





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin

class       __A     (						a    ):
				__A      =				"""MCTCTFeatureExtractor"""
				__A      =				"""AutoTokenizer"""


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								super().__init__(UpperCAmelCase_   , UpperCAmelCase_							)
								lowerCamelCase	 =self.feature_extractor
								lowerCamelCase	 =False


				def __call__(		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								# For backward compatibility
								if self._in_target_context_manager:
												return self.current_processor(*UpperCAmelCase_   , **UpperCAmelCase_							)

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

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


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								return self.tokenizer.batch_decode(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								# For backward compatibility
								if self._in_target_context_manager:
												return self.current_processor.pad(*UpperCAmelCase_   , **UpperCAmelCase_							)

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

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

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


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								return self.tokenizer.decode(*UpperCAmelCase_   , **UpperCAmelCase_							)


				@contextmanager
				def _snake_case       (		self							):
								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."""							)
								lowerCamelCase	 =True
								lowerCamelCase	 =self.tokenizer
								yield
								lowerCamelCase	 =self.feature_extractor
								lowerCamelCase	 =False












import os

try:
							from .build_directory_md import good_file_paths
except ImportError:
							from build_directory_md import good_file_paths  # type: ignore

UpperCAmelCase__						:			List[Any]									=list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

UpperCAmelCase__						:			Dict									=[file for file in filepaths if file != file.lower()]
if upper_files:
							print(F"{len(upper_files)} files contain uppercase characters:")
							print('''\n'''.join(upper_files) + '''\n''')

UpperCAmelCase__						:			Dict									=[file for file in filepaths if ''' ''' in file]
if space_files:
							print(F"{len(space_files)} files contain space characters:")
							print('''\n'''.join(space_files) + '''\n''')

UpperCAmelCase__						:			Dict									=[file for file in filepaths if '''-''' in file]
if hyphen_files:
							print(F"{len(hyphen_files)} files contain hyphen characters:")
							print('''\n'''.join(hyphen_files) + '''\n''')

UpperCAmelCase__						:			Optional[int]									=[file for file in filepaths if os.sep not in file]
if nodir_files:
							print(F"{len(nodir_files)} files are not in a directory:")
							print('''\n'''.join(nodir_files) + '''\n''')

UpperCAmelCase__						:			str									=len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
							import sys

							sys.exit(bad_files)





