"""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(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			nn.ModuleList([src_layers[i] for i in layers_to_copy]							)
							assert len(lowerCAmelCase							) == len(lowerCAmelCase							), F'''{len(lowerCAmelCase							)} != {len(lowerCAmelCase							)}'''
							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(		lowerCAmelCase			,  lowerCAmelCase							):
							try:
														A__       :     Any								=			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(lowerCAmelCase							)							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							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(lowerCAmelCase							)							)
							elif n_student == 1:
														return [n_teacher - 1]
							else:
														return LAYERS_TO_SUPERVISE[n_teacher][n_student]

def  _A(		lowerCAmelCase			,  lowerCAmelCase = "student"			,  lowerCAmelCase = None			,  lowerCAmelCase = None			,  lowerCAmelCase=False			,  lowerCAmelCase=None			,  lowerCAmelCase=None			,  **lowerCAmelCase			,  ):
							A__       :     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(lowerCAmelCase			,  lowerCAmelCase							):
														AutoTokenizer.from_pretrained(lowerCAmelCase							).save_pretrained(lowerCAmelCase							)  # purely for convenience
														A__       :     int								=			AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase							).eval()
							else:
														assert isinstance(lowerCAmelCase			,  lowerCAmelCase							), F'''teacher must be a model or string got type {type(lowerCAmelCase							)}'''
							A__       :     str								=			teacher.config.to_diff_dict()

							try:
														A__				,							A__       :     List[str]								=			teacher.config.encoder_layers, teacher.config.decoder_layers
														if e is None:
																					A__       :     Optional[int]								=			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__       :     Union[str, Any]								=			teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
														else:
																					A__				,							A__       :     Optional[Any]								=			teacher.config.num_layers, teacher.config.num_decoder_layers
														if e is None:
																					A__       :     Any								=			teacher_e
														if d is None:
																					A__       :     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(lowerCAmelCase							)

							# Copy weights
							A__       :     Optional[int]								=			teacher.config_class(**lowerCAmelCase							)
							A__       :     Optional[Any]								=			AutoModelForSeqaSeqLM.from_config(lowerCAmelCase							)
							# 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=lowerCAmelCase							)
							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__       :     Optional[Any]								=			list(range(lowerCAmelCase							)							), list(range(lowerCAmelCase							)							)
														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(lowerCAmelCase							)
														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__       :     int								=			pick_layers_to_copy(lowerCAmelCase			,  lowerCAmelCase							)
							if d_layers_to_copy is None:
														A__       :     Tuple								=			pick_layers_to_copy(lowerCAmelCase			,  lowerCAmelCase							)

							try:
														if hasattr(
														    lowerCAmelCase			,  """prophetnet"""							):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
																					copy_layers(teacher.prophetnet.encoder.layers			,  student.prophetnet.encoder.layers			,  lowerCAmelCase							)
																					copy_layers(teacher.prophetnet.decoder.layers			,  student.prophetnet.decoder.layers			,  lowerCAmelCase							)
														else:
																					copy_layers(teacher.model.encoder.layers			,  student.model.encoder.layers			,  lowerCAmelCase							)
																					copy_layers(teacher.model.decoder.layers			,  student.model.decoder.layers			,  lowerCAmelCase							)
							except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
														copy_layers(teacher.encoder.block			,  student.encoder.block			,  lowerCAmelCase							)
														copy_layers(teacher.decoder.block			,  student.decoder.block			,  lowerCAmelCase							)
							logger.info(
							    F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''							)
							A__       :     Union[str, Any]								=			{
							    """teacher_type""": teacher.config.model_type,
							    """copied_encoder_layers""": e_layers_to_copy,
							    """copied_decoder_layers""": d_layers_to_copy,
							}
							student.save_pretrained(lowerCAmelCase							)
							# 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"""


import heapq






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   []

  # for each node and his adjacency list add them and the rank of the node to queue
  # using heapq module the queue will be filled like a Priority Queue
  # heapq works with a min priority queue, so I used -1*len(v) to build it
  for key, value in graph.items():
    # O(log(n))
    heapq.heappush(lowercase ,[-1 * len(lowercase		), (key, value)]		)

  # chosen_vertices = set of chosen vertices
  _UpperCAmelCase    			=   set()

  # while queue isn't empty and there are still edges
  #   (queue[0][0] is the rank of the node with max rank)
  while queue and queue[0][0] != 0:
    # extract vertex with max rank from queue and add it to chosen_vertices
    _UpperCAmelCase    			=   heapq.heappop(lowercase		)[1][0]
    chosen_vertices.add(lowercase		)

    # Remove all arcs adjacent to argmax
    for elem in queue:
      # if v haven't adjacent node, skip
      if elem[0] == 0:
        continue
      # if argmax is reachable from elem
      # remove argmax from elem's adjacent list and update his rank
      if argmax in elem[1][1]:
        _UpperCAmelCase    			=   elem[1][1].index(lowercase		)
        del elem[1][1][index]
        elem[0] += 1
        # re-order the queue
    heapq.heapify(lowercase		)
  return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCAmelCase__			=    {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(F'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')
