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

set_seed(770)


SCREAMING_SNAKE_CASE				: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',
}


SCREAMING_SNAKE_CASE				:Union[str, Any]									=							{
    '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',
    },
}

SCREAMING_SNAKE_CASE				:List[str]									=							os.path.dirname(os.path.abspath(__file__))
SCREAMING_SNAKE_CASE				:Optional[int]									=							os.path.join(os.path.expanduser('~'), '.cache')
SCREAMING_SNAKE_CASE				:Any									=							os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')


def 		UpperCAmelCase     (      a_						,  a_=False		)						->      Optional[Any]:







				"""simple docstring"""



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


def 		UpperCAmelCase     (      a_						,  a_		)						->      List[str]:







				"""simple docstring"""



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


def 		UpperCAmelCase     (      a_						,  a_						,  a_=False						,  a_="text"		)						->      List[Any]:







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

												__A       =					state_dict.pop(a_		)

				__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(a_		) != 0:
								raise ValueError(F'''extra keys found: {extra_keys}'''		)
				if len(a_		) != 0:
								raise ValueError(F'''missing keys: {missing_keys}'''		)
				model.load_state_dict(a_						,  strict=a_		)
				__A       =					model.num_parameters(exclude_embeddings=a_		)
				__A       =					checkpoint["best_val_loss"].item()
				logger.info(F'''model loaded: {round(n_params/1E6						,  1		)}M params, {round(a_						,  3		)} loss'''		)
				model.eval()
				model.to(a_		)
				del checkpoint, state_dict

				return model


def 		UpperCAmelCase     (      a_						,  a_=False						,  a_="text"		)						->      List[Any]:







				"""simple docstring"""



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

				__A       =					"cpu"  # do conversion on cpu

				__A       =					_get_ckpt_path(a_						,  use_small=a_		)
				__A       =					_load_model(a_						,  a_						,  model_type=a_						,  use_small=a_		)

				# load bark initial model
				__A       =					_bark_load_model(a_						,  "cpu"						,  model_type=a_						,  use_small=a_		)

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

				if model.num_parameters(exclude_embeddings=a_		) != 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(a_		)[0]

								__A       =					model(a_		)

								# 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(a_						,  a_		)
								__A       =					bark_model(a_						,  a_		)

								__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(a_		).mkdir(exist_ok=a_		)
				model.save_pretrained(a_		)


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_						,  a_						,  a_						,  )						->      Union[str, Any]:







				"""simple docstring"""



				__A       =					os.path.join(a_						,  a_		)

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

				__A       =					BarkSemanticModel.from_pretrained(a_		)
				__A       =					BarkCoarseModel.from_pretrained(a_		)
				__A       =					BarkFineModel.from_pretrained(a_		)
				__A       =					EncodecModel.from_pretrained("facebook/encodec_24khz"		)

				__A       =					BarkConfig.from_sub_model_configs(
				    a_						,  a_						,  a_						,  a_		)

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

				__A       =					BarkModel(a_		)

				__A       =					semantic
				__A       =					coarseAcoustic
				__A       =					fineAcoustic
				__A       =					codec

				__A       =					bark_generation_config

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				: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.')

					SCREAMING_SNAKE_CASE				:Union[str, Any]									=							parser.parse_args()

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





from __future__ import annotations

import queue


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= data
											a__	:							Tuple      						= None
											a__	:							Optional[Any]      						= None






def     SCREAMING_SNAKE_CASE(      )	->						TreeNode:
				print("\n********Press N to stop entering at any point of time********\n" )
				a__	:							Tuple      						= input("Enter the value of the root node: " ).strip().lower()
				a__	:							queue.Queue      						= queue.Queue()
				a__	:							str      						= TreeNode(int(__UpperCamelCase ) )
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							str      						= q.get()
								a__	:							Optional[Any]      						= F'Enter the left node of {node_found.data}: '
								a__	:							List[Any]      						= input(__UpperCamelCase ).strip().lower() or "n"
								if check == "n":
												return tree_node
								a__	:							Optional[int]      						= TreeNode(int(__UpperCamelCase ) )
								a__	:							List[str]      						= left_node
								q.put(__UpperCamelCase )
								a__	:							Dict      						= F'Enter the right node of {node_found.data}: '
								a__	:							List[str]      						= input(__UpperCamelCase ).strip().lower() or "n"
								if check == "n":
												return tree_node
								a__	:							str      						= TreeNode(int(__UpperCamelCase ) )
								a__	:							Any      						= right_node
								q.put(__UpperCamelCase )
				raise






