
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_  :	Dict 				=					logging.get_logger(__name__)


lowerCamelCase_  :	int 				=					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

lowerCamelCase_  :	Dict 				=					{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

lowerCamelCase_  :	Any 				=					{
    """allenai/longformer-base-4096""": 40_96,
    """allenai/longformer-large-4096""": 40_96,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 40_96,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 40_96,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 40_96,
}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     A__   (  )					->		List[str]:
				UpperCamelCase_:							List[str]		=  (
				    list(range(ord("""!"""  )       ,      ord("""~"""  ) + 1  )  ) + list(range(ord("""¡"""  )       ,      ord("""¬"""  ) + 1  )  ) + list(range(ord("""®"""  )       ,      ord("""ÿ"""  ) + 1  )  )
				)
				UpperCamelCase_:							Any		=  bs[:]
				UpperCamelCase_:							Tuple		=  0
				for b in range(2**8  ):
								if b not in bs:
												bs.append(lowerCamelCase  )
												cs.append(2**8 + n  )
												n += 1
				UpperCamelCase_:							List[Any]		=  [chr(lowerCamelCase  ) for n in cs]
				return dict(zip(lowerCamelCase       ,      lowerCamelCase  )  )







def     A__   (  lowerCamelCase  )					->		Optional[Any]:
				UpperCamelCase_:							Union[str, Any]		=  set()
				UpperCamelCase_:							List[Any]		=  word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)  )
								UpperCamelCase_:							Any		=  char
				return pairs





class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  Optional[int]    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  str    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  int    =	["""input_ids""", """attention_mask"""]

			def __init__(    self	:    str  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Dict  ,		snake_case_	:    Optional[Any]="replace"  ,		snake_case_	:    List[str]="<s>"  ,		snake_case_	:    Dict="</s>"  ,		snake_case_	:    List[str]="</s>"  ,		snake_case_	:    Tuple="<s>"  ,		snake_case_	:    str="<unk>"  ,		snake_case_	:    Any="<pad>"  ,		snake_case_	:    Tuple="<mask>"  ,		snake_case_	:    Union[str, Any]=False  ,		**snake_case_	:    str  ,		):
							UpperCamelCase_:							List[Any]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else bos_token
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else eos_token
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else sep_token
							UpperCamelCase_:							Any		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else cls_token
							UpperCamelCase_:							List[Any]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else unk_token
							UpperCamelCase_:							Optional[int]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							UpperCamelCase_:							List[str]		=  AddedToken(snake_case_  ,		lstrip=snake_case_  ,		rstrip=snake_case_ ) if isinstance(snake_case_  ,		snake_case_ ) else mask_token

							super().__init__(
							    errors=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		unk_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		pad_token=snake_case_  ,		mask_token=snake_case_  ,		add_prefix_space=snake_case_  ,		**snake_case_  ,		)

							with open(snake_case_  ,		encoding="""utf-8""" ) as vocab_handle:
											UpperCamelCase_:							Union[str, Any]		=  json.load(snake_case_ )
							UpperCamelCase_:							List[Any]		=  {v: k for k, v in self.encoder.items()}
							UpperCamelCase_:							int		=  errors  # how to handle errors in decoding
							UpperCamelCase_:							str		=  bytes_to_unicode()
							UpperCamelCase_:							str		=  {v: k for k, v in self.byte_encoder.items()}
							with open(snake_case_  ,		encoding="""utf-8""" ) as merges_handle:
											UpperCamelCase_:							Optional[int]		=  merges_handle.read().split("""\n""" )[1:-1]
							UpperCamelCase_:							str		=  [tuple(merge.split() ) for merge in bpe_merges]
							UpperCamelCase_:							Tuple		=  dict(zip(snake_case_  ,		range(len(snake_case_ ) ) ) )
							UpperCamelCase_:							Dict		=  {}
							UpperCamelCase_:							Optional[Any]		=  add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							UpperCamelCase_:							List[str]		=  re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )

			@property
			def  lowerCAmelCase__		(    self	:    List[str] ):
							return len(self.encoder )

			def  lowerCAmelCase__		(    self	:    Any ):
							return dict(self.encoder  ,		**self.added_tokens_encoder )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    List[str] ):
							if token in self.cache:
											return self.cache[token]
							UpperCamelCase_:							int		=  tuple(snake_case_ )
							UpperCamelCase_:							List[Any]		=  get_pairs(snake_case_ )

