
'''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												=	logging.get_logger(__name__)

set_seed(770)


__lowerCAmelCase												=	{
    '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												=	{
    '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												=	os.path.dirname(os.path.abspath(__file__))
__lowerCAmelCase												=	os.path.join(os.path.expanduser('~'), '.cache')
__lowerCAmelCase												=	os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
							_snake_case       =  model_type
							if use_small:
														key += "_small"
							return os.path.join(_SCREAMING_SNAKE_CASE							, REMOTE_MODEL_PATHS[key]["""file_name"""]				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							os.makedirs(_SCREAMING_SNAKE_CASE							, exist_ok=_SCREAMING_SNAKE_CASE				)
							hf_hub_download(repo_id=_SCREAMING_SNAKE_CASE							, filename=_SCREAMING_SNAKE_CASE							, local_dir=_SCREAMING_SNAKE_CASE				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False							, _SCREAMING_SNAKE_CASE="text"				):
							if model_type == "text":
														_snake_case       =  BarkSemanticModel
														_snake_case       =  BarkSemanticConfig
														_snake_case       =  BarkSemanticGenerationConfig
							elif model_type == "coarse":
														_snake_case       =  BarkCoarseModel
														_snake_case       =  BarkCoarseConfig
														_snake_case       =  BarkCoarseGenerationConfig
							elif model_type == "fine":
														_snake_case       =  BarkFineModel
														_snake_case       =  BarkFineConfig
														_snake_case       =  BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							_snake_case       =  f"""{model_type}_small""" if use_small else model_type
							_snake_case       =  REMOTE_MODEL_PATHS[model_key]
							if not os.path.exists(_SCREAMING_SNAKE_CASE				):
														logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""				)
														_download(model_info["""repo_id"""]							, model_info["""file_name"""]				)
							_snake_case       =  torch.load(_SCREAMING_SNAKE_CASE							, map_location=_SCREAMING_SNAKE_CASE				)
							# this is a hack
							_snake_case       =  checkpoint["""model_args"""]
							if "input_vocab_size" not in model_args:
														_snake_case       =  model_args["""vocab_size"""]
														_snake_case       =  model_args["""vocab_size"""]
														del model_args["vocab_size"]

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

							_snake_case       =  ConfigClass(**checkpoint["""model_args"""]				)
							_snake_case       =  ModelClass(config=_SCREAMING_SNAKE_CASE				)
							_snake_case       =  GenerationConfigClass()

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

																					_snake_case       =  state_dict.pop(_SCREAMING_SNAKE_CASE				)

							_snake_case       =  set(state_dict.keys()				) - set(model.state_dict().keys()				)
							_snake_case       =  {k for k in extra_keys if not k.endswith(""".attn.bias"""				)}
							_snake_case       =  set(model.state_dict().keys()				) - set(state_dict.keys()				)
							_snake_case       =  {k for k in missing_keys if not k.endswith(""".attn.bias"""				)}
							if len(_SCREAMING_SNAKE_CASE				) != 0:
														raise ValueError(f"""extra keys found: {extra_keys}"""				)
							if len(_SCREAMING_SNAKE_CASE				) != 0:
														raise ValueError(f"""missing keys: {missing_keys}"""				)
							model.load_state_dict(_SCREAMING_SNAKE_CASE							, strict=_SCREAMING_SNAKE_CASE				)
							_snake_case       =  model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE				)
							_snake_case       =  checkpoint["""best_val_loss"""].item()
							logger.info(f"""model loaded: {round(n_params/1E6							, 1				)}M params, {round(_SCREAMING_SNAKE_CASE							, 3				)} loss"""				)
							model.eval()
							model.to(_SCREAMING_SNAKE_CASE				)
							del checkpoint, state_dict

							return model






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False							, _SCREAMING_SNAKE_CASE="text"				):
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							_snake_case       =  """cpu"""  # do conversion on cpu

							_snake_case       =  _get_ckpt_path(_SCREAMING_SNAKE_CASE							, use_small=_SCREAMING_SNAKE_CASE				)
							_snake_case       =  _load_model(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, model_type=_SCREAMING_SNAKE_CASE							, use_small=_SCREAMING_SNAKE_CASE				)

							# load bark initial model
							_snake_case       =  _bark_load_model(_SCREAMING_SNAKE_CASE							, """cpu"""							, model_type=_SCREAMING_SNAKE_CASE							, use_small=_SCREAMING_SNAKE_CASE				)

							if model_type == "text":
														_snake_case       =  bark_model["""model"""]

							if model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE				) != 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       =  5
							_snake_case       =  10

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

														_snake_case       =  model(_SCREAMING_SNAKE_CASE				)

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

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

														_snake_case       =  model(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
														_snake_case       =  bark_model(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

														_snake_case       =  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(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, ):
							_snake_case       =  os.path.join(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

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

							_snake_case       =  BarkSemanticModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  BarkCoarseModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  BarkFineModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  EncodecModel.from_pretrained("""facebook/encodec_24khz"""				)

							_snake_case       =  BarkConfig.from_sub_model_configs(
							    _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

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

							_snake_case       =  BarkModel(_SCREAMING_SNAKE_CASE				)

							_snake_case       =  semantic
							_snake_case       =  coarseAcoustic
							_snake_case       =  fineAcoustic
							_snake_case       =  codec

							_snake_case       =  bark_generation_config

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


if __name__ == "__main__":
				__lowerCAmelCase												=	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												=	parser.parse_args()

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

'''simple docstring'''
from __future__ import annotations






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# We need to create solution object to save path.
							_snake_case       =  [[0 for _ in range(_SCREAMING_SNAKE_CASE				)] for _ in range(_SCREAMING_SNAKE_CASE				)]
							_snake_case       =  run_maze(_SCREAMING_SNAKE_CASE							, 0							, 0							, _SCREAMING_SNAKE_CASE				)
							if solved:
														print("""\n""".join(str(_SCREAMING_SNAKE_CASE				) for row in solutions				)				)
							else:
														print("""No solution exists!"""				)
							return solved






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# Final check point.
							if i == j == (size - 1):
														_snake_case       =  1
														return True

							_snake_case       =  (not i < 0) and (not j < 0)  # Check lower bounds
							_snake_case       =  (i < size) and (j < size)  # Check upper bounds

							if lower_flag and upper_flag:
														# check for already visited and block points.
														_snake_case       =  (not solutions[i][j]) and (not maze[i][j])
														if block_flag:
																					# check visited
																					_snake_case       =  1

																					# check for directions
																					if (
																					    run_maze(_SCREAMING_SNAKE_CASE							, i + 1							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
																					    or run_maze(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, j + 1							, _SCREAMING_SNAKE_CASE				)
																					    or run_maze(_SCREAMING_SNAKE_CASE							, i - 1							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
																					    or run_maze(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, j - 1							, _SCREAMING_SNAKE_CASE				)
																					):
																												return True

																					_snake_case       =  0
																					return False
							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()