"""simple docstring"""




# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    'pipelines_utils',
    '0.22.0',
    'Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.',
    standard_warn=False,
    stacklevel=3,
)





"""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 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """ylacombe/bark-small"""
       _lowerCAmelCase						:       Optional[Any]  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       int  			=     """en_speaker_1"""
       _lowerCAmelCase						:       List[Any]  			=     """This is a test string"""
       _lowerCAmelCase						:       Any  			=     """speaker_embeddings_path.json"""
       _lowerCAmelCase						:       List[Any]  			=     """speaker_embeddings"""






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






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       int  			=     BarkProcessor(tokenizer=a__  )

       processor.save_pretrained(self.tmpdirname  )
       _lowerCAmelCase						:       str  			=     BarkProcessor.from_pretrained(self.tmpdirname  )

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






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     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				,     )

       _lowerCAmelCase						:       Tuple  			=     self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""  )

       _lowerCAmelCase						:       List[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     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     35
       _lowerCAmelCase						:       Union[str, Any]  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     8

       _lowerCAmelCase						:       Dict  			=     {
           """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
       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string				,     voice_preset=a__  )

       _lowerCAmelCase						:       Tuple  			=     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
       _lowerCAmelCase						:       List[Any]  			=     os.path.join(self.tmpdirname				,     """file.npz"""  )
       np.savez(a__				,     **a__  )
       _lowerCAmelCase						:       List[Any]  			=     processor(text=self.input_string				,     voice_preset=a__  )
       _lowerCAmelCase						:       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 the hub
       _lowerCAmelCase						:       str  			=     processor(text=self.input_string				,     voice_preset=self.voice_preset  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.get_tokenizer()

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor(tokenizer=a__  )

       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string  )

       _lowerCAmelCase						:       Tuple  			=     tokenizer(
           self.input_string				,     padding="""max_length"""				,     max_length=256				,     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()  )




