





"""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


__UpperCAmelCase        =logging.get_logger(__name__)



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



   '''simple docstring'''



   A__											=					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() )


__UpperCAmelCase        ={
    # 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        ={
    # 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							(			A ,	A )       ->						Tuple:



   '''simple docstring'''



   try:
      A__											=					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        __a							(			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        __a							(			A ,	A = "student" ,	A = None ,	A = None ,	A=False ,	A=None ,	A=None ,	**A ,	)       ->						Tuple[PreTrainedModel, List[int], List[int]]:



   '''simple docstring'''



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

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



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_       :




      """simple docstring"""




      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		Union[str, Any]=6.0  ,   UpperCAmelCase      :		Any=None  ,   UpperCAmelCase      :		Optional[Any]=False  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		Tuple="fp4"  ,   UpperCAmelCase      :		str=False  ,   **UpperCAmelCase      :		Union[str, Any]  ,   )	->	str:


       '''simple docstring'''

       lowercase       : Optional[int]						=load_in_abit
       lowercase       : Union[str, Any]						=load_in_abit
       lowercase       : Tuple						=llm_inta_threshold
       lowercase       : Optional[Any]						=llm_inta_skip_modules
       lowercase       : int						=llm_inta_enable_fpaa_cpu_offload
       lowercase       : Dict						=llm_inta_has_fpaa_weight
       lowercase       : str						=bnb_abit_quant_type
       lowercase       : int						=bnb_abit_use_double_quant

       if bnb_abit_compute_dtype is None:
        lowercase       : str						=torch.floataa
       elif isinstance(UpperCAmelCase  ,   UpperCAmelCase		):
        lowercase       : Tuple						=getattr(UpperCAmelCase  ,   UpperCAmelCase		)
       elif isinstance(UpperCAmelCase  ,   torch.dtype		):
        lowercase       : Optional[int]						=bnb_abit_compute_dtype
       else:
        raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype'''		)

       self.post_init()






      def 					A__   (			self      :		List[Any]		)	->	Union[str, Any]:


       '''simple docstring'''

       if not isinstance(self.llm_inta_threshold  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_threshold must be a float'''		)

       if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_skip_modules must be a list of strings'''		)
       if not isinstance(self.llm_inta_enable_fpaa_cpu_offload  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean'''		)

       if not isinstance(self.llm_inta_has_fpaa_weight  ,   UpperCAmelCase		):
        raise ValueError('''llm_int8_has_fp16_weight must be a boolean'''		)

       if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype  ,   torch.dtype		):
        raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype'''		)

       if not isinstance(self.bnb_abit_quant_type  ,   UpperCAmelCase		):
        raise ValueError('''bnb_4bit_quant_type must be a string'''		)

       if not isinstance(self.bnb_abit_use_double_quant  ,   UpperCAmelCase		):
        raise ValueError('''bnb_4bit_use_double_quant must be a boolean'''		)

       if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes'''		)		) >= version.parse(
           '''0.39.0'''		):
        raise ValueError(
            '''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version'''		)






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

       return self.load_in_abit or self.load_in_abit






      def 					A__   (			self      :		Tuple		)	->	Any:


       '''simple docstring'''

       if self.load_in_abit:
        return "llm_int8"
       elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
        return "fp4"
       elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
        return "nf4"
       else:
        return None






      @classmethod
      def 					A__   (			cls      :		Union[str, Any]  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Any  ,   **UpperCAmelCase      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Dict						=cls(**UpperCAmelCase		)

       lowercase       : Dict						=[]
       for key, value in kwargs.items():
        if hasattr(UpperCAmelCase  ,   UpperCAmelCase		):
         setattr(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
         to_remove.append(UpperCAmelCase		)
       for key in to_remove:
        kwargs.pop(UpperCAmelCase  ,   UpperCAmelCase		)

       if return_unused_kwargs:
        return config, kwargs
       else:
        return config






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Union[str, os.PathLike]		)	->	Dict:


       '''simple docstring'''

       with open(UpperCAmelCase  ,   '''w'''  ,   encoding='''utf-8'''		) as writer:
        lowercase       : Any						=self.to_dict()
        lowercase       : List[Any]						=json.dumps(UpperCAmelCase  ,   indent=2  ,   sort_keys=UpperCAmelCase		) + '''\n'''

        writer.write(UpperCAmelCase		)






      def 					A__   (			self      :		Optional[Any]		)	->	Dict[str, Any]:


       '''simple docstring'''

       lowercase       : Tuple						=copy.deepcopy(self.__dict__		)
       lowercase       : Optional[Any]						=str(output['''bnb_4bit_compute_dtype''']		).split('''.'''		)[1]

       return output






      def __repr__(			self      :		Optional[Any]		)	->	Union[str, Any]:


       '''simple docstring'''

       return f'{self.__class__.__name__} {self.to_json_string()}'






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		bool = True		)	->	str:


       '''simple docstring'''

       if use_diff is True:
        lowercase       : int						=self.to_diff_dict()
       else:
        lowercase       : List[str]						=self.to_dict()
       return json.dumps(UpperCAmelCase  ,   indent=2  ,   sort_keys=UpperCAmelCase		) + "\n"






      def 					A__   (			self      :		Any		)	->	Dict[str, Any]:


       '''simple docstring'''

       lowercase       : Any						=self.to_dict()

       # get the default config dict
       lowercase       : Union[str, Any]						=BitsAndBytesConfig().to_dict()

       lowercase       : int						={}

       # only serialize values that differ from the default config
       for key, value in config_dict.items():
        if value != default_config_dict[key]:
         lowercase       : Dict						=value

       return serializable_config_dict



