





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


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







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a    ) -> Optional[Any]:
					__A      :		Optional[int]         =							nn.ModuleList([src_layers[i] for i in layers_to_copy]    )
					assert len(_UpperCAmelCase    ) == len(_UpperCAmelCase    ), F"""{len(_UpperCAmelCase    )} != {len(_UpperCAmelCase    )}"""
					dest_layers.load_state_dict(layers_to_copy.state_dict()    )


UpperCAmelCase		:  Optional[Any]         =	{
    # 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))},
}
UpperCAmelCase		:  int         =	{
    # 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 					_SCREAMING_SNAKE_CASE							(  a   ,		a    ) -> Any:
					try:
										__A      :		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(_UpperCAmelCase    )    )







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a    ) -> List[str]:
					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(_UpperCAmelCase    )    )
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a = "student"   ,		a = None   ,		a = None   ,		a=False   ,		a=None   ,		a=None   ,		**a   ,		) -> Union[str, Any]:
					__A      :		int         =							'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(_UpperCAmelCase   ,		_UpperCAmelCase    ):
										AutoTokenizer.from_pretrained(_UpperCAmelCase    ).save_pretrained(_UpperCAmelCase    )  # purely for convenience
										__A      :		Dict         =							AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase    ).eval()
					else:
										assert isinstance(_UpperCAmelCase   ,		_UpperCAmelCase    ), F"""teacher must be a model or string got type {type(_UpperCAmelCase    )}"""
					__A      :		List[str]         =							teacher.config.to_diff_dict()

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

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

					try:
										if hasattr(
										    _UpperCAmelCase   ,		'prophetnet'    ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
															copy_layers(teacher.prophetnet.encoder.layers   ,		student.prophetnet.encoder.layers   ,		_UpperCAmelCase    )
															copy_layers(teacher.prophetnet.decoder.layers   ,		student.prophetnet.decoder.layers   ,		_UpperCAmelCase    )
										else:
															copy_layers(teacher.model.encoder.layers   ,		student.model.encoder.layers   ,		_UpperCAmelCase    )
															copy_layers(teacher.model.decoder.layers   ,		student.model.decoder.layers   ,		_UpperCAmelCase    )
					except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
										copy_layers(teacher.encoder.block   ,		student.encoder.block   ,		_UpperCAmelCase    )
										copy_layers(teacher.decoder.block   ,		student.decoder.block   ,		_UpperCAmelCase    )
					logger.info(
					    F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"""    )
					__A      :		Dict         =							{
					    'teacher_type': teacher.config.model_type,
					    'copied_encoder_layers': e_layers_to_copy,
					    'copied_decoder_layers': d_layers_to_copy,
					}
					student.save_pretrained(_UpperCAmelCase    )
					# 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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase     :						Dict						       =  {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						int						       =  ['''LayoutXLMTokenizer''']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						Any						       =  ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       __UpperCamelCase     :						Dict						       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





