





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_    =				{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =				['''RobertaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =				[
	    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaForCausalLM''',
	    '''RobertaForMaskedLM''',
	    '''RobertaForMultipleChoice''',
	    '''RobertaForQuestionAnswering''',
	    '''RobertaForSequenceClassification''',
	    '''RobertaForTokenClassification''',
	    '''RobertaModel''',
	    '''RobertaPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =				[
	    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaForCausalLM''',
	    '''TFRobertaForMaskedLM''',
	    '''TFRobertaForMultipleChoice''',
	    '''TFRobertaForQuestionAnswering''',
	    '''TFRobertaForSequenceClassification''',
	    '''TFRobertaForTokenClassification''',
	    '''TFRobertaMainLayer''',
	    '''TFRobertaModel''',
	    '''TFRobertaPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =				[
	    '''FlaxRobertaForCausalLM''',
	    '''FlaxRobertaForMaskedLM''',
	    '''FlaxRobertaForMultipleChoice''',
	    '''FlaxRobertaForQuestionAnswering''',
	    '''FlaxRobertaForSequenceClassification''',
	    '''FlaxRobertaForTokenClassification''',
	    '''FlaxRobertaModel''',
	    '''FlaxRobertaPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
	from .tokenization_roberta import RobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roberta_fast import RobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta import (
		    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaForCausalLM,
		    RobertaForMaskedLM,
		    RobertaForMultipleChoice,
		    RobertaForQuestionAnswering,
		    RobertaForSequenceClassification,
		    RobertaForTokenClassification,
		    RobertaModel,
		    RobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta import (
		    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaForCausalLM,
		    TFRobertaForMaskedLM,
		    TFRobertaForMultipleChoice,
		    TFRobertaForQuestionAnswering,
		    TFRobertaForSequenceClassification,
		    TFRobertaForTokenClassification,
		    TFRobertaMainLayer,
		    TFRobertaModel,
		    TFRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		    FlaxRobertaPreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase_    =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








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      	=  logging.get_logger(__name__)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  None:





						"""simple docstring"""



						_UpperCAmelCase									=			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      	=  {
    # 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      	=  {
    # 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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Dict:





						"""simple docstring"""



						try:
												_UpperCAmelCase									=			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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  List[int]:





						"""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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase = "student" ,		__lowerCAmelCase = None ,		__lowerCAmelCase = None ,		__lowerCAmelCase=False ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None ,		**__lowerCAmelCase ,		)->  Tuple[PreTrainedModel, List[int], List[int]]:





						"""simple docstring"""



						_UpperCAmelCase									=			'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
												_UpperCAmelCase									=			AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase	).eval()
						else:
												assert isinstance(__lowerCAmelCase ,		__lowerCAmelCase	), F"""teacher must be a model or string got type {type(__lowerCAmelCase	)}"""
						_UpperCAmelCase									=			teacher.config.to_diff_dict()

						try:
												_UpperCAmelCase					, _UpperCAmelCase									=			teacher.config.encoder_layers, teacher.config.decoder_layers
												if e is None:
																		_UpperCAmelCase									=			teacher_e
												if d is None:
																		_UpperCAmelCase									=			teacher_d
												init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}	)
						except AttributeError:  # T5
												if hasattr(teacher.config ,		'num_encoder_layers'	):
																		_UpperCAmelCase					, _UpperCAmelCase									=			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
												else:
																		_UpperCAmelCase					, _UpperCAmelCase									=			teacher.config.num_layers, teacher.config.num_decoder_layers
												if e is None:
																		_UpperCAmelCase									=			teacher_e
												if d is None:
																		_UpperCAmelCase									=			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
						_UpperCAmelCase									=			teacher.config_class(**__lowerCAmelCase	)
						_UpperCAmelCase									=			AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase	)
						# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
						_UpperCAmelCase									=			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
												_UpperCAmelCase					, _UpperCAmelCase									=			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:
												_UpperCAmelCase									=			pick_layers_to_copy(__lowerCAmelCase ,		__lowerCAmelCase	)
						if d_layers_to_copy is None:
												_UpperCAmelCase									=			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}"""	)
						_UpperCAmelCase									=			{
						    '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)
