
"""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 	a   (			self							:			int			)->       List[str]:



      """simple docstring"""

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




 def 	a   (			self							:			Tuple  , **a_							:			List[Any]			)->       Optional[Any]:



      """simple docstring"""

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




 def 	a   (			self							:			Tuple			)->       Tuple:



      """simple docstring"""

      shutil.rmtree(self.tmpdirname			)




 def 	a   (			self							:			int			)->       Dict:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	self.get_tokenizer()

      UpperCAmelCase_    :     Optional[int]            =	BarkProcessor(tokenizer=__lowerCAmelCase			)

      processor.save_pretrained(self.tmpdirname			)
      UpperCAmelCase_    :     int            =	BarkProcessor.from_pretrained(self.tmpdirname			)

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




 @slow
 def 	a   (			self							:			Tuple			)->       List[str]:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	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[int]            =	self.get_tokenizer(bos_token="""(BOS)"""  , eos_token="""(EOS)"""			)

      UpperCAmelCase_    :     Optional[int]            =	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							:			List[str]			)->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	BarkProcessor.from_pretrained(
          pretrained_processor_name_or_path=self.checkpoint  , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  , )

      UpperCAmelCase_    :     Optional[Any]            =	35
      UpperCAmelCase_    :     List[str]            =	2
      UpperCAmelCase_    :     List[Any]            =	8

      UpperCAmelCase_    :     str            =	{
          """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
      UpperCAmelCase_    :     Optional[Any]            =	processor(text=self.input_string  , voice_preset=__lowerCAmelCase			)

      UpperCAmelCase_    :     Dict            =	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
      UpperCAmelCase_    :     Any            =	os.path.join(self.tmpdirname  , """file.npz"""			)
      np.savez(__lowerCAmelCase  , **__lowerCAmelCase			)
      UpperCAmelCase_    :     str            =	processor(text=self.input_string  , voice_preset=__lowerCAmelCase			)
      UpperCAmelCase_    :     Union[str, 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
      UpperCAmelCase_    :     int            =	processor(text=self.input_string  , voice_preset=self.voice_preset			)






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



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	self.get_tokenizer()

      UpperCAmelCase_    :     str            =	BarkProcessor(tokenizer=__lowerCAmelCase			)

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

      UpperCAmelCase_    :     Optional[Any]            =	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()			)


"""simple docstring"""


import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs






@pytest.mark.parametrize(
    """kwargs, expected""" ,[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(lowercase ,i + 1		) for i in range(10		)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 ,4		), range(4 ,7		), range(7 ,10		)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 ,1		), range(1 ,2		), range(2 ,3		)]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _distribute_shards(**lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected""" ,[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _split_gen_kwargs(lowercase ,lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, expected""" ,[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  if expected is RuntimeError:
    with pytest.raises(lowercase		):
      _number_of_shards_in_gen_kwargs(lowercase		)
  else:
    _UpperCAmelCase    			=   _number_of_shards_in_gen_kwargs(lowercase		)
    assert out == expected
