





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      	=		logging.get_logger(__name__)

set_seed(7_70)


_SCREAMING_SNAKE_CASE      	=		{
    """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      	=		{
    """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      	=		os.path.dirname(os.path.abspath(__file__))
_SCREAMING_SNAKE_CASE      	=		os.path.join(os.path.expanduser("""~"""), """.cache""")
_SCREAMING_SNAKE_CASE      	=		os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a=False   ):
	snake_case_		:			Dict											=	model_type
	if use_small:
		key += "_small"
	return os.path.join(__a   , REMOTE_MODEL_PATHS[key]['file_name']   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	os.makedirs(__a   , exist_ok=__a   )
	hf_hub_download(repo_id=__a   , filename=__a   , local_dir=__a   )


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

	# convert Bark model arguments to HF Bark model arguments
	snake_case_		:			str											=	model_args.pop('n_head'   )
	snake_case_		:			Optional[int]											=	model_args.pop('n_embd'   )
	snake_case_		:			int											=	model_args.pop('n_layer'   )

	snake_case_		:			int											=	ConfigClass(**checkpoint['model_args']   )
	snake_case_		:			Dict											=	ModelClass(config=__a   )
	snake_case_		:			Dict											=	GenerationConfigClass()

	snake_case_		:			str											=	model_generation_config
	snake_case_		:			Optional[int]											=	checkpoint['model']
	# fixup checkpoint
	snake_case_		:			Dict											=	'_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
			snake_case_		:			Optional[Any]											=	k[len(__a   ) :]
			for old_layer_name in new_layer_name_dict:
				snake_case_		:			str											=	new_k.replace(__a   , new_layer_name_dict[old_layer_name]   )

			snake_case_		:			str											=	state_dict.pop(__a   )

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

	snake_case_		:			List[str]											=	'cpu'  # do conversion on cpu

	snake_case_		:			List[Any]											=	_get_ckpt_path(__a   , use_small=__a   )
	snake_case_		:			Tuple											=	_load_model(__a   , __a   , model_type=__a   , use_small=__a   )

	# load bark initial model
	snake_case_		:			List[str]											=	_bark_load_model(__a   , 'cpu'   , model_type=__a   , use_small=__a   )

	if model_type == "text":
		snake_case_		:			List[Any]											=	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
	snake_case_		:			Optional[int]											=	5
	snake_case_		:			Optional[int]											=	10

	if model_type in ["text", "coarse"]:
		snake_case_		:			Optional[Any]											=	torch.randint(2_56   , (batch_size, sequence_length)   , dtype=torch.int   )
		snake_case_		:			Dict											=	bark_model(__a   )[0]

		snake_case_		:			List[str]											=	model(__a   )

		# take last logits
		snake_case_		:			Optional[Any]											=	output_new_model_total.logits[:, [-1], :]

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

		snake_case_		:			Any											=	model(__a   , __a   )
		snake_case_		:			Dict											=	bark_model(__a   , __a   )

		snake_case_		:			Dict											=	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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   , __a   , ):
	snake_case_		:			str											=	os.path.join(__a   , __a   )

	snake_case_		:			Dict											=	BarkSemanticConfig.from_pretrained(os.path.join(__a   , 'config.json'   )   )
	snake_case_		:			Union[str, Any]											=	BarkCoarseConfig.from_pretrained(os.path.join(__a   , 'config.json'   )   )
	snake_case_		:			Dict											=	BarkFineConfig.from_pretrained(os.path.join(__a   , 'config.json'   )   )
	snake_case_		:			List[Any]											=	EncodecConfig.from_pretrained('facebook/encodec_24khz'   )

	snake_case_		:			Tuple											=	BarkSemanticModel.from_pretrained(__a   )
	snake_case_		:			Optional[Any]											=	BarkCoarseModel.from_pretrained(__a   )
	snake_case_		:			Optional[int]											=	BarkFineModel.from_pretrained(__a   )
	snake_case_		:			Optional[int]											=	EncodecModel.from_pretrained('facebook/encodec_24khz'   )

	snake_case_		:			Optional[int]											=	BarkConfig.from_sub_model_configs(
	    __a   , __a   , __a   , __a   )

	snake_case_		:			Tuple											=	BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config   , coarseAcoustic.generation_config   , fineAcoustic.generation_config   )

	snake_case_		:			Optional[int]											=	BarkModel(__a   )

	snake_case_		:			Optional[int]											=	semantic
	snake_case_		:			Any											=	coarseAcoustic
	snake_case_		:			List[Any]											=	fineAcoustic
	snake_case_		:			Any											=	codec

	snake_case_		:			str											=	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      	=		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      	=		parser.parse_args()

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








import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece.model""")
_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_SCREAMING_SNAKE_CASE      	=		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			str       =       CamembertTokenizer
						__magic_name__:			Optional[Any]       =       CamembertTokenizerFast
						__magic_name__:			Optional[int]       =       True
						__magic_name__:			str       =       True






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:			Union[str, Any]											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)






						def UpperCAmelCase_				(      self      :      int	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			int											=	'<pad>'
							snake_case_		:			int											=	1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A	)		,   _A	)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A	)		,   _A	)






						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	list(self.get_tokenizer().get_vocab().keys()	)

							self.assertEqual(vocab_keys[0]		,   '<s>NOTUSED'	)
							self.assertEqual(vocab_keys[1]		,   '<pad>'	)
							self.assertEqual(vocab_keys[-1]		,   '<mask>'	)
							self.assertEqual(len(_A	)		,   1004	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size		,   1005	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Any											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)
							snake_case_		:			List[Any]											=	CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

							snake_case_		:			str											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Dict											=	tokenizer.encode(_A	)
							snake_case_		:			Tuple											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			List[Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			Any											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							snake_case_		:			Union[str, Any]											=	tokenizer.convert_ids_to_tokens(_A	)
							snake_case_		:			Optional[Any]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							if not self.test_rust_tokenizer:
								return

							snake_case_		:			Union[str, Any]											=	self.get_tokenizer()
							snake_case_		:			Any											=	self.get_rust_tokenizer()

							snake_case_		:			Union[str, Any]											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Optional[int]											=	tokenizer.tokenize(_A	)
							snake_case_		:			Dict											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			str											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[int]											=	self.get_rust_tokenizer()
							snake_case_		:			int											=	tokenizer.encode(_A	)
							snake_case_		:			int											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)






						@slow
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							snake_case_		:			Dict											=	[
							    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
							    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
							    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
							    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
							    'telles que la traduction et la synthèse de texte.',
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=_A		,   model_name='camembert-base'		,   revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'		,   sequences=_A		,   )

