



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     ):




     def 		snake_case_		(				self):
         A__		 =					'''ylacombe/bark-small'''
         A__		 =					tempfile.mkdtemp()
         A__		 =					'''en_speaker_1'''
         A__		 =					'''This is a test string'''
         A__		 =					'''speaker_embeddings_path.json'''
         A__		 =					'''speaker_embeddings'''




     def 		snake_case_		(				self ,						**a__):
         return AutoTokenizer.from_pretrained(self.checkpoint ,						**_UpperCAmelCase)




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




     def 		snake_case_		(				self):
         A__		 =					self.get_tokenizer()

         A__		 =					BarkProcessor(tokenizer=_UpperCAmelCase)

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

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




     @slow
     def 		snake_case_		(				self):
         A__		 =					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 ,						)

         A__		 =					self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)''')

         A__		 =					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 		snake_case_		(				self):
         A__		 =					BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,						)

         A__		 =					3_5
         A__		 =					2
         A__		 =					8

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

         # test providing already loaded voice_preset
         A__		 =					processor(text=self.input_string ,						voice_preset=_UpperCAmelCase)

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

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

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

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




     def 		snake_case_		(				self):
         A__		 =					self.get_tokenizer()

         A__		 =					BarkProcessor(tokenizer=_UpperCAmelCase)

         A__		 =					processor(text=self.input_string)

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

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





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"T5Config"



def 		lowerCAmelCase__    (					UpperCamelCase_				:       jnp.array ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       int	)->  jnp.ndarray:
    A__		 =					jnp.zeros_like(UpperCamelCase_	)
    A__		 =					shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]	)
    A__		 =					shifted_input_ids.at[:, 0].set(UpperCamelCase_	)

    A__		 =					jnp.where(shifted_input_ids == -1_0_0 ,       UpperCamelCase_ ,       UpperCamelCase_	)
    return shifted_input_ids



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig
