






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__    :   int    		=       logging.get_logger(__name__)

set_seed(770)


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





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_=False	) ->	Dict:


	"""simple docstring"""



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





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_	) ->	List[Any]:


	"""simple docstring"""



	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__    ( snake_case_,	snake_case_,	snake_case_=False,	snake_case_="text"	) ->	int:


	"""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(_SCREAMING_SNAKE_CASE	):
		logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""	)
		_download(model_info['''repo_id'''],	model_info['''file_name''']	)
	a												=      torch.load(_SCREAMING_SNAKE_CASE,	map_location=_SCREAMING_SNAKE_CASE	)
	# 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=_SCREAMING_SNAKE_CASE	)
	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(_SCREAMING_SNAKE_CASE	):
			# replace part of the key with corresponding layer name in HF implementation
			a												=      k[len(_SCREAMING_SNAKE_CASE	) :]
			for old_layer_name in new_layer_name_dict:
				a												=      new_k.replace(_SCREAMING_SNAKE_CASE,	new_layer_name_dict[old_layer_name]	)

			a												=      state_dict.pop(_SCREAMING_SNAKE_CASE	)

	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(_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	)
	a												=      model.num_parameters(exclude_embeddings=_SCREAMING_SNAKE_CASE	)
	a												=      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__    ( snake_case_,	snake_case_=False,	snake_case_="text"	) ->	List[str]:


	"""simple docstring"""



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

	a												=      '''cpu'''  # do conversion on cpu

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

	# load bark initial model
	a												=      _bark_load_model(_SCREAMING_SNAKE_CASE,	'''cpu''',	model_type=_SCREAMING_SNAKE_CASE,	use_small=_SCREAMING_SNAKE_CASE	)

	if model_type == "text":
		a												=      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
	a												=      5
	a												=      1_0

	if model_type in ["text", "coarse"]:
		a												=      torch.randint(2_5_6,	(batch_size, sequence_length),	dtype=torch.int	)
		a												=      bark_model(_SCREAMING_SNAKE_CASE	)[0]

		a												=      model(_SCREAMING_SNAKE_CASE	)

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

	else:
		a												=      3
		a												=      8
		a												=      torch.randint(2_5_6,	(batch_size, sequence_length, n_codes_total),	dtype=torch.int	)

		a												=      model(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	)
		a												=      bark_model(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	)

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





def 	SCREAMING_SNAKE_CASE__    ( snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_,	) ->	Dict:


	"""simple docstring"""



	a												=      os.path.join(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	)

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

	a												=      BarkSemanticModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
	a												=      BarkCoarseModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
	a												=      BarkFineModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
	a												=      EncodecModel.from_pretrained('''facebook/encodec_24khz'''	)

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

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

	a												=      BarkModel(_SCREAMING_SNAKE_CASE	)

	a												=      semantic
	a												=      coarseAcoustic
	a												=      fineAcoustic
	a												=      codec

	a												=      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__":
				UpperCamelCase__    :   Optional[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.""")

				UpperCamelCase__    :   Optional[int]    		=       parser.parse_args()

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












def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    print('\nThe shortest path matrix using Floyd Warshall algorithm\n'				)
    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            if dist[i][j] != float('inf'				):
                print(int(dist[i][j]				)							,     end='\t'				)
            else:
                print('INF'							,     end='\t'				)
        print()





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							[[float('inf'				) for _ in range(_SCREAMING_SNAKE_CASE				)] for _ in range(_SCREAMING_SNAKE_CASE				)]

    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            _A     =							graph[i][j]

            # check vertex k against all other vertices (i, j)
    for k in range(_SCREAMING_SNAKE_CASE				):
        # looping through rows of graph array
        for i in range(_SCREAMING_SNAKE_CASE				):
            # looping through columns of graph array
            for j in range(_SCREAMING_SNAKE_CASE				):
                if (
                    dist[i][k] != float('inf'				)
                    and dist[k][j] != float('inf'				)
                    and dist[i][k] + dist[k][j] < dist[i][j]
                ):
                    _A     =							dist[i][k] + dist[k][j]

    _print_dist(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    return dist, v


if __name__ == "__main__":
 __A							:			Dict         =			int(input("Enter number of vertices: "))
 __A							:			Union[str, Any]         =			int(input("Enter number of edges: "))

 __A							:			List[str]         =			[[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  __A							:			List[Any]         =			0.0

  # src and dst are indices that must be within the array size graph[e][v]
  # failure to follow this will result in an error
 for i in range(e):
  print("\nEdge ", i + 1)
  __A							:			Union[str, Any]         =			int(input("Enter source:"))
  __A							:			List[str]         =			int(input("Enter destination:"))
  __A							:			Union[str, Any]         =			float(input("Enter weight:"))
  __A							:			Any         =			weight

 floyd_warshall(graph, v)

 # Example Input
 # Enter number of vertices: 3
 # Enter number of edges: 2

 # # generated graph from vertex and edge inputs
 # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
 # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

 # specify source, destination and weight for edge #1
 # Edge  1
 # Enter source:1
 # Enter destination:2
 # Enter weight:2

 # specify source, destination and weight for edge #2
 # Edge  2
 # Enter source:2
 # Enter destination:1
 # Enter weight:1

 # # Expected Output from the vertice, edge and src, dst, weight inputs!!
 # 0		INF	INF
 # INF	0	2
 # INF	1	0





