


'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__A	:	Tuple     =     {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A	:	Tuple     =     [
							    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ASTForAudioClassification',
							    'ASTModel',
							    'ASTPreTrainedModel',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A	:	int     =     ['ASTFeatureExtractor']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

							__A	:	Union[str, Any]     =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



'''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)