




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 				_A  (		lowerCAmelCase_	:   List[Any]  ,							lowerCAmelCase_	:   Tuple=False     ):
     """simple docstring"""

     lowerCAmelCase__            =					model_type
     if use_small:
          key += "_small"
     return os.path.join(lowerCAmelCase_  ,							REMOTE_MODEL_PATHS[key]["file_name"]     )



def 				_A  (		lowerCAmelCase_	:   int  ,							lowerCAmelCase_	:   List[Any]     ):
     """simple docstring"""

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



def 				_A  (		lowerCAmelCase_	:   Dict  ,							lowerCAmelCase_	:   Any  ,							lowerCAmelCase_	:   List[str]=False  ,							lowerCAmelCase_	:   Optional[Any]="text"     ):
     """simple docstring"""

     if model_type == "text":
          lowerCAmelCase__            =					BarkSemanticModel
          lowerCAmelCase__            =					BarkSemanticConfig
          lowerCAmelCase__            =					BarkSemanticGenerationConfig
     elif model_type == "coarse":
          lowerCAmelCase__            =					BarkCoarseModel
          lowerCAmelCase__            =					BarkCoarseConfig
          lowerCAmelCase__            =					BarkCoarseGenerationConfig
     elif model_type == "fine":
          lowerCAmelCase__            =					BarkFineModel
          lowerCAmelCase__            =					BarkFineConfig
          lowerCAmelCase__            =					BarkFineGenerationConfig
     else:
          raise NotImplementedError()
     lowerCAmelCase__            =					F'{model_type}_small' if use_small else model_type
     lowerCAmelCase__            =					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"]     )
     lowerCAmelCase__            =					torch.load(lowerCAmelCase_  ,							map_location=lowerCAmelCase_     )
     # this is a hack
     lowerCAmelCase__            =					checkpoint["model_args"]
     if "input_vocab_size" not in model_args:
          lowerCAmelCase__            =					model_args["vocab_size"]
          lowerCAmelCase__            =					model_args["vocab_size"]
          del model_args["vocab_size"]

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

     lowerCAmelCase__            =					ConfigClass(**checkpoint["model_args"]     )
     lowerCAmelCase__            =					ModelClass(config=lowerCAmelCase_     )
     lowerCAmelCase__            =					GenerationConfigClass()

     lowerCAmelCase__            =					model_generation_config
     lowerCAmelCase__            =					checkpoint["model"]
     # fixup checkpoint
     lowerCAmelCase__            =					"_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
               lowerCAmelCase__            =					k[len(lowerCAmelCase_     ) :]
               for old_layer_name in new_layer_name_dict:
                    lowerCAmelCase__            =					new_k.replace(lowerCAmelCase_  ,							new_layer_name_dict[old_layer_name]     )

               lowerCAmelCase__            =					state_dict.pop(lowerCAmelCase_     )

     lowerCAmelCase__            =					set(state_dict.keys()     ) - set(model.state_dict().keys()     )
     lowerCAmelCase__            =					{k for k in extra_keys if not k.endswith(".attn.bias"     )}
     lowerCAmelCase__            =					set(model.state_dict().keys()     ) - set(state_dict.keys()     )
     lowerCAmelCase__            =					{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_     )
     lowerCAmelCase__            =					model.num_parameters(exclude_embeddings=lowerCAmelCase_     )
     lowerCAmelCase__            =					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_	:   Tuple  ,							lowerCAmelCase_	:   str=False  ,							lowerCAmelCase_	:   int="text"     ):
     """simple docstring"""

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

     lowerCAmelCase__            =					"cpu"  # do conversion on cpu

     lowerCAmelCase__            =					_get_ckpt_path(lowerCAmelCase_  ,							use_small=lowerCAmelCase_     )
     lowerCAmelCase__            =					_load_model(lowerCAmelCase_  ,							lowerCAmelCase_  ,							model_type=lowerCAmelCase_  ,							use_small=lowerCAmelCase_     )

     # load bark initial model
     lowerCAmelCase__            =					_bark_load_model(lowerCAmelCase_  ,							"cpu"  ,							model_type=lowerCAmelCase_  ,							use_small=lowerCAmelCase_     )

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

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

          lowerCAmelCase__            =					model(lowerCAmelCase_     )

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

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

          lowerCAmelCase__            =					model(lowerCAmelCase_  ,							lowerCAmelCase_     )
          lowerCAmelCase__            =					bark_model(lowerCAmelCase_  ,							lowerCAmelCase_     )

          lowerCAmelCase__            =					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_	:   Tuple  ,							lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   Union[str, Any]  ,							lowerCAmelCase_	:   Optional[int]  ,							lowerCAmelCase_	:   Union[str, Any]  ,							lowerCAmelCase_	:   List[Any]  ,							):
     """simple docstring"""

     lowerCAmelCase__            =					os.path.join(lowerCAmelCase_  ,							lowerCAmelCase_     )

