
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()
SCREAMING_SNAKE_CASE__   :			int												=    logging.get_logger(__name__)

set_seed(770)


SCREAMING_SNAKE_CASE__   :			Optional[int]												=    {
    "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",
}


SCREAMING_SNAKE_CASE__   :			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",
    },
}

SCREAMING_SNAKE_CASE__   :			Any												=    os.path.dirname(os.path.abspath(__file__))
SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    os.path.join(os.path.expanduser("~"), ".cache")
SCREAMING_SNAKE_CASE__   :			Optional[int]												=    os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")




def  __magic_name__ (   __lowerCAmelCase	:						List[Any]		,  __lowerCAmelCase	:						Optional[Any]=False							)			->   Optional[Any]:
				__lowerCamelCase    					=	model_type
				if use_small:
								key += "_small"
				return os.path.join(__lowerCAmelCase		,  REMOTE_MODEL_PATHS[key]['''file_name''']							)




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




def  __magic_name__ (   __lowerCAmelCase	:						Optional[Any]		,  __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						List[Any]=False		,  __lowerCAmelCase	:						str="text"							)			->   List[str]:
				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  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						str=False		,  __lowerCAmelCase	:						List[Any]="text"							)			->   List[str]:
				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  __magic_name__ (   __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						Dict		,  )			->   str:
				__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__":
					SCREAMING_SNAKE_CASE__   :			Any												=    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.")

					SCREAMING_SNAKE_CASE__   :			List[str]												=    parser.parse_args()

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





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if dst_width < 0 or dst_height < 0:
										raise ValueError('''Destination width/height should be > 0'''  )

						__lowerCamelCase    					=	img
						__lowerCamelCase    					=	img.shape[1]
						__lowerCamelCase    					=	img.shape[0]
						__lowerCamelCase    					=	dst_width
						__lowerCamelCase    					=	dst_height

						__lowerCamelCase    					=	self.src_w / self.dst_w
						__lowerCamelCase    					=	self.src_h / self.dst_h

						__lowerCamelCase    					=	__lowerCamelCase    					=	(
						    np.ones((self.dst_h, self.dst_w, 3)							,			np.uinta  ) * 2_55
						)






		def  __A       (		self		:							List[Any]  )     ->							str:
						for i in range(self.dst_h  ):
										for j in range(self.dst_w  ):
														__lowerCamelCase    					=	self.img[self.get_y(SCREAMING_SNAKE_CASE__  )][self.get_x(SCREAMING_SNAKE_CASE__  )]






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_x * x  )







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_y * y  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			List[str]												=    800, 600
					SCREAMING_SNAKE_CASE__   :			int												=    imread("image_data/lena.jpg", 1)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    NearestNeighbour(im, dst_w, dst_h)
					n.process()

					imshow(
					    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
					)
					waitKey(0)
					destroyAllWindows()



