




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__     :     str		    =       logging.get_logger(__name__)

set_seed(7_70)


SCREAMING_SNAKE_CASE__     :     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",
}


SCREAMING_SNAKE_CASE__     :     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",
    },
}

SCREAMING_SNAKE_CASE__     :     List[str]		    =       os.path.dirname(os.path.abspath(__file__))
SCREAMING_SNAKE_CASE__     :     Dict		    =       os.path.join(os.path.expanduser("~"), ".cache")
SCREAMING_SNAKE_CASE__     :     Dict		    =       os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False )			->							List[Any]:


			'''simple docstring'''







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

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False       ,    SCREAMING_SNAKE_CASE="text" )			->							Tuple:


			'''simple docstring'''







			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(SCREAMING_SNAKE_CASE ):
						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(SCREAMING_SNAKE_CASE       ,    map_location=SCREAMING_SNAKE_CASE )
			# 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=SCREAMING_SNAKE_CASE )
			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(SCREAMING_SNAKE_CASE ):
									# replace part of the key with corresponding layer name in HF implementation
									SCREAMING_SNAKE_CASE_								=						k[len(SCREAMING_SNAKE_CASE ) :]
									for old_layer_name in new_layer_name_dict:
												SCREAMING_SNAKE_CASE_								=						new_k.replace(SCREAMING_SNAKE_CASE       ,    new_layer_name_dict[old_layer_name] )

									SCREAMING_SNAKE_CASE_								=						state_dict.pop(SCREAMING_SNAKE_CASE )

			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(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 )
			SCREAMING_SNAKE_CASE_								=						model.num_parameters(exclude_embeddings=SCREAMING_SNAKE_CASE )
			SCREAMING_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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False       ,    SCREAMING_SNAKE_CASE="text" )			->							int:


			'''simple docstring'''







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

			SCREAMING_SNAKE_CASE_								=						'cpu'  # do conversion on cpu

			SCREAMING_SNAKE_CASE_								=						_get_ckpt_path(SCREAMING_SNAKE_CASE       ,    use_small=SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						_load_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    model_type=SCREAMING_SNAKE_CASE       ,    use_small=SCREAMING_SNAKE_CASE )

			# load bark initial model
			SCREAMING_SNAKE_CASE_								=						_bark_load_model(SCREAMING_SNAKE_CASE       ,    'cpu'       ,    model_type=SCREAMING_SNAKE_CASE       ,    use_small=SCREAMING_SNAKE_CASE )

			if model_type == "text":
						SCREAMING_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
			SCREAMING_SNAKE_CASE_								=						5
			SCREAMING_SNAKE_CASE_								=						10

			if model_type in ["text", "coarse"]:
						SCREAMING_SNAKE_CASE_								=						torch.randint(2_56       ,    (batch_size, sequence_length)       ,    dtype=torch.int )
						SCREAMING_SNAKE_CASE_								=						bark_model(SCREAMING_SNAKE_CASE )[0]

						SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE )

						# 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(2_56       ,    (batch_size, sequence_length, n_codes_total)       ,    dtype=torch.int )

						SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
						SCREAMING_SNAKE_CASE_								=						bark_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

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

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    )			->							Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						os.path.join(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

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

			SCREAMING_SNAKE_CASE_								=						BarkSemanticModel.from_pretrained(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						BarkCoarseModel.from_pretrained(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						BarkFineModel.from_pretrained(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						EncodecModel.from_pretrained('facebook/encodec_24khz' )

			SCREAMING_SNAKE_CASE_								=						BarkConfig.from_sub_model_configs(
			    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

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

			SCREAMING_SNAKE_CASE_								=						BarkModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE_								=						semantic
			SCREAMING_SNAKE_CASE_								=						coarseAcoustic
			SCREAMING_SNAKE_CASE_								=						fineAcoustic
			SCREAMING_SNAKE_CASE_								=						codec

			SCREAMING_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__":
				SCREAMING_SNAKE_CASE__     :     Tuple		    =       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)






class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE = ""      ,     SCREAMING_SNAKE_CASE = False      )  ->					None:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{}

				# A node will be a leaf if the tree contains its word
				SCREAMING_SNAKE_CASE_								=						is_leaf

				SCREAMING_SNAKE_CASE_								=						prefix





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					tuple[str, str, str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for q, w in zip(self.prefix      ,     SCREAMING_SNAKE_CASE      ):
							if q != w:
										break

							x += 1

				return self.prefix[:x], self.prefix[x:], word[x:]