     lowerCAmelCase__            =					BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_  ,							"config.json"     )     )
     lowerCAmelCase__            =					BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_  ,							"config.json"     )     )
     lowerCAmelCase__            =					BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_  ,							"config.json"     )     )
     lowerCAmelCase__            =					EncodecConfig.from_pretrained("facebook/encodec_24khz"     )

     lowerCAmelCase__            =					BarkSemanticModel.from_pretrained(lowerCAmelCase_     )
     lowerCAmelCase__            =					BarkCoarseModel.from_pretrained(lowerCAmelCase_     )
     lowerCAmelCase__            =					BarkFineModel.from_pretrained(lowerCAmelCase_     )
     lowerCAmelCase__            =					EncodecModel.from_pretrained("facebook/encodec_24khz"     )

     lowerCAmelCase__            =					BarkConfig.from_sub_model_configs(
         lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_  ,							lowerCAmelCase_     )

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

     lowerCAmelCase__            =					BarkModel(lowerCAmelCase_     )

     lowerCAmelCase__            =					semantic
     lowerCAmelCase__            =					coarseAcoustic
     lowerCAmelCase__            =					fineAcoustic
     lowerCAmelCase__            =					codec

     lowerCAmelCase__            =					bark_generation_config

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


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)





'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


SCREAMING_SNAKE_CASE__          =  {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class 					snake_case			(nn.Module ):



       def __init__(					self  ,UpperCAmelCase_   )	->				Dict:
           super().__init__()
           lowercase__									=    torchvision.models.resnetaaa(pretrained=UpperCAmelCase_   )
           lowercase__									=    list(model.children()   )[:-2]
           lowercase__									=    nn.Sequential(*UpperCAmelCase_   )
           lowercase__									=    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )







       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
           lowercase__									=    self.pool(self.model(UpperCAmelCase_   )   )
           lowercase__									=    torch.flatten(UpperCAmelCase_  ,start_dim=2   )
           lowercase__									=    out.transpose(1  ,2   ).contiguous()
           return out  # BxNx2048





class 					snake_case			(UpperCamelCase ):



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    [json.loads(UpperCAmelCase_   ) for l in open(UpperCAmelCase_   )]
           lowercase__									=    os.path.dirname(UpperCAmelCase_   )
           lowercase__									=    tokenizer
           lowercase__									=    labels
           lowercase__									=    len(UpperCAmelCase_   )
           lowercase__									=    max_seq_length

           lowercase__									=    transforms



       def __len__(					self   )	->				Optional[Any]:
           return len(self.data   )



       def __getitem__(					self  ,UpperCAmelCase_   )	->				Optional[int]:
           lowercase__									=    torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,add_special_tokens=UpperCAmelCase_   )   )
           lowercase__							,							lowercase__							,							lowercase__									=    sentence[0], sentence[1:-1], sentence[-1]
           lowercase__									=    sentence[: self.max_seq_length]

           lowercase__									=    torch.zeros(self.n_classes   )
           lowercase__									=    1

           lowercase__									=    Image.open(os.path.join(self.data_dir  ,self.data[index]["img"]   )   ).convert("RGB"   )
           lowercase__									=    self.transforms(UpperCAmelCase_   )

           return {
               "image_start_token": start_token,
               "image_end_token": end_token,
               "sentence": sentence,
               "image": image,
               "label": label,
           }







       def 				_a   (					self   )	->				str:
           lowercase__									=    Counter()
           for row in self.data:
               label_freqs.update(row["label"]   )
           return label_freqs





def 							lowerCamelCase	(					_snake_case	:    Optional[int]							):





    '''simple docstring'''


    lowercase__									=    [len(row["sentence"]							) for row in batch]
    lowercase__							,							lowercase__									=    len(_snake_case							), max(_snake_case							)

    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)
    lowercase__									=    torch.zeros(_snake_case  ,_snake_case  ,dtype=torch.long							)

    for i_batch, (input_row, length) in enumerate(zip(_snake_case  ,_snake_case							)							):
        lowercase__									=    input_row["sentence"]
        lowercase__									=    1

    lowercase__									=    torch.stack([row["image"] for row in batch]							)
    lowercase__									=    torch.stack([row["label"] for row in batch]							)
    lowercase__									=    torch.stack([row["image_start_token"] for row in batch]							)
    lowercase__									=    torch.stack([row["image_end_token"] for row in batch]							)

    return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def 							lowerCamelCase	(					):





    '''simple docstring'''


    return [
        "Crime",
        "Drama",
        "Thriller",
        "Action",
        "Comedy",
        "Romance",
        "Documentary",
        "Short",
        "Mystery",
        "History",
        "Family",
        "Adventure",
        "Fantasy",
        "Sci-Fi",
        "Western",
        "Horror",
        "Sport",
        "War",
        "Music",
        "Musical",
        "Animation",
        "Biography",
        "Film-Noir",
    ]




def 							lowerCamelCase	(					):





    '''simple docstring'''


    return transforms.Compose(
        [
            transforms.Resize(256							),
            transforms.CenterCrop(224							),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]  ,std=[0.12_221_994, 0.12_145_835, 0.14_380_469]  ,),
        ]							)




