



'''simple docstring'''

import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


snake_case__						      =       re.compile(r'''\b(a|an|the)\b''', re.UNICODE)

snake_case__						      =       None



def 					__lowerCamelCase					(				)      ->				Optional[Any]:
		_a					:				List[Any]				=							argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.'  )
		parser.add_argument('data_file'						,      metavar='data.json'						,      help='Input data JSON file.'  )
		parser.add_argument('pred_file'						,      metavar='pred.json'						,      help='Model predictions.'  )
		parser.add_argument(
		    '--out-file'						,      '-o'						,      metavar='eval.json'						,      help='Write accuracy metrics to file (default is stdout).'  )
		parser.add_argument(
		    '--na-prob-file'						,      '-n'						,      metavar='na_prob.json'						,      help='Model estimates of probability of no answer.'  )
		parser.add_argument(
		    '--na-prob-thresh'						,      '-t'						,      type=lowerCAmelCase_						,      default=1.0						,      help='Predict "" if no-answer probability exceeds this (default = 1.0).'						,      )
		parser.add_argument(
		    '--out-image-dir'						,      '-p'						,      metavar='out_images'						,      default=lowerCAmelCase_						,      help='Save precision-recall curves to directory.'  )
		parser.add_argument('--verbose'						,      '-v'						,      action='store_true'  )
		if len(sys.argv  ) == 1:
				parser.print_help()
				sys.exit(1  )
		return parser.parse_args()



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		_a					:				Any				=							{}
		for article in dataset:
				for p in article["paragraphs"]:
						for qa in p["qas"]:
								_a					:				Union[str, Any]				=							bool(qa['answers']['text']  )
		return qid_to_has_ans



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:

		def remove_articles(lowerCAmelCase_  ):
				return ARTICLES_REGEX.sub(' '						,      lowerCAmelCase_  )

		def white_space_fix(lowerCAmelCase_  ):
				return " ".join(text.split()  )

		def remove_punc(lowerCAmelCase_  ):
				_a					:				Optional[int]				=							set(string.punctuation  )
				return "".join(ch for ch in text if ch not in exclude  )

		def lower(lowerCAmelCase_  ):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_  )  )  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:

		if not s:
				return []
		return normalize_answer(lowerCAmelCase_  ).split()



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Any:
		return int(normalize_answer(lowerCAmelCase_  ) == normalize_answer(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:
		_a					:				str				=							get_tokens(lowerCAmelCase_  )
		_a					:				Optional[int]				=							get_tokens(lowerCAmelCase_  )
		_a					:				List[Any]				=							collections.Counter(lowerCAmelCase_  ) & collections.Counter(lowerCAmelCase_  )
		_a					:				Tuple				=							sum(common.values()  )
		if len(lowerCAmelCase_  ) == 0 or len(lowerCAmelCase_  ) == 0:
				# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
				return int(gold_toks == pred_toks  )
		if num_same == 0:
				return 0
		_a					:				Dict				=							1.0 * num_same / len(lowerCAmelCase_  )
		_a					:				Dict				=							1.0 * num_same / len(lowerCAmelCase_  )
		_a					:				List[str]				=							(2 * precision * recall) / (precision + recall)
		return fa



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Union[str, Any]:
		_a					:				Union[str, Any]				=							{}
		_a					:				Optional[Any]				=							{}
		for article in dataset:
				for p in article["paragraphs"]:
						for qa in p["qas"]:
								_a					:				Tuple				=							qa['id']
								_a					:				List[Any]				=							[t for t in qa['answers']['text'] if normalize_answer(lowerCAmelCase_  )]
								if not gold_answers:
										# For unanswerable questions, only correct answer is empty string
										_a					:				List[str]				=							['']
								if qid not in preds:
										print(f"""Missing prediction for {qid}"""  )
										continue
								_a					:				Optional[int]				=							preds[qid]
								# Take max over all gold answers
								_a					:				List[str]				=							max(compute_exact(lowerCAmelCase_						,      lowerCAmelCase_  ) for a in gold_answers  )
								_a					:				Any				=							max(compute_fa(lowerCAmelCase_						,      lowerCAmelCase_  ) for a in gold_answers  )
		return exact_scores, fa_scores



