


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class      __lowerCAmelCase  (			_a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'Wav2Vec2FeatureExtractor'
  _SCREAMING_SNAKE_CASE		     =							'AutoTokenizer'



  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							List[str]	) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__(lowerCAmelCase_     ,					lowerCAmelCase_	)
     snake_case_            =       self.feature_extractor
     snake_case_            =       False



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			Tuple:




     """simple docstring"""


     try:
        return super().from_pretrained(lowerCAmelCase_     ,					**lowerCAmelCase_	)
     except OSError:
        warnings.warn(
            F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
            " include a `tokenizer_class` attribute is deprecated and will be "
            "removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`"
            " attribute to either your `config.json` or `tokenizer_config.json` "
            "file to suppress this warning: "     ,					lowerCAmelCase_     ,					)

        snake_case_            =       WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_     ,					**lowerCAmelCase_	)
        snake_case_            =       WavaVecaCTCTokenizer.from_pretrained(lowerCAmelCase_     ,					**lowerCAmelCase_	)

        return cls(feature_extractor=lowerCAmelCase_     ,					tokenizer=lowerCAmelCase_	)



  def __call__(  self							:							str     ,					*_lowerCAmelCase							:							Union[str, Any]     ,					**_lowerCAmelCase							:							Any	) ->			Optional[int]:




     """simple docstring"""


     if self._in_target_context_manager:
        return self.current_processor(*lowerCAmelCase_     ,					**lowerCAmelCase_	)

     if "raw_speech" in kwargs:
        warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."	)
        snake_case_            =       kwargs.pop("raw_speech"	)
     else:
        snake_case_            =       kwargs.pop("audio"     ,					lowerCAmelCase_	)
     snake_case_            =       kwargs.pop("sampling_rate"     ,					lowerCAmelCase_	)
     snake_case_            =       kwargs.pop("text"     ,					lowerCAmelCase_	)
     if len(lowerCAmelCase_	) > 0:
        snake_case_            =       args[0]
        snake_case_            =       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 audio is not None:
        snake_case_            =       self.feature_extractor(lowerCAmelCase_     ,					*lowerCAmelCase_     ,					sampling_rate=lowerCAmelCase_     ,					**lowerCAmelCase_	)
     if text is not None:
        snake_case_            =       self.tokenizer(lowerCAmelCase_     ,					**lowerCAmelCase_	)

     if text is None:
        return inputs
     elif audio is None:
        return encodings
     else:
        snake_case_            =       encodings["input_ids"]
        return inputs



  def        lowerCAmelCase__	(  self							:							str     ,					*_lowerCAmelCase							:							List[str]     ,					**_lowerCAmelCase							:							Any	) ->			Any:




     """simple docstring"""


     if self._in_target_context_manager:
        return self.current_processor.pad(*lowerCAmelCase_     ,					**lowerCAmelCase_	)

     snake_case_            =       kwargs.pop("input_features"     ,					lowerCAmelCase_	)
     snake_case_            =       kwargs.pop("labels"     ,					lowerCAmelCase_	)
     if len(lowerCAmelCase_	) > 0:
        snake_case_            =       args[0]
        snake_case_            =       args[1:]

     if input_features is not None:
        snake_case_            =       self.feature_extractor.pad(lowerCAmelCase_     ,					*lowerCAmelCase_     ,					**lowerCAmelCase_	)
     if labels is not None:
        snake_case_            =       self.tokenizer.pad(lowerCAmelCase_     ,					**lowerCAmelCase_	)

     if labels is None:
        return input_features
     elif input_features is None:
        return labels
     else:
        snake_case_            =       labels["input_ids"]
        return input_features



  def        lowerCAmelCase__	(  self							:							Optional[int]     ,					*_lowerCAmelCase							:							List[str]     ,					**_lowerCAmelCase							:							List[str]	) ->			Optional[Any]:




     """simple docstring"""
     return self.tokenizer.batch_decode(*lowerCAmelCase_     ,					**lowerCAmelCase_	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					*_lowerCAmelCase							:							Tuple     ,					**_lowerCAmelCase							:							List[Any]	) ->			List[str]:




     """simple docstring"""
     return self.tokenizer.decode(*lowerCAmelCase_     ,					**lowerCAmelCase_	)



  @contextmanager
  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			int:




     """simple docstring"""
     warnings.warn(
         "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
         "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
         "your audio inputs, or in a separate call."	)
     snake_case_            =       True
     snake_case_            =       self.tokenizer
     yield
     snake_case_            =       self.feature_extractor
     snake_case_            =       False










'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case : str  				=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_snake_case : List[str]  				=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							tempfile.mkdtemp()
														os.makedirs(os.path.join(self.diffusers_dir				,     '''schedulers/'''   )   )
														_a      	=							self.diffusers_dir
														shutil.copy(
														    os.path.join(lowerCAmelCase_				,     '''src/diffusers/schedulers/scheduling_ddpm.py'''   )				,     os.path.join(self.diffusers_dir				,     '''schedulers/scheduling_ddpm.py'''   )				,     )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							'''src/diffusers'''
														shutil.rmtree(self.diffusers_dir   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    str=None   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + class_code
														if overwrite_result is not None:
																					_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
														_a      	=							black.Mode(target_versions={black.TargetVersion.PYaa}				,     line_length=1_19   )
														_a      	=							black.format_str(lowerCAmelCase_				,     mode=lowerCAmelCase_   )
														_a      	=							os.path.join(self.diffusers_dir				,     '''new_code.py'''   )
														with open(lowerCAmelCase_				,     '''w'''				,     newline='''\n'''   ) as f:
																					f.write(lowerCAmelCase_   )
														if overwrite_result is None:
																					self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase_   )   ) == 0   )
														else:
																					check_copies.is_copy_consistent(f.name				,     overwrite=lowerCAmelCase_   )
																					with open(lowerCAmelCase_				,     '''r'''   ) as f:
																												self.assertTrue(f.read()				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   )
														self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Union[str, Any]:


														"""simple docstring"""


														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     REFERENCE_CODE + '''\n'''				,     )

														# With no empty line at the end
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     lowerCAmelCase_				,     )

														# Copy consistency with rename
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

														# Copy consistency with a really long name
														_a      	=							'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
														self.check_copy_consistency(
														    F'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'				,     F'{long_class_name}SchedulerOutput'				,     re.sub('''Bert'''				,     lowerCAmelCase_				,     lowerCAmelCase_   )				,     )

														# Copy consistency with overwrite
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     lowerCAmelCase_				,     overwrite_result=re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

