





'''simple docstring'''



import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       _snake_case     		=				"ylacombe/bark-small"
       _snake_case     		=				tempfile.mkdtemp()
       _snake_case     		=				"en_speaker_1"
       _snake_case     		=				"This is a test string"
       _snake_case     		=				"speaker_embeddings_path.json"
       _snake_case     		=				"speaker_embeddings"

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return AutoTokenizer.from_pretrained(self.checkpoint						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       shutil.rmtree(self.tmpdirname	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BarkProcessor(tokenizer=lowerCamelCase	)

       processor.save_pretrained(self.tmpdirname	)
       _snake_case     		=				BarkProcessor.from_pretrained(self.tmpdirname	)

       self.assertEqual(processor.tokenizer.get_vocab()						,   tokenizer.get_vocab()	)

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint						,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						,   )
       processor.save_pretrained(
           self.tmpdirname						,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						,   speaker_embeddings_directory=self.speaker_embeddings_directory						,   )

       _snake_case     		=				self.get_tokenizer(bos_token="(BOS)"						,   eos_token="(EOS)"	)

       _snake_case     		=				BarkProcessor.from_pretrained(
           self.tmpdirname						,   self.speaker_embeddings_dict_path						,   bos_token="(BOS)"						,   eos_token="(EOS)"						,   )

       self.assertEqual(processor.tokenizer.get_vocab()						,   tokenizer_add_kwargs.get_vocab()	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint						,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						,   )

       _snake_case     		=				35
       _snake_case     		=				2
       _snake_case     		=				8

       _snake_case     		=				{
           "semantic_prompt": np.ones(lowerCamelCase	),
           "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)	),
           "fine_prompt": np.ones((nb_codebooks_total, seq_len)	),
       }

       # test providing already loaded voice_preset
       _snake_case     		=				processor(text=self.input_string						,   voice_preset=lowerCamelCase	)

       _snake_case     		=				inputs["history_prompt"]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()						,   processed_voice_preset.get(lowerCamelCase						,   np.array([]	)	).tolist()	)

       # test loading voice preset from npz file
       _snake_case     		=				os.path.join(self.tmpdirname						,   "file.npz"	)
       np.savez(lowerCamelCase						,   **lowerCamelCase	)
       _snake_case     		=				processor(text=self.input_string						,   voice_preset=lowerCamelCase	)
       _snake_case     		=				inputs["history_prompt"]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()						,   processed_voice_preset.get(lowerCamelCase						,   np.array([]	)	).tolist()	)

       # test loading voice preset from the hub
       _snake_case     		=				processor(text=self.input_string						,   voice_preset=self.voice_preset	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.get_tokenizer()

       _snake_case     		=				BarkProcessor(tokenizer=lowerCamelCase	)

       _snake_case     		=				processor(text=self.input_string	)

       _snake_case     		=				tokenizer(
           self.input_string						,   padding="max_length"						,   max_length=256						,   add_special_tokens=lowerCamelCase						,   return_attention_mask=lowerCamelCase						,   return_token_type_ids=lowerCamelCase						,   )

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]						,   encoded_processor[key].squeeze().tolist()	)









'''simple docstring'''



import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
 from scipy.stats import pearsonr, spearmanr
 from sklearn.metrics import fa_score, matthews_corrcoef


__magic_name__   :						List[str]    =			(
    """This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
    """library. You can have a look at this example script for pointers: """
    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  return (preds == labels).mean()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__       )
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  _snake_case     		=				spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       ), f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''
  if task_name == "cola":
    return {"mcc": matthews_corrcoef(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "sst-2":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mrpc":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "sts-b":
    return pearson_and_spearman(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "qqp":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "mnli":
    return {"mnli/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mnli-mm":
    return {"mnli-mm/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "qnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "rte":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "wnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "hans":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
    raise ValueError(f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''       )
  if task_name == "xnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )