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		_a					:				int				=							{}
		for qid, s in scores.items():
				_a					:				Union[str, Any]				=							na_probs[qid] > na_prob_thresh
				if pred_na:
						_a					:				Tuple				=							float(not qid_to_has_ans[qid]  )
				else:
						_a					:				Union[str, Any]				=							s
		return new_scores



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=None  )      ->				Dict:

		if not qid_list:
				_a					:				Optional[int]				=							len(lowerCAmelCase_  )
				return collections.OrderedDict(
				    [
				        ('exact', 100.0 * sum(exact_scores.values()  ) / total),
				        ('f1', 100.0 * sum(fa_scores.values()  ) / total),
				        ('total', total),
				    ]  )
		else:
				_a					:				List[Any]				=							len(lowerCAmelCase_  )
				return collections.OrderedDict(
				    [
				        ('exact', 100.0 * sum(exact_scores[k] for k in qid_list  ) / total),
				        ('f1', 100.0 * sum(fa_scores[k] for k in qid_list  ) / total),
				        ('total', total),
				    ]  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Tuple:

		for k in new_eval:
				_a					:				Optional[Any]				=							new_eval[k]



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Tuple:
		plt.step(lowerCAmelCase_						,      lowerCAmelCase_						,      color='b'						,      alpha=0.2						,      where='post'  )
		plt.fill_between(lowerCAmelCase_						,      lowerCAmelCase_						,      step='post'						,      alpha=0.2						,      color='b'  )
		plt.xlabel('Recall'  )
		plt.ylabel('Precision'  )
		plt.xlim([0.0, 1.05]  )
		plt.ylim([0.0, 1.05]  )
		plt.title(lowerCAmelCase_  )
		plt.savefig(lowerCAmelCase_  )
		plt.clf()



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=None						,      lowerCAmelCase_=None  )      ->				Optional[int]:
		_a					:				Union[str, Any]				=							sorted(lowerCAmelCase_						,      key=lambda lowerCAmelCase_  : na_probs[k]  )
		_a					:				Any				=							0.0
		_a					:				List[Any]				=							1.0
		_a					:				int				=							0.0
		_a					:				Tuple				=							[1.0]
		_a					:				Dict				=							[0.0]
		_a					:				Any				=							0.0
		for i, qid in enumerate(lowerCAmelCase_  ):
				if qid_to_has_ans[qid]:
						true_pos += scores[qid]
				_a					:				int				=							true_pos / float(i + 1  )
				_a					:				List[str]				=							true_pos / float(lowerCAmelCase_  )
				if i == len(lowerCAmelCase_  ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
						# i.e., if we can put a threshold after this point
						avg_prec += cur_p * (cur_r - recalls[-1])
						precisions.append(lowerCAmelCase_  )
						recalls.append(lowerCAmelCase_  )
		if out_image:
				plot_pr_curve(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		return {"ap": 100.0 * avg_prec}



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Tuple:

		if out_image_dir and not os.path.exists(lowerCAmelCase_  ):
				os.makedirs(lowerCAmelCase_  )
		_a					:				Optional[int]				=							sum(1 for v in qid_to_has_ans.values() if v  )
		if num_true_pos == 0:
				return
		_a					:				Tuple				=							make_precision_recall_eval(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      out_image=os.path.join(lowerCAmelCase_						,      'pr_exact.png'  )						,      title='Precision-Recall curve for Exact Match score'						,      )
		_a					:				int				=							make_precision_recall_eval(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      out_image=os.path.join(lowerCAmelCase_						,      'pr_f1.png'  )						,      title='Precision-Recall curve for F1 score'						,      )
		_a					:				Optional[Any]				=							{k: float(lowerCAmelCase_  ) for k, v in qid_to_has_ans.items()}
		_a					:				Dict				=							make_precision_recall_eval(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      out_image=os.path.join(lowerCAmelCase_						,      'pr_oracle.png'  )						,      title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'						,      )
		merge_eval(lowerCAmelCase_						,      lowerCAmelCase_						,      'pr_exact'  )
		merge_eval(lowerCAmelCase_						,      lowerCAmelCase_						,      'pr_f1'  )
		merge_eval(lowerCAmelCase_						,      lowerCAmelCase_						,      'pr_oracle'  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:

