





"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   A      (_UpperCAmelCase   ):
			'''simple docstring'''




			__lowerCamelCase     :	List[Any]          =   "Speech2TextFeatureExtractor"
			__lowerCamelCase     :	Dict          =   "Speech2TextTokenizer"






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







					"""simple docstring"""






					super().__init__(_UpperCAmelCase							,     _UpperCAmelCase       )
					A__  				=       self.feature_extractor
					A__  				=       False






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







					"""simple docstring"""






					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."""       )
							A__  				=       kwargs.pop("""raw_speech"""       )
					else:
							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 audio is not None:
							A__  				=       self.feature_extractor(_UpperCAmelCase							,     *_UpperCAmelCase							,     sampling_rate=_UpperCAmelCase							,     **_UpperCAmelCase       )
					if text is not None:
							A__  				=       self.tokenizer(_UpperCAmelCase							,     **_UpperCAmelCase       )

					if text is None:
							return inputs
					elif audio is None:
							return encodings
					else:
							A__  				=       encodings['''input_ids''']
							return inputs






			def        a_	(      self       :   Optional[int]							,     *__lowerCAmelCase       :   str							,     **__lowerCAmelCase       :   Optional[Any]       )     ->     int:







					"""simple docstring"""






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






			def        a_	(      self       :   Union[str, Any]							,     *__lowerCAmelCase       :   int							,     **__lowerCAmelCase       :   Optional[int]       )     ->     Tuple:







					"""simple docstring"""






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







			@contextmanager
			def        a_	(      self       :   Tuple       )     ->     Any:







					"""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










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser(
					    description=(
					        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
					        ''' Distillation'''
					    )
					)
					parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
					parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
					parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
					parser.add_argument('''--vocab_transform''', action='''store_true''')
					A   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

					for w in ["word_embeddings", "position_embeddings"]:
										A   : str			=				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										A   : Any			=				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)



