



"""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 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Any											=    '''ylacombe/bark-small'''
         _UpperCAmelCase							:  Any											=    tempfile.mkdtemp()
         _UpperCAmelCase							:  Union[str, Any]											=    '''en_speaker_1'''
         _UpperCAmelCase							:  Optional[Any]											=    '''This is a test string'''
         _UpperCAmelCase							:  List[str]											=    '''speaker_embeddings_path.json'''
         _UpperCAmelCase							:  Optional[int]											=    '''speaker_embeddings'''





     def  __lowerCAmelCase	(					self       ,	**A		)							->					str:
         return AutoTokenizer.from_pretrained(self.checkpoint       ,	**A		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         shutil.rmtree(self.tmpdirname		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  str											=    self.get_tokenizer()

         _UpperCAmelCase							:  Tuple											=    BarkProcessor(tokenizer=A		)

         processor.save_pretrained(self.tmpdirname		)
         _UpperCAmelCase							:  Union[str, Any]											=    BarkProcessor.from_pretrained(self.tmpdirname		)

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





     @slow
     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Optional[Any]											=    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       ,	)

         _UpperCAmelCase							:  Any											=    self.get_tokenizer(bos_token='''(BOS)'''       ,	eos_token='''(EOS)'''		)

         _UpperCAmelCase							:  Any											=    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  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Dict											=    BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint       ,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,	)

         _UpperCAmelCase							:  Dict											=    3_5
         _UpperCAmelCase							:  Any											=    2
         _UpperCAmelCase							:  Any											=    8

         _UpperCAmelCase							:  Any											=    {
             '''semantic_prompt''': np.ones(A		),
             '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)		),
             '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)		),
         }

         # test providing already loaded voice_preset
         _UpperCAmelCase							:  Optional[Any]											=    processor(text=self.input_string       ,	voice_preset=A		)

         _UpperCAmelCase							:  Optional[int]											=    inputs['''history_prompt''']
         for key in voice_preset:
             self.assertListEqual(voice_preset[key].tolist()       ,	processed_voice_preset.get(A       ,	np.array([]		)		).tolist()		)

         # test loading voice preset from npz file
         _UpperCAmelCase							:  str											=    os.path.join(self.tmpdirname       ,	'''file.npz'''		)
         np.savez(A       ,	**A		)
         _UpperCAmelCase							:  Optional[Any]											=    processor(text=self.input_string       ,	voice_preset=A		)
         _UpperCAmelCase							:  str											=    inputs['''history_prompt''']

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

         # test loading voice preset from the hub
         _UpperCAmelCase							:  Optional[int]											=    processor(text=self.input_string       ,	voice_preset=self.voice_preset		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  List[str]											=    self.get_tokenizer()

         _UpperCAmelCase							:  Optional[Any]											=    BarkProcessor(tokenizer=A		)

         _UpperCAmelCase							:  str											=    processor(text=self.input_string		)

         _UpperCAmelCase							:  Tuple											=    tokenizer(
             self.input_string       ,	padding='''max_length'''       ,	max_length=2_5_6       ,	add_special_tokens=A       ,	return_attention_mask=A       ,	return_token_type_ids=A       ,	)

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







"""simple docstring"""
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase    :str     =					object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase    :str     =					object()





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  Dict											=    tuple((re.compile(x + '''$'''							) for x in qs)							)
    for i in range(len(UpperCamelCase__							) - len(UpperCamelCase__							) + 1							):
        _UpperCAmelCase							:  str											=    [x.match(UpperCamelCase__							) for x, y in zip(UpperCamelCase__     ,       ks[i:]							)]
        if matches and all(UpperCamelCase__							):
            return True
    return False





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]							):
    def replace(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Tuple							):
        for rule, replacement in rules:
            if _match(UpperCamelCase__     ,       UpperCamelCase__							):
                return replacement
        return val

    return replace





def  lowerCamelCase_						():
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        (("transformer", "wte", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("attention", "out_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("mlp", "c_fc", "bias"), P('''mp'''							)),
        (("mlp", "c_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]





def  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[str]											=    _get_partition_rules()
    _UpperCAmelCase							:  List[str]											=    _replacement_rules(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    {k: _unmatched for k in flatten_dict(UpperCamelCase__							)}
    _UpperCAmelCase							:  int											=    {k: replace(UpperCamelCase__     ,       UpperCamelCase__							) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(UpperCamelCase__							)							)