		if not qid_list:
				return
		_a					:				Optional[Any]				=							[na_probs[k] for k in qid_list]
		_a					:				Any				=							np.ones_like(lowerCAmelCase_  ) / float(len(lowerCAmelCase_  )  )
		plt.hist(lowerCAmelCase_						,      weights=lowerCAmelCase_						,      bins=20						,      range=(0.0, 1.0)  )
		plt.xlabel('Model probability of no-answer'  )
		plt.ylabel('Proportion of dataset'  )
		plt.title(f"""Histogram of no-answer probability: {name}"""  )
		plt.savefig(os.path.join(lowerCAmelCase_						,      f"""na_prob_hist_{name}.png"""  )  )
		plt.clf()



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:
		_a					:				List[Any]				=							sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]  )
		_a					:				Union[str, Any]				=							num_no_ans
		_a					:				Optional[Any]				=							cur_score
		_a					:				List[str]				=							0.0
		_a					:				Any				=							sorted(lowerCAmelCase_						,      key=lambda lowerCAmelCase_  : na_probs[k]  )
		for i, qid in enumerate(lowerCAmelCase_  ):
				if qid not in scores:
						continue
				if qid_to_has_ans[qid]:
						_a					:				Tuple				=							scores[qid]
				else:
						if preds[qid]:
								_a					:				int				=							-1
						else:
								_a					:				Tuple				=							0
				cur_score += diff
				if cur_score > best_score:
						_a					:				List[Any]				=							cur_score
						_a					:				Tuple				=							na_probs[qid]
		return 100.0 * best_score / len(lowerCAmelCase_  ), best_thresh



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[str]:
		_a	, _a					:				int				=							find_best_thresh(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		_a	, _a					:				int				=							find_best_thresh(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		_a					:				Union[str, Any]				=							best_exact
		_a					:				List[Any]				=							exact_thresh
		_a					:				Dict				=							best_fa
		_a					:				Union[str, Any]				=							fa_thresh



def 					__lowerCamelCase					(				)      ->				List[str]:

		with open(OPTS.data_file  ) as f:
				_a					:				str				=							json.load(lowerCAmelCase_  )
				_a					:				Optional[Any]				=							dataset_json['data']
		with open(OPTS.pred_file  ) as f:
				_a					:				List[Any]				=							json.load(lowerCAmelCase_  )
		if OPTS.na_prob_file:
				with open(OPTS.na_prob_file  ) as f:
						_a					:				int				=							json.load(lowerCAmelCase_  )
		else:
				_a					:				Optional[Any]				=							{k: 0.0 for k in preds}
		_a					:				int				=							make_qid_to_has_ans(lowerCAmelCase_  )  # maps qid to True/False
		_a					:				Union[str, Any]				=							[k for k, v in qid_to_has_ans.items() if v]
		_a					:				List[str]				=							[k for k, v in qid_to_has_ans.items() if not v]
		_a	, _a					:				Optional[Any]				=							get_raw_scores(lowerCAmelCase_						,      lowerCAmelCase_  )
		_a					:				int				=							apply_no_ans_threshold(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      OPTS.na_prob_thresh  )
		_a					:				List[Any]				=							apply_no_ans_threshold(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      OPTS.na_prob_thresh  )
		_a					:				Optional[int]				=							make_eval_dict(lowerCAmelCase_						,      lowerCAmelCase_  )
		if has_ans_qids:
				_a					:				List[Any]				=							make_eval_dict(lowerCAmelCase_						,      lowerCAmelCase_						,      qid_list=lowerCAmelCase_  )
				merge_eval(lowerCAmelCase_						,      lowerCAmelCase_						,      'HasAns'  )
		if no_ans_qids:
				_a					:				Dict				=							make_eval_dict(lowerCAmelCase_						,      lowerCAmelCase_						,      qid_list=lowerCAmelCase_  )
				merge_eval(lowerCAmelCase_						,      lowerCAmelCase_						,      'NoAns'  )
		if OPTS.na_prob_file:
				find_all_best_thresh(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		if OPTS.na_prob_file and OPTS.out_image_dir:
				run_precision_recall_analysis(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      OPTS.out_image_dir  )
				histogram_na_prob(lowerCAmelCase_						,      lowerCAmelCase_						,      OPTS.out_image_dir						,      'hasAns'  )
				histogram_na_prob(lowerCAmelCase_						,      lowerCAmelCase_						,      OPTS.out_image_dir						,      'noAns'  )
		if OPTS.out_file:
				with open(OPTS.out_file						,      'w'  ) as f:
						json.dump(lowerCAmelCase_						,      lowerCAmelCase_  )
		else:
				print(json.dumps(lowerCAmelCase_						,      indent=2  )  )


