






from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
	import importlib_metadata
else:
	import importlib.metadata as importlib_metadata


_UpperCamelCase			:	Union[str, Any]								=''


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):






	class 						UpperCAmelCase__ (							tr.AbstractTransform							):







			def __init__(					self       ,A__ = " "		):
							_A	:				Any   =      sentence_delimiter







			def  A__      (					self       ,A__		):
							return list(A__		)







			def  A__      (					self       ,A__		):
							_A	:				Any   =      []
							for sent_idx, sentence in enumerate(A__		):
											chars.extend(self.process_string(A__		)		)
											if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(A__		) - 1:
															chars.append(self.sentence_delimiter		)
							return chars



	_UpperCamelCase			:	List[Any]								=tr.Compose(
	    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
	)
else:
	_UpperCamelCase			:	Any								=tr.Compose(
	    [
	        tr.RemoveMultipleSpaces(),
	        tr.Strip(),
	        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
	        tr.ReduceToListOfListOfChars(),
	    ]
	)


_UpperCamelCase			:	Tuple								='\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

_UpperCamelCase			:	Any								='\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n'

_UpperCamelCase			:	int								='\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> cer = datasets.load_metric("cer")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,				_KWARGS_DESCRIPTION							)
class 						UpperCAmelCase__ (							datasets.Metric							):







		def  A__      (					self		):
						return datasets.MetricInfo(
						    description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
						        {
						            '''predictions''': datasets.Value('''string'''       ,id='''sequence'''		),
						            '''references''': datasets.Value('''string'''       ,id='''sequence'''		),
						        }		)       ,codebase_urls=['''https://github.com/jitsi/jiwer/''']       ,reference_urls=[
						        '''https://en.wikipedia.org/wiki/Word_error_rate''',
						        '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
						    ]       ,)







		def  A__      (					self       ,A__       ,A__       ,A__=False		):
						if concatenate_texts:
										return jiwer.compute_measures(
										    A__       ,A__       ,truth_transform=A__       ,hypothesis_transform=A__       ,)["wer"]

						_A	:				Union[str, Any]   =      0
						_A	:				Optional[Any]   =      0
						for prediction, reference in zip(A__       ,A__		):
										_A	:				List[Any]   =      jiwer.compute_measures(
										    A__       ,A__       ,truth_transform=A__       ,hypothesis_transform=A__       ,)
										incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
										total += measures["substitutions"] + measures["deletions"] + measures["hits"]

						return incorrect / total











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

set_seed(770)


_UpperCamelCase			:	List[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',
}


_UpperCamelCase			:	Tuple								={
    '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',
    },
}

_UpperCamelCase			:	Any								=os.path.dirname(os.path.abspath(__file__))
_UpperCamelCase			:	Tuple								=os.path.join(os.path.expanduser('~'), '.cache')
_UpperCamelCase			:	Dict								=os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')



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



def  a__ (__lowercase							:str					,  __lowercase							:Optional[Any]   )		-> Dict:
				os.makedirs(__lowercase					,  exist_ok=__lowercase   )
				hf_hub_download(repo_id=__lowercase					,  filename=__lowercase					,  local_dir=__lowercase   )



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

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

				_A	:				Dict   =      ConfigClass(**checkpoint['''model_args''']   )
				_A	:				Optional[Any]   =      ModelClass(config=__lowercase   )
				_A	:				Optional[Any]   =      GenerationConfigClass()

				_A	:				Dict   =      model_generation_config
				_A	:				int   =      checkpoint['''model''']
				# fixup checkpoint
				_A	:				str   =      '''_orig_mod.'''
				for k, v in list(state_dict.items()   ):
								if k.startswith(__lowercase   ):
												# replace part of the key with corresponding layer name in HF implementation
												_A	:				Optional[Any]   =      k[len(__lowercase   ) :]
												for old_layer_name in new_layer_name_dict:
																_A	:				Any   =      new_k.replace(__lowercase					,  new_layer_name_dict[old_layer_name]   )

												_A	:				str   =      state_dict.pop(__lowercase   )

				_A	:				List[str]   =      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	:				Union[str, Any]   =      set(model.state_dict().keys()   ) - set(state_dict.keys()   )
				_A	:				Any   =      {k for k in missing_keys if not k.endswith('''.attn.bias'''   )}
				if len(__lowercase   ) != 0:
								raise ValueError(f"""extra keys found: {extra_keys}"""   )
				if len(__lowercase   ) != 0:
								raise ValueError(f"""missing keys: {missing_keys}"""   )
				model.load_state_dict(__lowercase					,  strict=__lowercase   )
				_A	:				int   =      model.num_parameters(exclude_embeddings=__lowercase   )
				_A	:				Tuple   =      checkpoint['''best_val_loss'''].item()
				logger.info(f"""model loaded: {round(n_params/1e6					,  1   )}M params, {round(__lowercase					,  3   )} loss"""   )
				model.eval()
				model.to(__lowercase   )
				del checkpoint, state_dict

				return model



def  a__ (__lowercase							:Union[str, Any]					,  __lowercase							:Any=False					,  __lowercase							:Optional[int]="text"   )		-> Dict:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

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

				_A	:				Tuple   =      _get_ckpt_path(__lowercase					,  use_small=__lowercase   )
				_A	:				Any   =      _load_model(__lowercase					,  __lowercase					,  model_type=__lowercase					,  use_small=__lowercase   )

				# load bark initial model
				_A	:				Union[str, Any]   =      _bark_load_model(__lowercase					,  '''cpu'''					,  model_type=__lowercase					,  use_small=__lowercase   )

				if model_type == "text":
								_A	:				Any   =      bark_model['''model''']

				if model.num_parameters(exclude_embeddings=__lowercase   ) != 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	:				List[str]   =      5
				_A	:				List[str]   =      10

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

								_A	:				Optional[Any]   =      model(__lowercase   )

								# take last logits
								_A	:				Dict   =      output_new_model_total.logits[:, [-1], :]

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

								_A	:				List[str]   =      model(__lowercase					,  __lowercase   )
								_A	:				Dict   =      bark_model(__lowercase					,  __lowercase   )

								_A	:				Optional[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(__lowercase   ).mkdir(exist_ok=__lowercase   )
				model.save_pretrained(__lowercase   )



def  a__ (__lowercase							:Dict					,  __lowercase							:List[str]					,  __lowercase							:Union[str, Any]					,  __lowercase							:Dict					,  __lowercase							:List[Any]					,  __lowercase							:Optional[int]					,  )		-> Union[str, Any]:
				_A	:				List[Any]   =      os.path.join(__lowercase					,  __lowercase   )

				_A	:				List[str]   =      BarkSemanticConfig.from_pretrained(os.path.join(__lowercase					,  '''config.json'''   )   )
				_A	:				List[Any]   =      BarkCoarseConfig.from_pretrained(os.path.join(__lowercase					,  '''config.json'''   )   )
				_A	:				Optional[int]   =      BarkFineConfig.from_pretrained(os.path.join(__lowercase					,  '''config.json'''   )   )
				_A	:				Any   =      EncodecConfig.from_pretrained('''facebook/encodec_24khz'''   )

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

				_A	:				Tuple   =      BarkConfig.from_sub_model_configs(
				    __lowercase					,  __lowercase					,  __lowercase					,  __lowercase   )

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

				_A	:				Tuple   =      BarkModel(__lowercase   )

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

				_A	:				Optional[Any]   =      bark_generation_config

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


if __name__ == "__main__":
	_UpperCamelCase			:	str								=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.')

	_UpperCamelCase			:	Dict								=parser.parse_args()

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



