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()
__a  :     Any       =  logging.get_logger(__name__)

set_seed(7_7_0)


__a  :     Tuple       =  {
    """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""",
}


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

__a  :     Tuple       =  os.path.dirname(os.path.abspath(__file__))
__a  :     Tuple       =  os.path.join(os.path.expanduser("""~"""), """.cache""")
__a  :     Any       =  os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")





def 				UpperCAmelCase						(    lowercase							,			lowercase=False							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase=False							,			lowercase="text"							):







				"""simple docstring"""



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

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

				__lowercase 			=							ConfigClass(**checkpoint['''model_args''']							)
				__lowercase 			=							ModelClass(config=lowercase							)
				__lowercase 			=							GenerationConfigClass()

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

												__lowercase 			=							state_dict.pop(lowercase							)

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

				return model





def 				UpperCAmelCase						(    lowercase							,			lowercase=False							,			lowercase="text"							):







				"""simple docstring"""



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

				__lowercase 			=							'''cpu'''  # do conversion on cpu

				__lowercase 			=							_get_ckpt_path(lowercase							,			use_small=lowercase							)
				__lowercase 			=							_load_model(lowercase							,			lowercase							,			model_type=lowercase							,			use_small=lowercase							)

				# load bark initial model
				__lowercase 			=							_bark_load_model(lowercase							,			'''cpu'''							,			model_type=lowercase							,			use_small=lowercase							)

				if model_type == "text":
								__lowercase 			=							bark_model['''model''']

				if model.num_parameters(exclude_embeddings=lowercase							) != 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
				__lowercase 			=							5
				__lowercase 			=							10

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

								__lowercase 			=							model(lowercase							)

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

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

								__lowercase 			=							model(lowercase							,			lowercase							)
								__lowercase 			=							bark_model(lowercase							,			lowercase							)

								__lowercase 			=							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(lowercase							).mkdir(exist_ok=lowercase							)
				model.save_pretrained(lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			):







				"""simple docstring"""



				__lowercase 			=							os.path.join(lowercase							,			lowercase							)

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

				__lowercase 			=							BarkSemanticModel.from_pretrained(lowercase							)
				__lowercase 			=							BarkCoarseModel.from_pretrained(lowercase							)
				__lowercase 			=							BarkFineModel.from_pretrained(lowercase							)
				__lowercase 			=							EncodecModel.from_pretrained('''facebook/encodec_24khz'''							)

				__lowercase 			=							BarkConfig.from_sub_model_configs(
				    lowercase							,			lowercase							,			lowercase							,			lowercase							)

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

				__lowercase 			=							BarkModel(lowercase							)

				__lowercase 			=							semantic
				__lowercase 			=							coarseAcoustic
				__lowercase 			=							fineAcoustic
				__lowercase 			=							codec

				__lowercase 			=							bark_generation_config

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


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

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

		load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__a  :     List[str]       =  Lock()





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				global process_lock

				# we perform n swaps since after n swaps we know we are sorted
				# we *could* stop early if we are sorted already, but it takes as long to
				# find out we are sorted as it does to sort the list with this algorithm
				for i in range(0							,			10							):
								if (i + position) % 2 == 0 and r_send is not None:
												# send your value to your right neighbor
												process_lock.acquire()
												r_send[1].send(lowercase							)
												process_lock.release()

												# receive your right neighbor's value
												process_lock.acquire()
												__lowercase 			=							rr_cv[0].recv()
												process_lock.release()

												# take the lower value since you are on the left
												__lowercase 			=							min(lowercase							,			lowercase							)
								elif (i + position) % 2 != 0 and l_send is not None:
												# send your value to your left neighbor
												process_lock.acquire()
												l_send[1].send(lowercase							)
												process_lock.release()

												# receive your left neighbor's value
												process_lock.acquire()
												__lowercase 			=							lr_cv[0].recv()
												process_lock.release()

												# take the higher value since you are on the right
												__lowercase 			=							max(lowercase							,			lowercase							)
    # after all swaps are performed, send the values back to main
				result_pipe[1].send(lowercase							)





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							[]
				__lowercase 			=							[]
				# initialize the list of pipes where the values will be retrieved
				for _ in arr:
								result_pipe.append(Pipe()							)
				# creates the processes
				# the first and last process only have one neighbor so they are made outside
				# of the loop
				__lowercase 			=							Pipe()
				__lowercase 			=							Pipe()
				process_array_.append(
				    Process(
				        target=lowercase							,			args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])							,			)							)
				__lowercase 			=							temp_rs
				__lowercase 			=							temp_rr

				for i in range(1							,			len(lowercase							) - 1							):
								__lowercase 			=							Pipe()
								__lowercase 			=							Pipe()
								process_array_.append(
								    Process(
								        target=lowercase							,			args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])							,			)							)
								__lowercase 			=							temp_rs
								__lowercase 			=							temp_rr

				process_array_.append(
				    Process(
				        target=lowercase							,			args=(
				            len(lowercase							) - 1,
				            arr[len(lowercase							) - 1],
				            temp_ls,
				            None,
				            temp_lr,
				            None,
				            result_pipe[len(lowercase							) - 1],
				        )							,			)							)

				# start the processes
				for p in process_array_:
								p.start()

				# wait for the processes to end and write their values to the list
				for p in range(0							,			len(lowercase							)							):
								__lowercase 			=							result_pipe[p][0].recv()
								process_array_[p].join()
				return arr





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							list(range(10							,			0							,			-1							)							)
				print('''Initial List'''							)
				print(*lowercase							)
				__lowercase 			=							odd_even_transposition(lowercase							)
				print('''Sorted List\n'''							)
				print(*lowercase							)


if __name__ == "__main__":
		main()