


"""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 __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      List[Any]=False  ):
				SCREAMING_SNAKE_CASE__     							=				model_type
				if use_small:
								key += "_small"
				return os.path.join(lowerCamelCase_						,					REMOTE_MODEL_PATHS[key]["file_name"]  )


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      Union[str, Any]  ):
				os.makedirs(lowerCamelCase_						,					exist_ok=lowerCamelCase_  )
				hf_hub_download(repo_id=lowerCamelCase_						,					filename=lowerCamelCase_						,					local_dir=lowerCamelCase_  )


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

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

				SCREAMING_SNAKE_CASE__     							=				ConfigClass(**checkpoint["model_args"]  )
				SCREAMING_SNAKE_CASE__     							=				ModelClass(config=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				GenerationConfigClass()

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

												SCREAMING_SNAKE_CASE__     							=				state_dict.pop(lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				set(state_dict.keys()  ) - set(model.state_dict().keys()  )
				SCREAMING_SNAKE_CASE__     							=				{k for k in extra_keys if not k.endswith(".attn.bias"  )}
				SCREAMING_SNAKE_CASE__     							=				set(model.state_dict().keys()  ) - set(state_dict.keys()  )
				SCREAMING_SNAKE_CASE__     							=				{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_  )
				SCREAMING_SNAKE_CASE__     							=				model.num_parameters(exclude_embeddings=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      str=False						,					lowerCamelCase_  :      Optional[Any]="text"  ):
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				SCREAMING_SNAKE_CASE__     							=				"cpu"  # do conversion on cpu

				SCREAMING_SNAKE_CASE__     							=				_get_ckpt_path(lowerCamelCase_						,					use_small=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				_load_model(lowerCamelCase_						,					lowerCamelCase_						,					model_type=lowerCamelCase_						,					use_small=lowerCamelCase_  )

				# load bark initial model
				SCREAMING_SNAKE_CASE__     							=				_bark_load_model(lowerCamelCase_						,					"cpu"						,					model_type=lowerCamelCase_						,					use_small=lowerCamelCase_  )

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

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

								SCREAMING_SNAKE_CASE__     							=				model(lowerCamelCase_  )

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

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

								SCREAMING_SNAKE_CASE__     							=				model(lowerCamelCase_						,					lowerCamelCase_  )
								SCREAMING_SNAKE_CASE__     							=				bark_model(lowerCamelCase_						,					lowerCamelCase_  )

								SCREAMING_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(lowerCamelCase_  ).mkdir(exist_ok=lowerCamelCase_  )
				model.save_pretrained(lowerCamelCase_  )


def __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      Tuple						,					lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Dict						,					):
				SCREAMING_SNAKE_CASE__     							=				os.path.join(lowerCamelCase_						,					lowerCamelCase_  )

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

				SCREAMING_SNAKE_CASE__     							=				BarkSemanticModel.from_pretrained(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				BarkCoarseModel.from_pretrained(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				BarkFineModel.from_pretrained(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				EncodecModel.from_pretrained("facebook/encodec_24khz"  )

				SCREAMING_SNAKE_CASE__     							=				BarkConfig.from_sub_model_configs(
				    lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  )

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

				SCREAMING_SNAKE_CASE__     							=				BarkModel(lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				semantic
				SCREAMING_SNAKE_CASE__     							=				coarseAcoustic
				SCREAMING_SNAKE_CASE__     							=				fineAcoustic
				SCREAMING_SNAKE_CASE__     							=				codec

				SCREAMING_SNAKE_CASE__     							=				bark_generation_config

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


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"""






import heapq


def __lowercase     (      lowerCamelCase_  :      dict  ):
				SCREAMING_SNAKE_CASE__     							=				[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase_						,					[-1 * len(lowerCamelCase_  ), (key, value)]  )

				# chosen_vertices = set of chosen vertices
				SCREAMING_SNAKE_CASE__     							=				set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								SCREAMING_SNAKE_CASE__     							=				heapq.heappop(lowerCamelCase_  )[1][0]
								chosen_vertices.add(lowerCamelCase_  )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																SCREAMING_SNAKE_CASE__     							=				elem[1][1].index(lowerCamelCase_  )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase_  )
				return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCamelCase     =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