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				for word in words:
							self.insert(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if self.prefix == word:
							SCREAMING_SNAKE_CASE_								=						True

				# Case 2: The node has no edges that have a prefix to the word
				# Solution: We create an edge from the current node to a new one
				# containing the word
				elif word[0] not in self.nodes:
							SCREAMING_SNAKE_CASE_								=						RadixNode(prefix=SCREAMING_SNAKE_CASE      ,     is_leaf=SCREAMING_SNAKE_CASE      )

				else:
							SCREAMING_SNAKE_CASE_								=						self.nodes[word[0]]
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )

							# Case 3: The node prefix is equal to the matching
							# Solution: We insert remaining word on the next node
							if remaining_prefix == "":
										self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE      )

							# Case 4: The word is greater equal to the matching
							# Solution: Create a node in between both nodes, change
							# prefixes and add the new node for the remaining word
							else:
										SCREAMING_SNAKE_CASE_								=						remaining_prefix

										SCREAMING_SNAKE_CASE_								=						self.nodes[matching_string[0]]
										SCREAMING_SNAKE_CASE_								=						RadixNode(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						aux_node

										if remaining_word == "":
													SCREAMING_SNAKE_CASE_								=						True
										else:
													self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.nodes.get(word[0]      ,     SCREAMING_SNAKE_CASE      )
				if not incoming_node:
							return False
				else:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )
							# If there is remaining prefix, the word can't be on the tree
							if remaining_prefix != "":
										return False
							# This applies when the word and the prefix are equal
							elif remaining_word == "":
										return incoming_node.is_leaf
							# We have word remaining so we check the next node
							else:
										return incoming_node.find(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.nodes.get(word[0]      ,     SCREAMING_SNAKE_CASE      )
				if not incoming_node:
							return False
				else:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )
							# If there is remaining prefix, the word can't be on the tree
							if remaining_prefix != "":
										return False
							# We have word remaining so we check the next node
							elif remaining_word != "":
										return incoming_node.delete(SCREAMING_SNAKE_CASE      )
							else:
										# If it is not a leaf, we don't have to delete
										if not incoming_node.is_leaf:
													return False
										else:
													# We delete the nodes if no edges go from it
													if len(incoming_node.nodes      ) == 0:
																del self.nodes[word[0]]
																# We merge the current node with its only child
																if len(self.nodes      ) == 1 and not self.is_leaf:
																			SCREAMING_SNAKE_CASE_								=						list(self.nodes.values()      )[0]
																			SCREAMING_SNAKE_CASE_								=						merging_node.is_leaf
																			self.prefix += merging_node.prefix
																			SCREAMING_SNAKE_CASE_								=						merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
													elif len(incoming_node.nodes      ) > 1:
																SCREAMING_SNAKE_CASE_								=						False
													# If there is 1 edge, we merge it with its child
													else:
																SCREAMING_SNAKE_CASE_								=						list(incoming_node.nodes.values()      )[0]
																SCREAMING_SNAKE_CASE_								=						merging_node.is_leaf
																incoming_node.prefix += merging_node.prefix
																SCREAMING_SNAKE_CASE_								=						merging_node.nodes

													return True





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = 0      )  ->					None:




				"""simple docstring"""



				if self.prefix != "":
							print('-' * height      ,     self.prefix      ,     '  (leaf)' if self.is_leaf else ''      )

				for value in self.nodes.values():
							value.print_tree(height + 1      )




def lowercase    (  )			->							bool:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						'banana bananas bandana band apple all beast'.split()
			SCREAMING_SNAKE_CASE_								=						RadixNode()
			root.insert_many(SCREAMING_SNAKE_CASE )

			assert all(root.find(SCREAMING_SNAKE_CASE ) for word in words )
			assert not root.find('bandanas' )
			assert not root.find('apps' )
			root.delete('all' )
			assert not root.find('all' )
			root.delete('banana' )
			assert not root.find('banana' )
			assert root.find('bananas' )

			return True

def lowercase    (  )			->							None:


			'''simple docstring'''







			assert test_trie()

def lowercase    (  )			->							None:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						RadixNode()
			SCREAMING_SNAKE_CASE_								=						'banana bananas bandanas bandana band apple all beast'.split()
			root.insert_many(SCREAMING_SNAKE_CASE )

			print('Words:'       ,    SCREAMING_SNAKE_CASE )
			print('Tree:' )
			root.print_tree()


if __name__ == "__main__":
				main()
