"""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       ):





						def  snake_case__		(				self   )    -> Union[str, Any]:
													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						,**__UpperCAmelCase   )    -> int:
													return AutoTokenizer.from_pretrained(self.checkpoint						,**__lowercase   )





						def  snake_case__		(				self   )    -> Any:
													shutil.rmtree(self.tmpdirname   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							self.get_tokenizer()

													A__		   =							BarkProcessor(tokenizer=__lowercase   )

													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   )    -> str:
													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   )    -> str:
													A__		   =							BarkProcessor.from_pretrained(
													    pretrained_processor_name_or_path=self.checkpoint						,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path						,)

													A__		   =							35
													A__		   =							2
													A__		   =							8

													A__		   =							{
													    """semantic_prompt""": np.ones(__lowercase   ),
													    """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=__lowercase   )

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

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

													for key in voice_preset:
																				self.assertListEqual(voice_preset[key].tolist()						,processed_voice_preset.get(__lowercase						,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   )    -> Any:
													A__		   =							self.get_tokenizer()

													A__		   =							BarkProcessor(tokenizer=__lowercase   )

													A__		   =							processor(text=self.input_string   )

													A__		   =							tokenizer(
													    self.input_string						,padding='max_length'						,max_length=2_56						,add_special_tokens=__lowercase						,return_attention_mask=__lowercase						,return_token_type_ids=__lowercase						,)

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













# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		:       Union[str, Any]			     = {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       Tuple			     = ["TimmBackbone"]


if TYPE_CHECKING:
       from .configuration_timm_backbone import TimmBackboneConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timm_backbone import TimmBackbone

else:
       import sys

       a		:       List[Any]			     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


