
"""simple docstring"""


import warnings
from contextlib import contextmanager

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







class 				lowerCAmelCase_				(      __snake_case    ):



						'''simple docstring'''


						_lowerCamelCase:    Any      =    'Wav2Vec2FeatureExtractor'
						_lowerCamelCase:    List[str]      =    'AutoTokenizer'


						def __init__( self     :      int  ,A_     :      Dict  ,A_     :      str      )     ->   int:
							super().__init__(A_  ,A_      )
							A						   =   self.feature_extractor
							A						   =   False


						@classmethod
						def 						_SCREAMING_SNAKE_CASE      ( cls     :      Optional[int]  ,A_     :      Optional[Any]  ,**A_     :      List[Any]      )     ->   List[str]:




							try:
								return super().from_pretrained(A_  ,**A_      )
							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: '  ,A_  ,)

								A						   =   WavaVecaFeatureExtractor.from_pretrained(A_  ,**A_      )
								A						   =   WavaVecaCTCTokenizer.from_pretrained(A_  ,**A_      )

								return cls(feature_extractor=A_  ,tokenizer=A_      )


						def __call__( self     :      Dict  ,*A_     :      Tuple  ,**A_     :      List[str]      )     ->   List[str]:




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

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

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,*A_     :      Dict  ,**A_     :      Dict      )     ->   List[str]:




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

							A						   =   kwargs.pop('input_features'  ,A_      )
							A						   =   kwargs.pop('labels'  ,A_      )
							if len(A_      ) > 0:
								A						   =   args[0]
								A						   =   args[1:]

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

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,*A_     :      str  ,**A_     :      Optional[int]      )     ->   Optional[Any]:
							return self.tokenizer.batch_decode(*A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,*A_     :      Any  ,**A_     :      List[Any]      )     ->   Any:
							return self.tokenizer.decode(*A_  ,**A_      )




						@contextmanager
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   List[str]:
							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.'      )
							A						   =   True
							A						   =   self.tokenizer
							yield
							A						   =   self.feature_extractor
							A						   =   False

"""simple docstring"""


# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_lowercase							   =				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
_lowercase							   =				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

_lowercase							   =				'''|'''.join(sys.argv[1:])
_lowercase							   =				re.compile(rF"""^({joined_dirs}).*?\.py$""")

_lowercase							   =				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')