


'''simple docstring'''






import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


a_					:    Tuple          =    "__DUMMY_TRANSFORMERS_USER__"
a_					:    List[Any]          =    "Dummy User"
a_					:    str          =    "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"

a_					:    Optional[int]          =    "https://hub-ci.huggingface.co"
a_					:    Any          =    CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
a_					:    Tuple          =    CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
a_					:    Union[str, Any]          =    Path("~/.huggingface/hub_ci_token").expanduser()






@pytest.fixture
def 	_A				(lowerCAmelCase__  :Optional[int]		)   ->  Tuple:




     '''simple docstring'''



     monkeypatch.setattr(
         'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'				,						lowerCAmelCase__		)






@pytest.fixture
def 	_A				(lowerCAmelCase__  :Dict		)   ->  Union[str, Any]:




     '''simple docstring'''



     monkeypatch.setattr('datasets.config.HF_ENDPOINT'				,						lowerCAmelCase__		)
     monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'				,						lowerCAmelCase__		)






@pytest.fixture
def 	_A				(lowerCAmelCase__  :Tuple		)   ->  Tuple:




     '''simple docstring'''



     monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'				,						lowerCAmelCase__		)






@pytest.fixture
def 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[Any]		)   ->  Union[str, Any]:




     '''simple docstring'''



     HfFolder.save_token(lowerCAmelCase__		)
     yield
     HfFolder.delete_token()






@pytest.fixture(scope='session'		)
def 	_A				()   ->  Optional[Any]:




     '''simple docstring'''



     return HfApi(endpoint=lowerCAmelCase__		)






@pytest.fixture(scope='session'		)
def 	_A				(lowerCAmelCase__  :HfApi		)   ->  Any:




     '''simple docstring'''



     _a			      =							HfFolder.get_token()
     HfFolder.save_token(lowerCAmelCase__		)
     yield CI_HUB_USER_TOKEN
     if previous_token is not None:
          HfFolder.save_token(lowerCAmelCase__		)






@pytest.fixture
def 	_A				(lowerCAmelCase__  :str		)   ->  str:




     '''simple docstring'''



     def _cleanup_repo(lowerCAmelCase__  :List[Any]		):
          hf_api.delete_repo(lowerCAmelCase__				,						token=lowerCAmelCase__				,						repo_type='dataset'		)

     return _cleanup_repo






@pytest.fixture
def 	_A				(lowerCAmelCase__  :Optional[int]		)   ->  List[str]:




     '''simple docstring'''



     @contextmanager
     def _temporary_repo(lowerCAmelCase__  :List[str]		):
          try:
               yield repo_id
          finally:
               cleanup_repo(lowerCAmelCase__		)

     return _temporary_repo






@pytest.fixture(scope='session'		)
def 	_A				(lowerCAmelCase__  :HfApi				,						lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :List[Any]		)   ->  List[Any]:




     '''simple docstring'''



     _a			      =							f'repo_txt_data-{int(time.time() * 10E3		)}'
     _a			      =							f'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase__				,						token=lowerCAmelCase__				,						repo_type='dataset'				,						private=lowerCAmelCase__		)
     hf_api.upload_file(
         token=lowerCAmelCase__				,						path_or_fileobj=str(lowerCAmelCase__		)				,						path_in_repo='data/text_data.txt'				,						repo_id=lowerCAmelCase__				,						repo_type='dataset'				,						)
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase__				,						token=lowerCAmelCase__				,						repo_type='dataset'		)
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass






@pytest.fixture()
def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :List[str]		)   ->  Optional[int]:




     '''simple docstring'''



     return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='session'		)
def 	_A				(lowerCAmelCase__  :HfApi				,						lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :Dict		)   ->  Any:




     '''simple docstring'''



     _a			      =							f'repo_zipped_txt_data-{int(time.time() * 10E3		)}'
     _a			      =							f'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase__				,						token=lowerCAmelCase__				,						repo_type='dataset'				,						private=lowerCAmelCase__		)
     hf_api.upload_file(
         token=lowerCAmelCase__				,						path_or_fileobj=str(lowerCAmelCase__		)				,						path_in_repo='data.zip'				,						repo_id=lowerCAmelCase__				,						repo_type='dataset'				,						)
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase__				,						token=lowerCAmelCase__				,						repo_type='dataset'		)
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass






