

"""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      __lowercase			(    unittest.TestCase					):

  def    __lowercase       (  self		:			Any    ):






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

  def    __lowercase       (  self		:			Tuple					,**A		:			List[Any]    ):






   '''simple docstring'''
   return AutoTokenizer.from_pretrained(self.checkpoint					,**A    )

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   shutil.rmtree(self.tmpdirname    )

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.get_tokenizer()

   UpperCAmelCase__					:  Any					   =					BarkProcessor(tokenizer=A    )

   processor.save_pretrained(self.tmpdirname    )
   UpperCAmelCase__					:  List[str]					   =					BarkProcessor.from_pretrained(self.tmpdirname    )

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

  @slow
  def    __lowercase       (  self		:			Dict    ):






   '''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__					:  int					   =					self.get_tokenizer(bos_token="""(BOS)"""					,eos_token="""(EOS)"""    )

   UpperCAmelCase__					:  List[str]					   =					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    __lowercase       (  self		:			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__					:  str					   =					35
   UpperCAmelCase__					:  Dict					   =					2
   UpperCAmelCase__					:  Tuple					   =					8

   UpperCAmelCase__					:  List[Any]					   =					{
       """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
   UpperCAmelCase__					:  Optional[Any]					   =					processor(text=self.input_string					,voice_preset=A    )

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




  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					self.get_tokenizer()

   UpperCAmelCase__					:  str					   =					BarkProcessor(tokenizer=A    )

   UpperCAmelCase__					:  List[str]					   =					processor(text=self.input_string    )

   UpperCAmelCase__					:  List[str]					   =					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()    )








"""simple docstring"""




from datetime import datetime as dt
import os

from github import Github


__UpperCAmelCase =				[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					Github(os.environ["""GITHUB_TOKEN"""]      )
 UpperCAmelCase__					:  int					   =					g.get_repo("""huggingface/transformers"""      )
 UpperCAmelCase__					:  Optional[int]					   =					repo.get_issues(state="""open"""      )

 for issue in open_issues:
  UpperCAmelCase__					:  Any					   =					sorted([comment for comment in issue.get_comments()]							,    key=lambda __UpperCamelCase      : i.created_at							,    reverse=__UpperCamelCase      )
  UpperCAmelCase__					:  Union[str, Any]					   =					comments[0] if len(__UpperCamelCase      ) > 0 else None
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and (dt.utcnow() - issue.updated_at).days > 7
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state="""closed"""      )
  elif (
      (dt.utcnow() - issue.updated_at).days > 23
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would add stale comment to {issue.number}")
   issue.create_comment(
       """This issue has been automatically marked as stale because it has not had """
       """recent activity. If you think this still needs to be addressed """
       """please comment on this thread.\n\nPlease note that issues that do not follow the """
       """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
       """are likely to be ignored."""      )


if __name__ == "__main__":
      main()