							if not pairs:
											return token

							while True:
											UpperCamelCase_:							Dict		=  min(snake_case_  ,		key=lambda snake_case_ : self.bpe_ranks.get(snake_case_  ,		float("""inf""" ) ) )
											if bigram not in self.bpe_ranks:
															break
											UpperCamelCase_,				UpperCamelCase_:							Union[str, Any]		=  bigram
											UpperCamelCase_:							Dict		=  []
											UpperCamelCase_:							Dict		=  0
											while i < len(snake_case_ ):
															try:
																			UpperCamelCase_:							Tuple		=  word.index(snake_case_  ,		snake_case_ )
															except ValueError:
																			new_word.extend(word[i:] )
																			break
															else:
																			new_word.extend(word[i:j] )
																			UpperCamelCase_:							Optional[Any]		=  j

															if word[i] == first and i < len(snake_case_ ) - 1 and word[i + 1] == second:
																			new_word.append(first + second )
																			i += 2
															else:
																			new_word.append(word[i] )
																			i += 1
											UpperCamelCase_:							List[Any]		=  tuple(snake_case_ )
											UpperCamelCase_:							int		=  new_word
											if len(snake_case_ ) == 1:
															break
											else:
															UpperCamelCase_:							Optional[Any]		=  get_pairs(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  """ """.join(snake_case_ )
							UpperCamelCase_:							Tuple		=  word
							return word

			def  lowerCAmelCase__		(    self	:    List[str]  ,		snake_case_	:    List[str] ):
							UpperCamelCase_:							Optional[Any]		=  []
							for token in re.findall(self.pat  ,		snake_case_ ):
											UpperCamelCase_:							Optional[Any]		=  """""".join(
											    self.byte_encoder[b] for b in token.encode("""utf-8""" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case_ ).split(""" """ ) )
							return bpe_tokens

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    List[Any] ):
							return self.encoder.get(snake_case_  ,		self.encoder.get(self.unk_token ) )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    Dict ):
							return self.decoder.get(snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[Any]  ,		snake_case_	:    Tuple ):
							UpperCamelCase_:							Dict		=  """""".join(snake_case_ )
							UpperCamelCase_:							Optional[Any]		=  bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8"""  ,		errors=self.errors )
							return text

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    str  ,		snake_case_	:    Optional[str] = None ):
							if not os.path.isdir(snake_case_ ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
											return
							UpperCamelCase_:							List[Any]		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
							UpperCamelCase_:							str		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )

							with open(snake_case_  ,		"""w"""  ,		encoding="""utf-8""" ) as f:
											f.write(json.dumps(self.encoder  ,		indent=2  ,		sort_keys=snake_case_  ,		ensure_ascii=snake_case_ ) + """\n""" )

							UpperCamelCase_:							List[Any]		=  0
							with open(snake_case_  ,		"""w"""  ,		encoding="""utf-8""" ) as writer:
											writer.write("""#version: 0.2\n""" )
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,		key=lambda snake_case_ : kv[1] ):
															if index != token_index:
																			logger.warning(
																			    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																			    """ Please check that the tokenizer is not corrupted!""" )
																			UpperCamelCase_:							List[str]		=  token_index
															writer.write(""" """.join(snake_case_ ) + """\n""" )
															index += 1

							return vocab_file, merge_file

			def  lowerCAmelCase__		(    self	:    Any  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCamelCase_:							List[Any]		=  [self.cls_token_id]
							UpperCamelCase_:							List[Any]		=  [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None  ,		snake_case_	:    bool = False ):
							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=snake_case_  ,		token_ids_a=snake_case_  ,		already_has_special_tokens=snake_case_ )

							if token_ids_a is None:
											return [1] + ([0] * len(snake_case_ )) + [1]
							return [1] + ([0] * len(snake_case_ )) + [1, 1] + ([0] * len(snake_case_ )) + [1]

			def  lowerCAmelCase__		(    self	:    Optional[Any]  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							UpperCamelCase_:							Tuple		=  [self.sep_token_id]
							UpperCamelCase_:							Optional[int]		=  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]




			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    Tuple  ,		snake_case_	:    Optional[int]=False  ,		**snake_case_	:    str ):
							UpperCamelCase_:							List[Any]		=  kwargs.pop("""add_prefix_space"""  ,		self.add_prefix_space )
							if (is_split_into_words or add_prefix_space) and (len(snake_case_ ) > 0 and not text[0].isspace()):
											UpperCamelCase_:							Union[str, Any]		=  """ """ + text
							return (text, kwargs)








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_  :	Union[str, Any] 				=					logging.get_logger(__name__)

set_seed(7_70)


lowerCamelCase_  :	str 				=					{
    """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_  :	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""",
    },
}

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







def     A__   (  lowerCamelCase       ,      lowerCamelCase=False  )					->		int:
				UpperCamelCase_:							Union[str, Any]		=  model_type
				if use_small:
								key += "_small"
				return os.path.join(lowerCamelCase       ,      REMOTE_MODEL_PATHS[key]["""file_name"""]  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				os.makedirs(lowerCamelCase       ,      exist_ok=lowerCamelCase  )
				hf_hub_download(repo_id=lowerCamelCase       ,      filename=lowerCamelCase       ,      local_dir=lowerCamelCase  )







