


'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__A	:	Dict     =     logging.get_logger(__name__)
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Optional[Any]   		=  ['input_features', 'attention_mask']





							def __init__(  self		:str  ,_UpperCamelCase		:int=8_0  ,_UpperCamelCase		:int=1_6_0_0_0  ,_UpperCamelCase		:Tuple=8_0  ,_UpperCamelCase		:Dict=0.0  ,_UpperCamelCase		:Optional[int]=True  ,_UpperCamelCase		:Dict=True  ,_UpperCamelCase		:List[Any]=True  ,**_UpperCamelCase		:str  ,):
									super().__init__(feature_size=_UpperCamelCase  ,sampling_rate=_UpperCamelCase  ,padding_value=_UpperCamelCase  ,**_UpperCamelCase			)
									snake_case_		:							List[Any]			       =						num_mel_bins
									snake_case_		:							Any			       =						do_ceptral_normalize
									snake_case_		:							int			       =						normalize_means
									snake_case_		:							int			       =						normalize_vars
									snake_case_		:							Tuple			       =						True





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:np.ndarray  ,):
									snake_case_		:							str			       =						waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
									snake_case_		:							List[Any]			       =						torch.from_numpy(_UpperCamelCase			).unsqueeze(0			)
									snake_case_		:							Optional[int]			       =						ta_kaldi.fbank(_UpperCamelCase  ,num_mel_bins=self.num_mel_bins  ,sample_frequency=self.sampling_rate			)
									return features.numpy()





							@staticmethod
							def 		a__   (  _UpperCamelCase		:np.ndarray  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Optional[bool] = True  ,_UpperCamelCase		:Optional[bool] = True  ,_UpperCamelCase		:float = 0.0  ,):
									# make sure we normalize float32 arrays
									if normalize_means:
											snake_case_		:							Dict			       =						x[:input_length].mean(axis=0			)
											snake_case_		:							Union[str, Any]			       =						np.subtract(_UpperCamelCase  ,_UpperCamelCase			)
									if normalize_vars:
											snake_case_		:							Optional[int]			       =						x[:input_length].std(axis=0			)
											snake_case_		:							Optional[Any]			       =						np.divide(_UpperCamelCase  ,_UpperCamelCase			)

									if input_length < x.shape[0]:
											snake_case_		:							int			       =						padding_value

									# make sure array is in float32
									snake_case_		:							int			       =						x.astype(np.floataa			)

									return x





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[np.ndarray]  ,_UpperCamelCase		:Optional[np.ndarray] = None			):
									snake_case_		:							Any			       =						attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
									return [
									    self.utterance_cmvn(_UpperCamelCase  ,_UpperCamelCase  ,self.normalize_means  ,self.normalize_vars  ,self.padding_value			)
									    for x, n in zip(_UpperCamelCase  ,_UpperCamelCase			)
									]





							def __call__(  self		:Optional[Any]  ,_UpperCamelCase		:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,_UpperCamelCase		:Union[bool, str, PaddingStrategy] = False  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:Optional[Union[str, TensorType]] = None  ,_UpperCamelCase		:Optional[int] = None  ,_UpperCamelCase		:Optional[bool] = None  ,**_UpperCamelCase		:Union[str, Any]  ,):

									if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
													raise ValueError(
													    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
													    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
													    F''' {self.sampling_rate} and not {sampling_rate}.'''			)
									else:
											logger.warning(
											    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
											    """Failing to do so can result in silent errors that might be hard to debug."""			)

									snake_case_		:							List[str]			       =						isinstance(_UpperCamelCase  ,np.ndarray			) and len(raw_speech.shape			) > 1
									if is_batched_numpy and len(raw_speech.shape			) > 2:
											raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''			)
									snake_case_		:							Dict			       =						is_batched_numpy or (
									    isinstance(_UpperCamelCase  ,(list, tuple)			) and (isinstance(raw_speech[0]  ,(np.ndarray, tuple, list)			))
									)

									if is_batched:
											snake_case_		:							Union[str, Any]			       =						[np.asarray(_UpperCamelCase  ,dtype=np.floataa			) for speech in raw_speech]
									elif not is_batched and not isinstance(_UpperCamelCase  ,np.ndarray			):
											snake_case_		:							List[str]			       =						np.asarray(_UpperCamelCase  ,dtype=np.floataa			)
									elif isinstance(_UpperCamelCase  ,np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
											snake_case_		:							str			       =						raw_speech.astype(np.floataa			)

