



'''simple docstring'''

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									=   logging.get_logger(__name__)

set_seed(770)


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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Any:
		_a     :      Optional[Any]        =							model_type
		if use_small:
				key += "_small"
		return os.path.join(lowerCAmelCase_						,			REMOTE_MODEL_PATHS[key]['file_name']  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      str:
		os.makedirs(lowerCAmelCase_						,			exist_ok=lowerCAmelCase_  )
		hf_hub_download(repo_id=lowerCAmelCase_						,			filename=lowerCAmelCase_						,			local_dir=lowerCAmelCase_  )



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

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

		_a     :      List[str]        =							ConfigClass(**checkpoint['model_args']  )
		_a     :      Union[str, Any]        =							ModelClass(config=lowerCAmelCase_  )
		_a     :      List[str]        =							GenerationConfigClass()

		_a     :      Dict        =							model_generation_config
		_a     :      str        =							checkpoint['model']
		# fixup checkpoint
		_a     :      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
						_a     :      Tuple        =							k[len(lowerCAmelCase_  ) :]
						for old_layer_name in new_layer_name_dict:
								_a     :      List[Any]        =							new_k.replace(lowerCAmelCase_						,			new_layer_name_dict[old_layer_name]  )

						_a     :      Any        =							state_dict.pop(lowerCAmelCase_  )

		_a     :      Union[str, Any]        =							set(state_dict.keys()  ) - set(model.state_dict().keys()  )
		_a     :      List[Any]        =							{k for k in extra_keys if not k.endswith('.attn.bias'  )}
		_a     :      Dict        =							set(model.state_dict().keys()  ) - set(state_dict.keys()  )
		_a     :      List[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_  )
		_a     :      List[str]        =							model.num_parameters(exclude_embeddings=lowerCAmelCase_  )
		_a     :      List[str]        =							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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False						,			lowerCAmelCase_="text"  )			->      str:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		_a     :      Optional[int]        =							'cpu'  # do conversion on cpu

		_a     :      str        =							_get_ckpt_path(lowerCAmelCase_						,			use_small=lowerCAmelCase_  )
		_a     :      Optional[Any]        =							_load_model(lowerCAmelCase_						,			lowerCAmelCase_						,			model_type=lowerCAmelCase_						,			use_small=lowerCAmelCase_  )

		# load bark initial model
		_a     :      Dict        =							_bark_load_model(lowerCAmelCase_						,			'cpu'						,			model_type=lowerCAmelCase_						,			use_small=lowerCAmelCase_  )

		if model_type == "text":
				_a     :      str        =							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
		_a     :      Any        =							5
		_a     :      int        =							10

		if model_type in ["text", "coarse"]:
				_a     :      int        =							torch.randint(256						,			(batch_size, sequence_length)						,			dtype=torch.int  )
				_a     :      Optional[Any]        =							bark_model(lowerCAmelCase_  )[0]

				_a     :      str        =							model(lowerCAmelCase_  )

				# take last logits
				_a     :      List[Any]        =							output_new_model_total.logits[:, [-1], :]

		else:
				_a     :      str        =							3
				_a     :      Any        =							8
				_a     :      Optional[int]        =							torch.randint(256						,			(batch_size, sequence_length, n_codes_total)						,			dtype=torch.int  )

				_a     :      Optional[int]        =							model(lowerCAmelCase_						,			lowerCAmelCase_  )
				_a     :      Optional[Any]        =							bark_model(lowerCAmelCase_						,			lowerCAmelCase_  )

				_a     :      List[Any]        =							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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			)			->      Optional[int]:
		_a     :      Optional[Any]        =							os.path.join(lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      List[Any]        =							BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_						,			'config.json'  )  )
		_a     :      List[Any]        =							BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_						,			'config.json'  )  )
		_a     :      Tuple        =							BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_						,			'config.json'  )  )
		_a     :      Optional[Any]        =							EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

		_a     :      Any        =							BarkSemanticModel.from_pretrained(lowerCAmelCase_  )
		_a     :      Union[str, Any]        =							BarkCoarseModel.from_pretrained(lowerCAmelCase_  )
		_a     :      Any        =							BarkFineModel.from_pretrained(lowerCAmelCase_  )
		_a     :      Tuple        =							EncodecModel.from_pretrained('facebook/encodec_24khz'  )

		_a     :      Tuple        =							BarkConfig.from_sub_model_configs(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		_a     :      List[Any]        =							BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config						,			coarseAcoustic.generation_config						,			fineAcoustic.generation_config  )

		_a     :      List[str]        =							BarkModel(lowerCAmelCase_  )

		_a     :      Any        =							semantic
		_a     :      List[str]        =							coarseAcoustic
		_a     :      Dict        =							fineAcoustic
		_a     :      Union[str, Any]        =							codec

		_a     :      Any        =							bark_generation_config

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


if __name__ == "__main__":
							__lowerCAmelCase									=   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									=   parser.parse_args()

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








import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin





@require_jieba
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							str        = CpmAntTokenizer
 __lowercase :							List[Any]        = False
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        super().setUp()

        lowercase__:   Any  			=   [
            '<d>',
            '</d>',
            '<s>',
            '</s>',
            '</_>',
            '<unk>',
            '<pad>',
            '</n>',
            '我',
            '是',
            'C',
            'P',
            'M',
            'A',
            'n',
            't',
        ]
        lowercase__:   List[Any]  			=   os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES['vocab_file'] )
        with open(self.vocab_file				, 'w'				, encoding='utf-8' ) as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
 @tooslow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b' )
        lowercase__:   Optional[Any]  			=   '今天天气真好！'
        lowercase__:   str  			=   ['今天', '天气', '真', '好', '！']
        lowercase__:   Optional[Any]  			=   tokenizer.tokenize(lowerCAmelCase__ )
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
        lowercase__:   List[str]  			=   '今天天气真好！'
        lowercase__:   List[str]  			=   [tokenizer.bos_token] + tokens

        lowercase__:   Tuple  			=   [6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )				, lowerCAmelCase__ )

        lowercase__:   Any  			=   tokenizer.decode(lowerCAmelCase__ )
        self.assertEqual(lowerCAmelCase__				, lowerCAmelCase__ )


