'''simple docstring'''






from typing import List, Optional

import numpy as np

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







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "EncodecFeatureExtractor"
   __lowerCAmelCase         =   ("T5Tokenizer", "T5TokenizerFast")
   def __init__(      self      :			Union[str, Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			Optional[int]:
        super().__init__(UpperCamelCase__, UpperCamelCase__     )
        _A    		=  self.feature_extractor
        _A    		=  False
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			Optional[Any]=None, UpperCamelCase__      :			Any=None, UpperCamelCase__      :			List[str]=True     )  ->			str:
        return self.tokenizer.get_decoder_prompt_ids(task=UpperCamelCase__, language=UpperCamelCase__, no_timestamps=UpperCamelCase__     )
   def __call__(      self      :			Any, *UpperCamelCase__      :			Union[str, Any], **UpperCamelCase__      :			Dict     )  ->			Union[str, Any]:
        # For backward compatibility
        if self._in_target_context_manager:
             return self.current_processor(*UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  kwargs.pop('audio', UpperCamelCase__     )
        _A    		=  kwargs.pop('sampling_rate', UpperCamelCase__     )
        _A    		=  kwargs.pop('text', UpperCamelCase__     )
        if len(UpperCamelCase__     ) > 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(UpperCamelCase__, **UpperCamelCase__     )

        if audio is not None:
             _A    		=  self.feature_extractor(UpperCamelCase__, *UpperCamelCase__, sampling_rate=UpperCamelCase__, **UpperCamelCase__     )

        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      :			Optional[int], *UpperCamelCase__      :			Optional[Any], **UpperCamelCase__      :			Optional[Any]     )  ->			Tuple:
        _A    		=  kwargs.pop('audio', UpperCamelCase__     )
        _A    		=  kwargs.pop('padding_mask', UpperCamelCase__     )

        if len(UpperCamelCase__     ) > 0:
             _A    		=  args[0]
             _A    		=  args[1:]

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

   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Optional = None     )  ->			List[np.ndarray]:
        _A    		=  to_numpy(UpperCamelCase__     )
        _A		,       _A		,       _A    		=  audio_values.shape

        if padding_mask is None:
             return list(UpperCamelCase__     )

        _A    		=  to_numpy(UpperCamelCase__     )

        # 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(UpperCamelCase__, ((0, 0), (0, difference)), 'constant', constant_values=UpperCamelCase__     )

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

        return audio_values




'''simple docstring'''






import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


_UpperCAmelCase    :     Optional[int]         =       '''\
    Text data.
    Second line of data.'''

_UpperCAmelCase    :     Optional[Any]         =       '''file'''





@pytest.fixture(scope='session'				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _A    		=  tmp_path_factory.mktemp('data'				) / (FILE_PATH + '.zstd')
     _A    		=  bytes(__snake_case   ,		'utf-8'				)
     with zstd.open(__snake_case   ,		'wb'				) as f:
          f.write(__snake_case				)
     return path





@pytest.fixture
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     with open(os.path.join(tmpfs.local_root_dir   ,		__snake_case				)   ,		'w'				) as f:
          f.write(__snake_case				)
     return FILE_PATH





@pytest.mark.parametrize('compression_format'   ,		['gzip', 'xz', 'zstd']				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict   ,		__snake_case							:			str   ,		__snake_case							:			Any   ,		__snake_case							:			Any   ,		__snake_case							:			List[str]   ,		__snake_case							:			List[str]				):
     _A    		=  {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
     _A    		=  input_paths[compression_format]
     _A    		=  tmp_path / 'cache'
     _A    		=  DownloadConfig(cache_dir=__snake_case   ,		extract_compressed_file=__snake_case				)
     _A    		=  cached_path(__snake_case   ,		download_config=__snake_case				)
     with open(__snake_case				) as f:
          _A    		=  f.read()
     with open(__snake_case				) as f:
          _A    		=  f.read()
     assert extracted_file_content == expected_file_content





@pytest.mark.parametrize('default_extracted'   ,		[True, False]				)
@pytest.mark.parametrize('default_cache_dir'   ,		[True, False]				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]   ,		__snake_case							:			List[str]   ,		__snake_case							:			Union[str, Any]   ,		__snake_case							:			int   ,		__snake_case							:			List[str]				):
     _A    		=  'custom_cache'
     _A    		=  'custom_extracted_dir'
     _A    		=  tmp_path / 'custom_extracted_path'
     if default_extracted:
          _A    		=  ('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
     else:
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'   ,		__snake_case				)
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,		str(__snake_case				)				)
          _A    		=  custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

     _A    		=  xz_file
     _A    		=  (
         DownloadConfig(extract_compressed_file=__snake_case				)
         if default_cache_dir
         else DownloadConfig(cache_dir=tmp_path / custom_cache_dir   ,		extract_compressed_file=__snake_case				)
     )
     _A    		=  cached_path(__snake_case   ,		download_config=__snake_case				)
     assert Path(__snake_case				).parent.parts[-2:] == expected





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str				):
     # absolute path
     _A    		=  str(Path(__snake_case				).resolve()				)
     assert cached_path(__snake_case				) == text_file
     # relative path
     _A    		=  str(Path(__snake_case				).resolve().relative_to(Path(os.getcwd()				)				)				)
     assert cached_path(__snake_case				) == text_file





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]				):
     # absolute path
     _A    		=  str(tmp_path.resolve() / '__missing_file__.txt'				)
     with pytest.raises(__snake_case				):
          cached_path(__snake_case				)
     # relative path
     _A    		=  './__missing_file__.txt'
     with pytest.raises(__snake_case				):
          cached_path(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  get_from_cache(F'tmp://{tmpfs_file}'				)
     with open(__snake_case				) as f:
          _A    		=  f.read()
     assert output_file_content == FILE_CONTENT





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      ):
     with pytest.raises(__snake_case				):
          cached_path('https://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          http_get('https://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          http_head('https://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          ftp_get('ftp://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          ftp_head('ftp://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          fsspec_get('s3://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          fsspec_head('s3://huggingface.co'				)



