'''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			:      int					      =				logging.get_logger(__name__)







def      UpperCamelCase_						(					snake_case_       :       nn.ModuleList						,		snake_case_       :       nn.ModuleList						,		snake_case_       :       List[int]      )					->     None:






 '''simple docstring'''

 __lowerCAmelCase											=						nn.ModuleList([src_layers[i] for i in layers_to_copy]      )
 assert len(snake_case_      ) == len(snake_case_      ), f"""{len(snake_case_      )} != {len(snake_case_      )}"""
 dest_layers.load_state_dict(layers_to_copy.state_dict()      )


_A			:      Tuple					      =				{
    # 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[str]					      =				{
    # 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      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       str      )					->     Optional[Any]:






 '''simple docstring'''

 try:
  __lowerCAmelCase											=						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(snake_case_      )      )







def      UpperCamelCase_						(					snake_case_       :       Dict						,		snake_case_       :       str      )					->     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(snake_case_      )      )
 elif n_student == 1:
  return [n_teacher - 1]
 else:
  return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def      UpperCamelCase_						(					snake_case_       :       Union[str, PreTrainedModel]						,		snake_case_       :       Union[str, Path] = "student"						,		snake_case_       :       Union[int, None] = None						,		snake_case_       :       Union[int, None] = None						,		snake_case_       :       int=False						,		snake_case_       :       Union[str, Any]=None						,		snake_case_       :       Dict=None						,		**snake_case_       :       Dict						,		)					->     Tuple[PreTrainedModel, List[int], List[int]]:






 '''simple docstring'''

 __lowerCAmelCase											=						"""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(snake_case_						,		snake_case_      ):
  AutoTokenizer.from_pretrained(snake_case_      ).save_pretrained(snake_case_      )  # purely for convenience
  __lowerCAmelCase											=						AutoModelForSeqaSeqLM.from_pretrained(snake_case_      ).eval()
 else:
  assert isinstance(snake_case_						,		snake_case_      ), f"""teacher must be a model or string got type {type(snake_case_      )}"""
 __lowerCAmelCase											=						teacher.config.to_diff_dict()

 try:
  __lowerCAmelCase							,       __lowerCAmelCase											=						teacher.config.encoder_layers, teacher.config.decoder_layers
  if e is None:
   __lowerCAmelCase											=						teacher_e
  if d is None:
   __lowerCAmelCase											=						teacher_d
  init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d}      )
 except AttributeError:  # T5
  if hasattr(teacher.config						,		"""num_encoder_layers"""      ):
   __lowerCAmelCase							,       __lowerCAmelCase											=						teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
  else:
   __lowerCAmelCase							,       __lowerCAmelCase											=						teacher.config.num_layers, teacher.config.num_decoder_layers
  if e is None:
   __lowerCAmelCase											=						teacher_e
  if d is None:
   __lowerCAmelCase											=						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(snake_case_      )

 # Copy weights
 __lowerCAmelCase											=						teacher.config_class(**snake_case_      )
 __lowerCAmelCase											=						AutoModelForSeqaSeqLM.from_config(snake_case_      )
 # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
 __lowerCAmelCase											=						student.load_state_dict(teacher.state_dict()						,		strict=snake_case_      )
 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
  __lowerCAmelCase							,       __lowerCAmelCase											=						list(range(snake_case_      )      ), list(range(snake_case_      )      )
  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(snake_case_      )
  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:
  __lowerCAmelCase											=						pick_layers_to_copy(snake_case_						,		snake_case_      )
 if d_layers_to_copy is None:
  __lowerCAmelCase											=						pick_layers_to_copy(snake_case_						,		snake_case_      )

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

from dataclasses import dataclass







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       float
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None







def      UpperCamelCase_						(					snake_case_       :       TreeNode | None      )					->     bool:






 '''simple docstring'''

 def is_valid_tree(snake_case_       :       TreeNode | None      ) -> bool:
  if node is None:
   return True

  if not isinstance(snake_case_						,		snake_case_      ):
   return False

  try:
   float(node.data      )
  except (TypeError, ValueError):
   return False

  return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

 if not is_valid_tree(snake_case_      ):
  raise ValueError(
      """Each node should be type of TreeNode and data should be float."""      )

 def is_binary_search_tree_recursive_check(
     snake_case_       :       TreeNode | None						,		snake_case_       :       float						,		snake_case_       :       float      ) -> bool:

  if node is None:
   return True

  return (
      left_bound < node.data < right_bound
      and is_binary_search_tree_recursive_check(node.left						,		snake_case_						,		node.data      )
      and is_binary_search_tree_recursive_check(
          node.right						,		node.data						,		snake_case_      )
  )

 return is_binary_search_tree_recursive_check(snake_case_						,		-float("""inf"""      )						,		float("""inf"""      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






