     )

  if "dense" in k or "proj" in new_k:
   UpperCAmelCase	:     List[Any]						 =			v.T
  UpperCAmelCase	:     Optional[int]						 =			torch.tensor(UpperCAmelCase_  ,	dtype=sd[new_k].dtype     )
  assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
 # make sure embedding.padding_idx is respected
 UpperCAmelCase	:     List[str]						 =			torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]     )
 UpperCAmelCase	:     Optional[Any]						 =			mapping['shared.weight']
 UpperCAmelCase	:     Any						 =			mapping['shared.weight']
 UpperCAmelCase	:     Dict						 =			{k: torch.zeros_like(UpperCAmelCase_     ) for k, v in sd.items() if k.endswith('bias'     ) and k not in mapping}
 mapping.update(**UpperCAmelCase_     )
 UpperCAmelCase	:     Dict						 =			torch_model.model.load_state_dict(UpperCAmelCase_  ,	strict=UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			[
     k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
 ]
 assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
 assert extra == [], F"""no matches found for the following tf keys {extra}"""
 return torch_model




def 				UpperCamelCase(					UpperCAmelCase_="./ckpt/aeslc/model.ckpt-32000"     ) ->    Any:
 UpperCAmelCase	:     List[str]						 =			tf.train.list_variables(UpperCAmelCase_     )
 UpperCAmelCase	:     List[Any]						 =			{}
 UpperCAmelCase	:     Dict						 =			['Adafactor', 'global_step']
 for name, shape in tqdm(UpperCAmelCase_  ,	desc='converting tf checkpoint to dict'     ):
  UpperCAmelCase	:     str						 =			any(pat in name for pat in ignore_name     )
  if skip_key:
   continue
  UpperCAmelCase	:     Any						 =			tf.train.load_variable(UpperCAmelCase_  ,	UpperCAmelCase_     )
  UpperCAmelCase	:     int						 =			array
 return tf_weights




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ) ->    Union[str, Any]:
 # save tokenizer first
 UpperCAmelCase	:     Dict						 =			Path(UpperCAmelCase_     ).parent.name
 UpperCAmelCase	:     Dict						 =			task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
 UpperCAmelCase	:     Tuple						 =			PegasusTokenizer.from_pretrained('sshleifer/pegasus'  ,	model_max_length=UpperCAmelCase_     )
 assert tok.model_max_length == desired_max_model_length
 tok.save_pretrained(UpperCAmelCase_     )

 # convert model
 UpperCAmelCase	:     Any						 =			get_tf_weights_as_numpy(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			task_specific_params[F"""summarization_{dataset}"""]
 if dataset == "large":
  UpperCAmelCase	:     List[str]						 =			task_specific_params
 UpperCAmelCase	:     Union[str, Any]						 =			convert_pegasus(UpperCAmelCase_  ,	UpperCAmelCase_     )
 torch_model.save_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     List[Any]						 =			torch_model.state_dict()
 sd.pop('model.decoder.embed_positions.weight'     )
 sd.pop('model.encoder.embed_positions.weight'     )
 torch.save(UpperCAmelCase_  ,	Path(UpperCAmelCase_     ) / 'pytorch_model.bin'     )


if __name__ == "__main__":
 lowercase__         =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
 parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
 lowercase__         =      parser.parse_args()
 if args.save_dir is None:
  lowercase__         =      Path(args.tf_ckpt_path).parent.name
  lowercase__         =      os.path.join("pegasus", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)







'''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()
lowercase__         =      logging.get_logger(__name__)

set_seed(770)


lowercase__         =      {
    "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",
}


lowercase__         =      {
    "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",
    },
}

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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False     ):
 UpperCAmelCase	:     List[str]						 =			model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCAmelCase_  ,	REMOTE_MODEL_PATHS[key]['file_name']     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 os.makedirs(UpperCAmelCase_  ,	exist_ok=UpperCAmelCase_     )
 hf_hub_download(repo_id=UpperCAmelCase_  ,	filename=UpperCAmelCase_  ,	local_dir=UpperCAmelCase_     )




