




"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					"laion/clap-htsat-unfused"
						__UpperCAmelCase	: List[str]		=					tempfile.mkdtemp()


			def       a_     (      self      :    Optional[Any]      ,  **UpperCamelCase_      :    Union[str, Any]):


						"""simple docstring"""




						return RobertaTokenizer.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    Union[str, Any]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						return ClapFeatureExtractor.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




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

						__UpperCAmelCase	: List[Any]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Union[str, Any]		=					ClapProcessor.from_pretrained(self.tmpdirname)

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

						self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor.to_json_string())
						self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					ClapProcessor(tokenizer=self.get_tokenizer()      ,  feature_extractor=self.get_feature_extractor())
						processor.save_pretrained(self.tmpdirname)

						__UpperCAmelCase	: int		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")
						__UpperCAmelCase	: Any		=					self.get_feature_extractor(do_normalize=UpperCamelCase_      ,  padding_value=1.0)

						__UpperCAmelCase	: Tuple		=					ClapProcessor.from_pretrained(
						    self.tmpdirname      ,  bos_token="(BOS)"      ,  eos_token="(EOS)"      ,  do_normalize=UpperCamelCase_      ,  padding_value=1.0)

						self.assertEqual(processor.tokenizer.get_vocab()      ,  tokenizer_add_kwargs.get_vocab())
						self.assertIsInstance(processor.tokenizer      ,  UpperCamelCase_)

						self.assertEqual(processor.feature_extractor.to_json_string()      ,  feature_extractor_add_kwargs.to_json_string())
						self.assertIsInstance(processor.feature_extractor      ,  UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_feature_extractor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					floats_list((3, 1000))

						__UpperCAmelCase	: Tuple		=					feature_extractor(UpperCamelCase_      ,  return_tensors="np")
						__UpperCAmelCase	: Any		=					processor(audios=UpperCamelCase_      ,  return_tensors="np")

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()      ,  input_processor[key].sum()      ,  delta=1e-2)


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.get_feature_extractor()
						__UpperCAmelCase	: Optional[int]		=					self.get_tokenizer()

						__UpperCAmelCase	: str		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Any		=					"This is a test string"

						__UpperCAmelCase	: List[Any]		=					processor(text=UpperCamelCase_)

						__UpperCAmelCase	: Dict		=					tokenizer(UpperCamelCase_)

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


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					self.get_feature_extractor()
						__UpperCAmelCase	: List[str]		=					self.get_tokenizer()

						__UpperCAmelCase	: List[str]		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						__UpperCAmelCase	: Tuple		=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__UpperCAmelCase	: int		=					processor.batch_decode(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					tokenizer.batch_decode(UpperCamelCase_)

						self.assertListEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self.get_feature_extractor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: int		=					ClapProcessor(tokenizer=UpperCamelCase_      ,  feature_extractor=UpperCamelCase_)

						self.assertListEqual(
						    processor.model_input_names[2:]      ,  feature_extractor.model_input_names      ,  msg="`processor` and `feature_extractor` model input names do not match"      ,  )








"""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      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    Optional[int]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




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


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer()

						__UpperCAmelCase	: Optional[Any]		=					BarkProcessor(tokenizer=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Optional[int]		=					BarkProcessor.from_pretrained(self.tmpdirname)

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


			@slow
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					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      ,  )

						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")

						__UpperCAmelCase	: Optional[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      :    Union[str, Any]):


						"""simple docstring"""




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

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

						__UpperCAmelCase	: str		=					{
						    "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
						__UpperCAmelCase	: Union[str, Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)

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


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					BarkProcessor(tokenizer=UpperCamelCase_)

						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string)

						__UpperCAmelCase	: List[Any]		=					tokenizer(
						    self.input_string      ,  padding="max_length"      ,  max_length=256      ,  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())