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

				# convert Bark model arguments to HF Bark model arguments
				UpperCamelCase_:							Tuple		=  model_args.pop("""n_head"""  )
				UpperCamelCase_:							Dict		=  model_args.pop("""n_embd"""  )
				UpperCamelCase_:							List[str]		=  model_args.pop("""n_layer"""  )

				UpperCamelCase_:							Optional[Any]		=  ConfigClass(**checkpoint["""model_args"""]  )
				UpperCamelCase_:							Optional[Any]		=  ModelClass(config=lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GenerationConfigClass()

				UpperCamelCase_:							Optional[Any]		=  model_generation_config
				UpperCamelCase_:							Optional[int]		=  checkpoint["""model"""]
				# fixup checkpoint
				UpperCamelCase_:							Dict		=  """_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
												UpperCamelCase_:							Optional[int]		=  k[len(lowerCamelCase  ) :]
												for old_layer_name in new_layer_name_dict:
																UpperCamelCase_:							Dict		=  new_k.replace(lowerCamelCase       ,      new_layer_name_dict[old_layer_name]  )

												UpperCamelCase_:							List[str]		=  state_dict.pop(lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  set(state_dict.keys()  ) - set(model.state_dict().keys()  )
				UpperCamelCase_:							Dict		=  {k for k in extra_keys if not k.endswith(""".attn.bias"""  )}
				UpperCamelCase_:							Optional[Any]		=  set(model.state_dict().keys()  ) - set(state_dict.keys()  )
				UpperCamelCase_:							Union[str, Any]		=  {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  )
				UpperCamelCase_:							str		=  model.num_parameters(exclude_embeddings=lowerCamelCase  )
				UpperCamelCase_:							int		=  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     A__   (  lowerCamelCase       ,      lowerCamelCase=False       ,      lowerCamelCase="text"  )					->		Any:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				UpperCamelCase_:							Union[str, Any]		=  """cpu"""  # do conversion on cpu

				UpperCamelCase_:							int		=  _get_ckpt_path(lowerCamelCase       ,      use_small=lowerCamelCase  )
				UpperCamelCase_:							Dict		=  _load_model(lowerCamelCase       ,      lowerCamelCase       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				# load bark initial model
				UpperCamelCase_:							List[Any]		=  _bark_load_model(lowerCamelCase       ,      """cpu"""       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				if model_type == "text":
								UpperCamelCase_:							Tuple		=  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
				UpperCamelCase_:							Optional[Any]		=  5
				UpperCamelCase_:							List[str]		=  10

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

								UpperCamelCase_:							Optional[Any]		=  model(lowerCamelCase  )

								# take last logits
								UpperCamelCase_:							Union[str, Any]		=  output_new_model_total.logits[:, [-1], :]

				else:
								UpperCamelCase_:							Tuple		=  3
								UpperCamelCase_:							List[Any]		=  8
								UpperCamelCase_:							List[str]		=  torch.randint(2_56       ,      (batch_size, sequence_length, n_codes_total)       ,      dtype=torch.int  )

								UpperCamelCase_:							int		=  model(lowerCamelCase       ,      lowerCamelCase  )
								UpperCamelCase_:							Any		=  bark_model(lowerCamelCase       ,      lowerCamelCase  )

								UpperCamelCase_:							Optional[int]		=  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     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      )					->		str:
				UpperCamelCase_:							List[str]		=  os.path.join(lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							List[Any]		=  BarkCoarseConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							Optional[int]		=  BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							Any		=  EncodecConfig.from_pretrained("""facebook/encodec_24khz"""  )

				UpperCamelCase_:							Optional[Any]		=  BarkSemanticModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  BarkCoarseModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  BarkFineModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  EncodecModel.from_pretrained("""facebook/encodec_24khz"""  )

				UpperCamelCase_:							int		=  BarkConfig.from_sub_model_configs(
				    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config       ,      coarseAcoustic.generation_config       ,      fineAcoustic.generation_config  )

				UpperCamelCase_:							Optional[Any]		=  BarkModel(lowerCamelCase  )

				UpperCamelCase_:							int		=  semantic
				UpperCamelCase_:							Tuple		=  coarseAcoustic
				UpperCamelCase_:							Optional[int]		=  fineAcoustic
				UpperCamelCase_:							Any		=  codec

				UpperCamelCase_:							Dict		=  bark_generation_config

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


if __name__ == "__main__":
							lowerCamelCase_  :	List[Any] 				=					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_  :	Dict 				=					parser.parse_args()

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