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

 # convert Bark model arguments to HF Bark model arguments
 UpperCAmelCase	:     Any						 =			model_args.pop('n_head'     )
 UpperCAmelCase	:     Optional[Any]						 =			model_args.pop('n_embd'     )
 UpperCAmelCase	:     Union[str, Any]						 =			model_args.pop('n_layer'     )

 UpperCAmelCase	:     List[Any]						 =			ConfigClass(**checkpoint['model_args']     )
 UpperCAmelCase	:     List[str]						 =			ModelClass(config=UpperCAmelCase_     )
 UpperCAmelCase	:     List[str]						 =			GenerationConfigClass()

 UpperCAmelCase	:     Dict						 =			model_generation_config
 UpperCAmelCase	:     int						 =			checkpoint['model']
 # fixup checkpoint
 UpperCAmelCase	:     Tuple						 =			'_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	:     str						 =			k[len(UpperCAmelCase_     ) :]
   for old_layer_name in new_layer_name_dict:
    UpperCAmelCase	:     List[Any]						 =			new_k.replace(UpperCAmelCase_  ,	new_layer_name_dict[old_layer_name]     )

   UpperCAmelCase	:     List[Any]						 =			state_dict.pop(UpperCAmelCase_     )

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

 UpperCAmelCase	:     List[str]						 =			'cpu'  # do conversion on cpu

 UpperCAmelCase	:     List[str]						 =			_get_ckpt_path(UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			_load_model(UpperCAmelCase_  ,	UpperCAmelCase_  ,	model_type=UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )

 # load bark initial model
 UpperCAmelCase	:     List[str]						 =			_bark_load_model(UpperCAmelCase_  ,	'cpu'  ,	model_type=UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )

 if model_type == "text":
  UpperCAmelCase	:     Tuple						 =			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	:     Optional[int]						 =			5
 UpperCAmelCase	:     Optional[int]						 =			10

 if model_type in ["text", "coarse"]:
  UpperCAmelCase	:     List[Any]						 =			torch.randint(2_56  ,	(batch_size, sequence_length)  ,	dtype=torch.int     )
  UpperCAmelCase	:     Optional[Any]						 =			bark_model(UpperCAmelCase_     )[0]

  UpperCAmelCase	:     List[str]						 =			model(UpperCAmelCase_     )

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

 else:
  UpperCAmelCase	:     Optional[int]						 =			3
  UpperCAmelCase	:     List[Any]						 =			8
  UpperCAmelCase	:     Dict						 =			torch.randint(2_56  ,	(batch_size, sequence_length, n_codes_total)  ,	dtype=torch.int     )

  UpperCAmelCase	:     str						 =			model(UpperCAmelCase_  ,	UpperCAmelCase_     )
  UpperCAmelCase	:     Dict						 =			bark_model(UpperCAmelCase_  ,	UpperCAmelCase_     )

  UpperCAmelCase	:     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(UpperCAmelCase_     ).mkdir(exist_ok=UpperCAmelCase_     )
 model.save_pretrained(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	):
 UpperCAmelCase	:     int						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )

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

 UpperCAmelCase	:     Dict						 =			BarkSemanticModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Tuple						 =			BarkCoarseModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			BarkFineModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			EncodecModel.from_pretrained('facebook/encodec_24khz'     )

 UpperCAmelCase	:     Optional[Any]						 =			BarkConfig.from_sub_model_configs(
     UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

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

 UpperCAmelCase	:     str						 =			BarkModel(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			semantic
 UpperCAmelCase	:     Tuple						 =			coarseAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			fineAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			codec

 UpperCAmelCase	:     Optional[int]						 =			bark_generation_config

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


if __name__ == "__main__":
 lowercase__         =      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.")

 lowercase__         =      parser.parse_args()

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