									# always return batch
									if not is_batched:
											snake_case_		:							List[Any]			       =						[raw_speech]

									# extract fbank features
									snake_case_		:							Union[str, Any]			       =						[self._extract_fbank_features(_UpperCamelCase			) for waveform in raw_speech]

									# convert into correct format for padding
									snake_case_		:							Any			       =						BatchFeature({"""input_features""": features}			)

									snake_case_		:							str			       =						self.pad(
									    _UpperCamelCase  ,padding=_UpperCamelCase  ,max_length=_UpperCamelCase  ,truncation=_UpperCamelCase  ,pad_to_multiple_of=_UpperCamelCase  ,return_attention_mask=_UpperCamelCase  ,**_UpperCamelCase  ,)

									# make sure list is in array format
									snake_case_		:							str			       =						padded_inputs.get("""input_features"""			)
									if isinstance(input_features[0]  ,_UpperCamelCase			):
											snake_case_		:							Any			       =						[np.asarray(_UpperCamelCase  ,dtype=np.floataa			) for feature in input_features]

									snake_case_		:							List[Any]			       =						padded_inputs.get("""attention_mask"""			)
									if attention_mask is not None:
											snake_case_		:							List[str]			       =						[np.asarray(_UpperCamelCase  ,dtype=np.intaa			) for array in attention_mask]

									# Utterance-level cepstral mean and variance normalization
									if self.do_ceptral_normalize:
											snake_case_		:							str			       =						(
											    np.array(_UpperCamelCase  ,dtype=np.intaa			)
											    if self._get_padding_strategies(_UpperCamelCase  ,max_length=_UpperCamelCase			) is not PaddingStrategy.DO_NOT_PAD
											    else None
											)
											snake_case_		:							Union[str, Any]			       =						self.normalize(
											    padded_inputs["""input_features"""]  ,attention_mask=_UpperCamelCase			)

									if return_tensors is not None:
											snake_case_		:							Optional[Any]			       =						padded_inputs.convert_to_tensors(_UpperCamelCase			)

									return padded_inputs



'''simple docstring'''






import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


__A	:	List[Any]     =     logging.get_logger(__name__)





def      UpperCAmelCase      (		lowerCamelCase_  :nn.ModuleList				,      lowerCamelCase_  :nn.ModuleList				,      lowerCamelCase_  :List[int] ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						nn.ModuleList([src_layers[i] for i in layers_to_copy] )
		assert len(lowerCamelCase_ ) == len(lowerCamelCase_ ), F'''{len(lowerCamelCase_ )} != {len(lowerCamelCase_ )}'''
		dest_layers.load_state_dict(layers_to_copy.state_dict() )


__A	:	Optional[int]     =     {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__A	:	Dict     =     {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		try:
				snake_case_		:							int			       =						LAYERS_TO_COPY[n_teacher][n_student]
				return val
		except KeyError:
				if n_student != n_teacher:
						warnings.warn(
						    F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
						    F''' {n_student}''' )
				return list(range(lowerCamelCase_ ) )





