


"""simple docstring"""


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
		a_											=			[]

		a_											=			os.path.join("tests", "models", "**", "test_modeling_*.py")
		a_											=			glob.glob(pattern)
		# TODO: deal with TF/Flax too
		a_											=			[
		    x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
		]

		for test_file in test_files:
				a_											=			get_tester_classes(test_file)
				for tester_class in tester_classes:
						# A few tester classes don't have `parent` parameter in `__init__`.
						# TODO: deal this better
						try:
								a_											=			tester_class(parent=None)
						except Exception:
								continue
						if hasattr(tester, "get_config"):
								a_											=			tester.get_config()
								for k, v in config.to_dict().items():
										if isinstance(v, int):
												a_											=			None
												if k in ["vocab_size"]:
														a_											=			1_00
												elif k in ["max_position_embeddings"]:
														a_											=			1_28
												elif k in ["hidden_size", "d_model"]:
														a_											=			40
												elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
														a_											=			5
												if target is not None and v > target:
														failures.append(
														    f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
														    f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
														    f''' than {target}.'''
														)

		if len(failures) > 0:
				raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))



"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	'AutoFeatureExtractor'
		__UpperCamelCase							=	'Speech2Text2Tokenizer'







		def __init__(    self					:				int			,      a__					:				Tuple			,      a__					:				Optional[Any]						)				->  str:





						'''simple docstring'''







						super().__init__(a__			,      a__						)
						_A         =	self.feature_extractor
						_A         =	False







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





						'''simple docstring'''







						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        a_       (    self					:				List[Any]			,      *a__					:				Tuple			,      **a__					:				List[Any]						)				->  List[Any]:





						'''simple docstring'''







						return self.tokenizer.batch_decode(*a__			,      **a__						)







		def        a_       (    self					:				Any			,      *a__					:				Dict			,      **a__					:				Tuple						)				->  Union[str, Any]:





						'''simple docstring'''







						return self.tokenizer.decode(*a__			,      **a__						)







		@contextmanager
		def        a_       (    self					:				Optional[int]						)				->  Optional[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."						)
						_A         =	True
						_A         =	self.tokenizer
						yield
						_A         =	self.feature_extractor
						_A         =	False