
'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					42


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline




'''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 ):

       '''simple docstring'''

       a_      :	int      =							"""ylacombe/bark-small"""
       a_      :	Dict      =							tempfile.mkdtemp()
       a_      :	Union[str, Any]      =							"""en_speaker_1"""
       a_      :	Dict      =							"""This is a test string"""
       a_      :	Optional[int]      =							"""speaker_embeddings_path.json"""
       a_      :	int      =							"""speaker_embeddings"""


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       return AutoTokenizer.from_pretrained(self.checkpoint							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       shutil.rmtree(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.get_tokenizer()

       a_      :	Optional[Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       processor.save_pretrained(self.tmpdirname )
       a_      :	List[str]      =							BarkProcessor.from_pretrained(self.tmpdirname )

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


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	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							,		)

       a_      :	List[str]      =							self.get_tokenizer(bos_token="""(BOS)"""							,		eos_token="""(EOS)""" )

       a_      :	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 ):

       '''simple docstring'''

       a_      :	List[Any]      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)

       a_      :	Dict      =							35
       a_      :	List[Any]      =							2
       a_      :	Optional[int]      =							8

       a_      :	int      =							{
           """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
       a_      :	Optional[Any]      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )

       a_      :	Tuple      =							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
       a_      :	Any      =							os.path.join(self.tmpdirname							,		"""file.npz""" )
       np.savez(lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )
       a_      :	List[Any]      =							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
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=self.voice_preset )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_tokenizer()

       a_      :	Union[str, Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       a_      :	Optional[int]      =							processor(text=self.input_string )

       a_      :	Optional[int]      =							tokenizer(
           self.input_string							,		padding="""max_length"""							,		max_length=2_56							,		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() )