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		if n_student > n_teacher:
				raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
		elif n_teacher == n_student:
				return list(range(lowerCamelCase_ ) )
		elif n_student == 1:
				return [n_teacher - 1]
		else:
				return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, PreTrainedModel]				,      lowerCamelCase_  :Union[str, Path] = "student"				,      lowerCamelCase_  :Union[int, None] = None				,      lowerCamelCase_  :Union[int, None] = None				,      lowerCamelCase_  :List[str]=False				,      lowerCamelCase_  :Optional[int]=None				,      lowerCamelCase_  :Optional[Any]=None				,      **lowerCamelCase_  :Dict				,      ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						"""encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
		assert (e is not None) or (d is not None), _msg
		if isinstance(lowerCamelCase_				,      lowerCamelCase_ ):
				AutoTokenizer.from_pretrained(lowerCamelCase_ ).save_pretrained(lowerCamelCase_ )  # purely for convenience
				snake_case_		:							List[Any]			       =						AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_ ).eval()
		else:
				assert isinstance(lowerCamelCase_				,      lowerCamelCase_ ), F'''teacher must be a model or string got type {type(lowerCamelCase_ )}'''
		snake_case_		:							Any			       =						teacher.config.to_diff_dict()

		try:
				snake_case_       ,      snake_case_		:							List[str]			       =						teacher.config.encoder_layers, teacher.config.decoder_layers
				if e is None:
						snake_case_		:							Dict			       =						teacher_e
				if d is None:
						snake_case_		:							List[Any]			       =						teacher_d
				init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
		except AttributeError:  # T5
				if hasattr(teacher.config				,      """num_encoder_layers""" ):
						snake_case_       ,      snake_case_		:							Tuple			       =						teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
				else:
						snake_case_       ,      snake_case_		:							Union[str, Any]			       =						teacher.config.num_layers, teacher.config.num_decoder_layers
				if e is None:
						snake_case_		:							Optional[int]			       =						teacher_e
				if d is None:
						snake_case_		:							List[str]			       =						teacher_d
				if hasattr(teacher.config				,      """num_encoder_layers""" ):
						init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
				else:
						init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
		init_kwargs.update(lowerCamelCase_ )

		# Copy weights
		snake_case_		:							List[Any]			       =						teacher.config_class(**lowerCamelCase_ )
		snake_case_		:							Tuple			       =						AutoModelForSeqaSeqLM.from_config(lowerCamelCase_ )
		# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
		snake_case_		:							Tuple			       =						student.load_state_dict(teacher.state_dict()				,      strict=lowerCamelCase_ )
		assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

		if copy_first_teacher_layers:  # Our copying is done. We just log and save
				snake_case_       ,      snake_case_		:							List[str]			       =						list(range(lowerCamelCase_ ) ), list(range(lowerCamelCase_ ) )
				logger.info(
				    F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
				    F''' {save_path}''' )
				student.save_pretrained(lowerCamelCase_ )
				return student, e_layers_to_copy, d_layers_to_copy

		# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
		if e_layers_to_copy is None:
				snake_case_		:							List[int]			       =						pick_layers_to_copy(lowerCamelCase_				,      lowerCamelCase_ )
		if d_layers_to_copy is None:
				snake_case_		:							List[int]			       =						pick_layers_to_copy(lowerCamelCase_				,      lowerCamelCase_ )

		try:
				if hasattr(
				    lowerCamelCase_				,      """prophetnet""" ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
						copy_layers(teacher.prophetnet.encoder.layers				,      student.prophetnet.encoder.layers				,      lowerCamelCase_ )
						copy_layers(teacher.prophetnet.decoder.layers				,      student.prophetnet.decoder.layers				,      lowerCamelCase_ )
				else:
						copy_layers(teacher.model.encoder.layers				,      student.model.encoder.layers				,      lowerCamelCase_ )
						copy_layers(teacher.model.decoder.layers				,      student.model.decoder.layers				,      lowerCamelCase_ )
		except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
				copy_layers(teacher.encoder.block				,      student.encoder.block				,      lowerCamelCase_ )
				copy_layers(teacher.decoder.block				,      student.decoder.block				,      lowerCamelCase_ )
		logger.info(
		    F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
		snake_case_		:							Any			       =						{
		    """teacher_type""": teacher.config.model_type,
		    """copied_encoder_layers""": e_layers_to_copy,
		    """copied_decoder_layers""": d_layers_to_copy,
		}
		student.save_pretrained(lowerCamelCase_ )
		# Save information about copying for easier reproducibility

		return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
							fire.Fire(create_student_by_copying_alternating_layers)