

'''simple docstring'''

from scipy.stats import spearmanr

import datasets


lowercase__         =      "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"

lowercase__         =      "\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {'spearmanr': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results['spearmanr'])\n        -0.7\n        >>> print(round(results['spearmanr_pvalue'], 2))\n        0.19\n"

lowercase__         =      r"\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n"




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






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     str:
      return datasets.MetricInfo(
          description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(
              {
                  'predictions': datasets.Value('float'	),
                  'references': datasets.Value('float'	),
              }	)					,      reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html']					,      )




     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					str					,      lowercase_			:					Dict					,      lowercase_			:					List[str]=False	)						->     Union[str, Any]:
      UpperCAmelCase	:     Union[str, Any]						 =			spearmanr(lowercase_					,      lowercase_	)
      if return_pvalue:
       return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
      else:
       return {"spearmanr": results[0]}







'''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()
lowercase__         =      logging.get_logger(__name__)

set_seed(770)


lowercase__         =      {
    "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",
}


lowercase__         =      {
    "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",
    },
}

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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False     ):
 UpperCAmelCase	:     List[str]						 =			model_type
 if use_small:
  key += "_small"
 return os.path.join(UpperCAmelCase_  ,	REMOTE_MODEL_PATHS[key]['file_name']     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 os.makedirs(UpperCAmelCase_  ,	exist_ok=UpperCAmelCase_     )
 hf_hub_download(repo_id=UpperCAmelCase_  ,	filename=UpperCAmelCase_  ,	local_dir=UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=False  ,	UpperCAmelCase_="text"     ):
 if model_type == "text":
  UpperCAmelCase	:     Dict						 =			BarkSemanticModel
  UpperCAmelCase	:     List[Any]						 =			BarkSemanticConfig
  UpperCAmelCase	:     Optional[int]						 =			BarkSemanticGenerationConfig
 elif model_type == "coarse":
  UpperCAmelCase	:     List[str]						 =			BarkCoarseModel
  UpperCAmelCase	:     Dict						 =			BarkCoarseConfig
  UpperCAmelCase	:     int						 =			BarkCoarseGenerationConfig
 elif model_type == "fine":
  UpperCAmelCase	:     List[Any]						 =			BarkFineModel
  UpperCAmelCase	:     Optional[Any]						 =			BarkFineConfig
  UpperCAmelCase	:     Dict						 =			BarkFineGenerationConfig
 else:
  raise NotImplementedError()
 UpperCAmelCase	:     Optional[int]						 =			F"""{model_type}_small""" if use_small else model_type
 UpperCAmelCase	:     Tuple						 =			REMOTE_MODEL_PATHS[model_key]
 if not os.path.exists(UpperCAmelCase_     ):
  logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`."""     )
  _download(model_info['repo_id']  ,	model_info['file_name']     )
 UpperCAmelCase	:     Optional[Any]						 =			torch.load(UpperCAmelCase_  ,	map_location=UpperCAmelCase_     )
 # this is a hack
 UpperCAmelCase	:     str						 =			checkpoint['model_args']
 if "input_vocab_size" not in model_args:
  UpperCAmelCase	:     Union[str, Any]						 =			model_args['vocab_size']
  UpperCAmelCase	:     Union[str, Any]						 =			model_args['vocab_size']
  del model_args["vocab_size"]

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

 UpperCAmelCase	:     List[Any]						 =			ConfigClass(**checkpoint['model_args']     )
 UpperCAmelCase	:     List[str]						 =			ModelClass(config=UpperCAmelCase_     )
 UpperCAmelCase	:     List[str]						 =			GenerationConfigClass()

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

   UpperCAmelCase	:     List[Any]						 =			state_dict.pop(UpperCAmelCase_     )

 UpperCAmelCase	:     Tuple						 =			set(state_dict.keys()     ) - set(model.state_dict().keys()     )
 UpperCAmelCase	:     Optional[int]						 =			{k for k in extra_keys if not k.endswith('.attn.bias'     )}
 UpperCAmelCase	:     str						 =			set(model.state_dict().keys()     ) - set(state_dict.keys()     )
 UpperCAmelCase	:     str						 =			{k for k in missing_keys if not k.endswith('.attn.bias'     )}
 if len(UpperCAmelCase_     ) != 0:
  raise ValueError(F"""extra keys found: {extra_keys}"""     )
 if len(UpperCAmelCase_     ) != 0:
  raise ValueError(F"""missing keys: {missing_keys}"""     )
 model.load_state_dict(UpperCAmelCase_  ,	strict=UpperCAmelCase_     )
 UpperCAmelCase	:     List[str]						 =			model.num_parameters(exclude_embeddings=UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[Any]						 =			checkpoint['best_val_loss'].item()
 logger.info(F"""model loaded: {round(n_params/1E6  ,	1     )}M params, {round(UpperCAmelCase_  ,	3     )} loss"""     )
 model.eval()
 model.to(UpperCAmelCase_     )
 del checkpoint, state_dict

 return model




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=False  ,	UpperCAmelCase_="text"     ):
 if model_type not in ("text", "coarse", "fine"):
  raise NotImplementedError()

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

 UpperCAmelCase	:     List[str]						 =			_get_ckpt_path(UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			_load_model(UpperCAmelCase_  ,	UpperCAmelCase_  ,	model_type=UpperCAmelCase_  ,	use_small=UpperCAmelCase_     )

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

 if model_type == "text":
  UpperCAmelCase	:     Tuple						 =			bark_model['model']

 if model.num_parameters(exclude_embeddings=UpperCAmelCase_     ) != 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[int]						 =			5
 UpperCAmelCase	:     Optional[int]						 =			10

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

  UpperCAmelCase	:     List[str]						 =			model(UpperCAmelCase_     )

  # take last logits
  UpperCAmelCase	:     str						 =			output_new_model_total.logits[:, [-1], :]

 else:
  UpperCAmelCase	:     Optional[int]						 =			3
  UpperCAmelCase	:     List[Any]						 =			8
  UpperCAmelCase	:     Dict						 =			torch.randint(2_56  ,	(batch_size, sequence_length, n_codes_total)  ,	dtype=torch.int     )

  UpperCAmelCase	:     str						 =			model(UpperCAmelCase_  ,	UpperCAmelCase_     )
  UpperCAmelCase	:     Dict						 =			bark_model(UpperCAmelCase_  ,	UpperCAmelCase_     )

  UpperCAmelCase	:     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(UpperCAmelCase_     ).mkdir(exist_ok=UpperCAmelCase_     )
 model.save_pretrained(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	):
 UpperCAmelCase	:     int						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )

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

 UpperCAmelCase	:     Dict						 =			BarkSemanticModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Tuple						 =			BarkCoarseModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			BarkFineModel.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			EncodecModel.from_pretrained('facebook/encodec_24khz'     )

 UpperCAmelCase	:     Optional[Any]						 =			BarkConfig.from_sub_model_configs(
     UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

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

 UpperCAmelCase	:     str						 =			BarkModel(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			semantic
 UpperCAmelCase	:     Tuple						 =			coarseAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			fineAcoustic
 UpperCAmelCase	:     Union[str, Any]						 =			codec

 UpperCAmelCase	:     Optional[int]						 =			bark_generation_config

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


if __name__ == "__main__":
 lowercase__         =      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.")

 lowercase__         =      parser.parse_args()

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




