


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       :      Dict	=	logging.get_logger(__name__)

set_seed(770)


_lowercase       :      List[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",
}


_lowercase       :      Dict	=	{
    "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       :      Union[str, Any]	=	os.path.dirname(os.path.abspath(__file__))
_lowercase       :      int	=	os.path.join(os.path.expanduser("~"), ".cache")
_lowercase       :      List[str]	=	os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")





def   _lowerCAmelCase				(   UpperCamelCase__:     Any							,					UpperCamelCase__:     Any=False						) ->   int:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     str						) ->   int:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Any							,					UpperCamelCase__:     Union[str, Any]=False							,					UpperCamelCase__:     Union[str, Any]="text"						) ->   str:





	"""simple docstring"""







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

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

	A								=				ConfigClass(**checkpoint["""model_args"""]						)
	A								=				ModelClass(config=UpperCAmelCase__						)
	A								=				GenerationConfigClass()

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

			A								=				state_dict.pop(UpperCAmelCase__						)

	A								=				set(state_dict.keys()						) - set(model.state_dict().keys()						)
	A								=				{k for k in extra_keys if not k.endswith(""".attn.bias"""						)}
	A								=				set(model.state_dict().keys()						) - set(state_dict.keys()						)
	A								=				{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__						)
	A								=				model.num_parameters(exclude_embeddings=UpperCAmelCase__						)
	A								=				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   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Optional[int]=False							,					UpperCamelCase__:     Dict="text"						) ->   str:





	"""simple docstring"""







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

	A								=				"""cpu"""  # do conversion on cpu

	A								=				_get_ckpt_path(UpperCAmelCase__							,					use_small=UpperCAmelCase__						)
	A								=				_load_model(UpperCAmelCase__							,					UpperCAmelCase__							,					model_type=UpperCAmelCase__							,					use_small=UpperCAmelCase__						)

	# load bark initial model
	A								=				_bark_load_model(UpperCAmelCase__							,					"""cpu"""							,					model_type=UpperCAmelCase__							,					use_small=UpperCAmelCase__						)

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

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

		A								=				model(UpperCAmelCase__						)

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

	else:
		A								=				3
		A								=				8
		A								=				torch.randint(2_56							,					(batch_size, sequence_length, n_codes_total)							,					dtype=torch.int						)

		A								=				model(UpperCAmelCase__							,					UpperCAmelCase__						)
		A								=				bark_model(UpperCAmelCase__							,					UpperCAmelCase__						)

		A								=				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   _lowerCAmelCase				(   UpperCamelCase__:     Any							,					UpperCamelCase__:     str							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     Tuple							,					) ->   List[str]:





	"""simple docstring"""







	A								=				os.path.join(UpperCAmelCase__							,					UpperCAmelCase__						)

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

	A								=				BarkSemanticModel.from_pretrained(UpperCAmelCase__						)
	A								=				BarkCoarseModel.from_pretrained(UpperCAmelCase__						)
	A								=				BarkFineModel.from_pretrained(UpperCAmelCase__						)
	A								=				EncodecModel.from_pretrained("""facebook/encodec_24khz"""						)

	A								=				BarkConfig.from_sub_model_configs(
	    UpperCAmelCase__							,					UpperCAmelCase__							,					UpperCAmelCase__							,					UpperCAmelCase__						)

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

	A								=				BarkModel(UpperCAmelCase__						)

	A								=				semantic
	A								=				coarseAcoustic
	A								=				fineAcoustic
	A								=				codec

	A								=				bark_generation_config

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


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

				_lowercase       :      int	=	parser.parse_args()

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







import os





def   _lowerCAmelCase				(   UpperCamelCase__:     str = "matrix.txt"						) ->   int:





	"""simple docstring"""







	with open(os.path.join(os.path.dirname(UpperCamelCase__						)							,					UpperCamelCase__						)						) as in_file:
		A								=				in_file.read()

	A								=				[[int(UpperCamelCase__						) for cell in row.split(""","""						)] for row in data.strip().splitlines()]
	A								=				[[0 for cell in row] for row in grid]
	A								=				len(grid[0]						)

	A								=				[[0 for i in range(UpperCamelCase__						)] for j in range(UpperCamelCase__						)]
	A								=				grid[0][0]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[0][i] + dp[0][i - 1]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[i][0] + dp[i - 1][0]

	for i in range(1							,					UpperCamelCase__						):
		for j in range(1							,					UpperCamelCase__						):
			A								=				grid[i][j] + min(dp[i - 1][j]							,					dp[i][j - 1]						)

	return dp[-1][-1]


if __name__ == "__main__":
				print(f'''{solution() = }''')