@pytest.fixture()
def 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :Union[str, Any]				,						lowerCAmelCase__  :Any		)   ->  Any:




     '''simple docstring'''



     return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='session'		)
def 	_A				(lowerCAmelCase__  :HfApi				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :Tuple		)   ->  Dict:




     '''simple docstring'''



     _a			      =							f'repo_zipped_img_data-{int(time.time() * 10E3		)}'
     _a			      =							f'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase__				,						token=lowerCAmelCase__				,						repo_type='dataset'				,						private=lowerCAmelCase__		)
     hf_api.upload_file(
         token=lowerCAmelCase__				,						path_or_fileobj=str(lowerCAmelCase__		)				,						path_in_repo='data.zip'				,						repo_id=lowerCAmelCase__				,						repo_type='dataset'				,						)
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase__				,						token=lowerCAmelCase__				,						repo_type='dataset'		)
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass






@pytest.fixture()
def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :Union[str, Any]		)   ->  Dict:




     '''simple docstring'''



     return hf_private_dataset_repo_zipped_img_data_





'''simple docstring'''






from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""EncodecFeatureExtractor"""
 _lowerCAmelCase 					=	("""T5Tokenizer""", """T5TokenizerFast""")
 def __init__( self      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      super().__init__(__magic_name__      ,				__magic_name__						)
      _a			      =							self.feature_extractor
      _a			      =							False
 def 							__UpperCAmelCase		( self      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=True						)					->							Tuple:
      return self.tokenizer.get_decoder_prompt_ids(task=__magic_name__      ,				language=__magic_name__      ,				no_timestamps=__magic_name__						)
 def __call__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[str]:
      # For backward compatibility
      if self._in_target_context_manager:
           return self.current_processor(*__magic_name__      ,				**__magic_name__						)

      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('sampling_rate'      ,				__magic_name__						)
      _a			      =							kwargs.pop('text'      ,				__magic_name__						)
      if len(__magic_name__						) > 0:
           _a			      =							args[0]
           _a			      =							args[1:]

      if audio is None and text is None:
           raise ValueError('You need to specify either an `audio` or `text` input to process.'						)

      if text is not None:
           _a			      =							self.tokenizer(__magic_name__      ,				**__magic_name__						)

      if audio is not None:
           _a			      =							self.feature_extractor(__magic_name__      ,				*__magic_name__      ,				sampling_rate=__magic_name__      ,				**__magic_name__						)

      if audio is None:
           return inputs

      elif text is None:
           return audio_inputs

      else:
           _a			      =							audio_inputs['input_values']
           if "padding_mask" in audio_inputs:
                _a			      =							audio_inputs['padding_mask']
           return inputs
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('padding_mask'      ,				__magic_name__						)

      if len(__magic_name__						) > 0:
           _a			      =							args[0]
           _a			      =							args[1:]

      if audio_values is not None:
           return self._decode_audio(__magic_name__      ,				padding_mask=__magic_name__						)
      else:
           return self.tokenizer.batch_decode(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[Any]:
      return self.tokenizer.decode(*__magic_name__      ,				**__magic_name__						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							List[np.ndarray]:
      _a			      =							to_numpy(__magic_name__						)
      _a      ,   _a      ,   _a			      =							audio_values.shape

      if padding_mask is None:
           return list(__magic_name__						)

      _a			      =							to_numpy(__magic_name__						)

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      _a			      =							seq_len - padding_mask.shape[-1]
      _a			      =							1 - self.feature_extractor.padding_value
      _a			      =							np.pad(__magic_name__      ,				((0, 0), (0, difference))      ,				'constant'      ,				constant_values=__magic_name__						)

      _a			      =							audio_values.tolist()
      for i in range(__magic_name__						):
           _a			      =							np.asarray(audio_values[i]						)[
               padding_mask[i][None, :] != self.feature_extractor.padding_value
           ]
           _a			      =							sliced_audio.reshape(__magic_name__      ,				-1						)

      return audio_values

