





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    :str		)   ->   List[Any]:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      """ylacombe/bark-small"""
         SCREAMING_SNAKE_CASE__			=      tempfile.mkdtemp()
         SCREAMING_SNAKE_CASE__			=      """en_speaker_1"""
         SCREAMING_SNAKE_CASE__			=      """This is a test string"""
         SCREAMING_SNAKE_CASE__			=      """speaker_embeddings_path.json"""
         SCREAMING_SNAKE_CASE__			=      """speaker_embeddings"""


       def      _snake_case  (							self    :List[str]							,				**__A    :List[str]		)   ->   List[str]:



         """simple docstring"""



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


       def      _snake_case  (							self    :List[str]		)   ->   Tuple:



         """simple docstring"""



         shutil.rmtree(self.tmpdirname		)


       def      _snake_case  (							self    :List[str]		)   ->   Optional[int]:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

         SCREAMING_SNAKE_CASE__			=      BarkProcessor(tokenizer=__A		)

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

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


       @slow
       def      _snake_case  (							self    :Any		)   ->   str:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      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							,				)

         SCREAMING_SNAKE_CASE__			=      self.get_tokenizer(bos_token="""(BOS)"""							,				eos_token="""(EOS)"""		)

         SCREAMING_SNAKE_CASE__			=      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    :Dict		)   ->   List[str]:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint							,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,				)

         SCREAMING_SNAKE_CASE__			=      35
         SCREAMING_SNAKE_CASE__			=      2
         SCREAMING_SNAKE_CASE__			=      8

         SCREAMING_SNAKE_CASE__			=      {
             """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
         SCREAMING_SNAKE_CASE__			=      processor(text=self.input_string							,				voice_preset=__A		)

         SCREAMING_SNAKE_CASE__			=      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
         SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				"""file.npz"""		)
         np.savez(__A							,				**__A		)
         SCREAMING_SNAKE_CASE__			=      processor(text=self.input_string							,				voice_preset=__A		)
         SCREAMING_SNAKE_CASE__			=      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
         SCREAMING_SNAKE_CASE__			=      processor(text=self.input_string							,				voice_preset=self.voice_preset		)


       def      _snake_case  (							self    :str		)   ->   Union[str, Any]:



         """simple docstring"""



         SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

         SCREAMING_SNAKE_CASE__			=      BarkProcessor(tokenizer=__A		)

         SCREAMING_SNAKE_CASE__			=      processor(text=self.input_string		)

         SCREAMING_SNAKE_CASE__			=      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()		)






import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow




@is_pipeline_test
@require_torch
class   UpperCamelCase_       (     unittest.TestCase    ):


							@require_torch
							def      _snake_case  (							self    :Dict		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""hf-internal-testing/tiny-clap-htsat-unfused"""		)
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      audio_classifier(__A							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[{"""score""": 0.5_0_1, """label""": """Sound of a dog"""}, {"""score""": 0.4_9_9, """label""": """Sound of vaccum cleaner"""}]							,				)


							@unittest.skip("""No models are available in TF"""		)
							def      _snake_case  (							self    :Dict		)   ->   List[str]:



									"""simple docstring"""



									pass


							@slow
							@require_torch
							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""laion/clap-htsat-unfused"""							,				)
									# This is an audio of a dog
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      audio_classifier(__A							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)

									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									        {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									    ]							,				)

									SCREAMING_SNAKE_CASE__			=      audio_classifier([audio] * 5							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        [
									            {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									            {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									        ],
									    ]
									    * 5							,				)
									SCREAMING_SNAKE_CASE__			=      audio_classifier(
									    [audio] * 5							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]							,				batch_size=5		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        [
									            {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									            {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									        ],
									    ]
									    * 5							,				)


							@unittest.skip("""No models are available in TF"""		)
							def      _snake_case  (							self    :str		)   ->   Optional[int]:



									"""simple docstring"""



									pass