




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


_snake_case										=       logging.get_logger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     str			=						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()     )


_snake_case										=       {
    # 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))},
}
_snake_case										=       {
    # 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 				lowerCAmelCase_	(     snake_case_,snake_case_     ):

      try:
            _A							:     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(__UpperCAmelCase     )     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):

      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 				lowerCAmelCase_	(     snake_case_,snake_case_ = "student",snake_case_ = None,snake_case_ = None,snake_case_=False,snake_case_=None,snake_case_=None,**snake_case_,):
      _A							:     Dict			=						"""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							:     Any			=						AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase     ).eval()
      else:
            assert isinstance(__UpperCAmelCase,__UpperCAmelCase     ), f'''teacher must be a model or string got type {type(__UpperCAmelCase     )}'''
      _A							:     Dict			=						teacher.config.to_diff_dict()

      try:
            _A			,   _A							:     List[Any]			=						teacher.config.encoder_layers, teacher.config.decoder_layers
            if e is None:
                  _A							:     Optional[int]			=						teacher_e
            if d is None:
                  _A							:     Tuple			=						teacher_d
            init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d}     )
      except AttributeError:  # T5
            if hasattr(teacher.config,"""num_encoder_layers"""     ):
                  _A			,   _A							:     Optional[Any]			=						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							:     Dict			=						teacher_e
            if d is None:
                  _A							:     List[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(__UpperCAmelCase     )

      # Copy weights
      _A							:     List[Any]			=						teacher.config_class(**__UpperCAmelCase     )
      _A							:     Tuple			=						AutoModelForSeqaSeqLM.from_config(__UpperCAmelCase     )
      # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
      _A							:     str			=						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							:     int			=						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							:     Dict			=						pick_layers_to_copy(__UpperCAmelCase,__UpperCAmelCase     )
      if d_layers_to_copy is None:
            _A							:     Optional[Any]			=						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							:     Optional[int]			=						{
          """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 collections import defaultdict


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				1
					snake_case_									=				True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(__UpperCAmelCase	)
					if ret % 2 == 0:
										cuts.append(__UpperCAmelCase	)
					return ret


def 		__magic_name__    (	)		->					Union[str, Any]:

					'''simple docstring'''

					dfs(1	)


if __name__ == "__main__":
							a		,a  :				Dict									=  10, 9
							a  :				Dict									=  defaultdict(list)
							a  :				dict[int, bool]									=  {}
							a  :				list[int]									=  []
							a  :				Tuple									=  0
							a  :				str									=  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)