if __name__ == "__main__":
							snake_case__						      =       parse_args()
							if OPTS.out_image_dir:
														import matplotlib

														matplotlib.use('''Agg''')
														import matplotlib.pyplot as plt
							main()









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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		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"  )      ->				List[str]:
		if model_type == "text":
				_a					:				List[str]				=							BarkSemanticModel
				_a					:				Optional[Any]				=							BarkSemanticConfig
				_a					:				Any				=							BarkSemanticGenerationConfig
		elif model_type == "coarse":
				_a					:				Tuple				=							BarkCoarseModel
				_a					:				str				=							BarkCoarseConfig
				_a					:				str				=							BarkCoarseGenerationConfig
		elif model_type == "fine":
				_a					:				List[str]				=							BarkFineModel
				_a					:				Optional[Any]				=							BarkFineConfig
				_a					:				str				=							BarkFineGenerationConfig
		else:
				raise NotImplementedError()
		_a					:				Dict				=							f"""{model_type}_small""" if use_small else model_type
		_a					:				Union[str, 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']  )
		_a					:				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					:				Dict				=							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[Any]				=							model_args.pop('n_head'  )
		_a					:				Any				=							model_args.pop('n_embd'  )
		_a					:				List[Any]				=							model_args.pop('n_layer'  )

		_a					:				Optional[int]				=							ConfigClass(**checkpoint['model_args']  )
		_a					:				List[str]				=							ModelClass(config=lowerCAmelCase_  )
		_a					:				Tuple				=							GenerationConfigClass()

		_a					:				Optional[Any]				=							model_generation_config
		_a					:				Optional[Any]				=							checkpoint['model']
		# fixup checkpoint
		_a					:				int				=							'_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					:				str				=							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					:				List[Any]				=							state_dict.pop(lowerCAmelCase_  )

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

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

		_a					:				Tuple				=							_get_ckpt_path(lowerCAmelCase_						,      use_small=lowerCAmelCase_  )
		_a					:				List[Any]				=							_load_model(lowerCAmelCase_						,      lowerCAmelCase_						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

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

		if model_type == "text":
				_a					:				int				=							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					:				List[str]				=							10

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

				_a					:				Tuple				=							model(lowerCAmelCase_  )

				# take last logits
				_a					:				Optional[int]				=							output_new_model_total.logits[:, [-1], :]

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

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

				_a					:				List[str]				=							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_						,      )      ->				Any:
		_a					:				Any				=							os.path.join(lowerCAmelCase_						,      lowerCAmelCase_  )

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

		_a					:				str				=							BarkSemanticModel.from_pretrained(lowerCAmelCase_  )
		_a					:				Dict				=							BarkCoarseModel.from_pretrained(lowerCAmelCase_  )
		_a					:				int				=							BarkFineModel.from_pretrained(lowerCAmelCase_  )
		_a					:				List[Any]				=							EncodecModel.from_pretrained('facebook/encodec_24khz'  )

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

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

		_a					:				Optional[Any]				=							BarkModel(lowerCAmelCase_  )

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

		_a					:				List[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)




