






"""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			:    str				 =		logging.get_logger(__name__)





def 						lowercase__(A ,		A ,		A						)    ->None:




						"""simple docstring"""



						lowercase__    :				List[Any]=	nn.ModuleList([src_layers[i] for i in layers_to_copy]						)
						assert len(A						) == len(A						), f'''{len(A						)} != {len(A						)}'''
						dest_layers.load_state_dict(layers_to_copy.state_dict()						)


a			:    Dict				 =		{
    # 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			:    List[Any]				 =		{
    # 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 						lowercase__(A ,		A						)    ->List[str]:




						"""simple docstring"""



						try:
												lowercase__    :				List[str]=	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(A						)						)





def 						lowercase__(A ,		A						)    ->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(A						)						)
						elif n_student == 1:
												return [n_teacher - 1]
						else:
												return LAYERS_TO_SUPERVISE[n_teacher][n_student]





def 						lowercase__(A ,		A = "student" ,		A = None ,		A = None ,		A=False ,		A=None ,		A=None ,		**A ,		)    ->Tuple[PreTrainedModel, List[int], List[int]]:




						"""simple docstring"""



						lowercase__    :				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(A ,		A						):
												AutoTokenizer.from_pretrained(A						).save_pretrained(A						)  # purely for convenience
												lowercase__    :				Union[str, Any]=	AutoModelForSeqaSeqLM.from_pretrained(A						).eval()
						else:
												assert isinstance(A ,		A						), f'''teacher must be a model or string got type {type(A						)}'''
						lowercase__    :				List[Any]=	teacher.config.to_diff_dict()

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

						# Copy weights
						lowercase__    :				Any=	teacher.config_class(**A						)
						lowercase__    :				Union[str, Any]=	AutoModelForSeqaSeqLM.from_config(A						)
						# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
						lowercase__    :				str=	student.load_state_dict(teacher.state_dict() ,		strict=A						)
						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
												lowercase__,     lowercase__    :				Dict=	list(range(A						)						), list(range(A						)						)
												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(A						)
												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:
												lowercase__    :				List[int]=	pick_layers_to_copy(A ,		A						)
						if d_layers_to_copy is None:
												lowercase__    :				List[int]=	pick_layers_to_copy(A ,		A						)

						try:
												if hasattr(
												    A ,		"prophetnet"						):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
																		copy_layers(teacher.prophetnet.encoder.layers ,		student.prophetnet.encoder.layers ,		A						)
																		copy_layers(teacher.prophetnet.decoder.layers ,		student.prophetnet.decoder.layers ,		A						)
												else:
																		copy_layers(teacher.model.encoder.layers ,		student.model.encoder.layers ,		A						)
																		copy_layers(teacher.model.decoder.layers ,		student.model.decoder.layers ,		A						)
						except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
												copy_layers(teacher.encoder.block ,		student.encoder.block ,		A						)
												copy_layers(teacher.decoder.block ,		student.decoder.block ,		A						)
						logger.info(
						    f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''						)
						lowercase__    :				int=	{
						    "teacher_type": teacher.config.model_type,
						    "copied_encoder_layers": e_layers_to_copy,
						    "copied_decoder_layers": d_layers_to_copy,
						}
						student.save_pretrained(A						)
						# 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)














"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    List[str]				 =		{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    List[str]				 =		[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			a			:    List[str]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)