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				print(node.data	,				end="," )
				pre_order(node.left )
				pre_order(node.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				in_order(node.left )
				print(node.data	,				end="," )
				in_order(node.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				post_order(node.left )
				post_order(node.right )
				print(node.data	,				end="," )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							queue.Queue      						= queue.Queue()
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							Tuple      						= q.get()
								print(node_dequeued.data	,				end="," )
								if node_dequeued.left:
												q.put(node_dequeued.left )
								if node_dequeued.right:
												q.put(node_dequeued.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							queue.Queue      						= queue.Queue()
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							int      						= []
								while not q.empty():
												a__	:							Union[str, Any]      						= q.get()
												print(node_dequeued.data	,				end="," )
												if node_dequeued.left:
																list_.append(node_dequeued.left )
												if node_dequeued.right:
																list_.append(node_dequeued.right )
								print()
								for node in list_:
												q.put(__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							list[TreeNode]      						= []
				a__	:							Optional[int]      						= node
				while n or stack:
								while n:  # start from root node, find its left child
												print(n.data	,				end="," )
												stack.append(__UpperCamelCase )
												a__	:							int      						= n.left
								# end of while means current node doesn't have left child
								a__	:							Dict      						= stack.pop()
								# start to traverse its right child
								a__	:							str      						= n.right






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							list[TreeNode]      						= []
				a__	:							Dict      						= node
				while n or stack:
								while n:
												stack.append(__UpperCamelCase )
												a__	:							List[Any]      						= n.left
								a__	:							str      						= stack.pop()
								print(n.data	,				end="," )
								a__	:							Any      						= n.right






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__						,   a__	:							List[str]      						= [], []
				a__	:							List[Any]      						= node
				stacka.append(__UpperCamelCase )
				while stacka:  # to find the reversed order of post order, store it in stack2
								a__	:							Optional[Any]      						= stacka.pop()
								if n.left:
												stacka.append(n.left )
								if n.right:
												stacka.append(n.right )
								stacka.append(__UpperCamelCase )
				while stacka:  # pop up from stack2 will be the post order
								print(stacka.pop().data	,				end="," )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = ""	,				__UpperCamelCase=50	,				__UpperCamelCase="*" )	->						str:
				if not s:
								return "\n" + width * char
				a__						,   a__	:							int      						= divmod(width - len(__UpperCamelCase ) - 2	,				2 )
				return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(prompt("""Binary Tree Traversals"""))

						lowerCamelCase		       =      build_tree()
						print(prompt("""Pre Order Traversal"""))
						pre_order(node)
						print(prompt() + """\n""")

						print(prompt("""In Order Traversal"""))
						in_order(node)
						print(prompt() + """\n""")

						print(prompt("""Post Order Traversal"""))
						post_order(node)
						print(prompt() + """\n""")

						print(prompt("""Level Order Traversal"""))
						level_order(node)
						print(prompt() + """\n""")

						print(prompt("""Actual Level Order Traversal"""))
						level_order_actual(node)
						print("""*""" * 50 + """\n""")

						print(prompt("""Pre Order Traversal - Iteration Version"""))
						pre_order_iter(node)
						print(prompt() + """\n""")

						print(prompt("""In Order Traversal - Iteration Version"""))
						in_order_iter(node)
						print(prompt() + """\n""")

						print(prompt("""Post Order Traversal - Iteration Version"""))
						post_order_iter(node)
						print(prompt())


