
'''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__      :		Dict       							=  logging.get_logger(__name__)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     None:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				nn.ModuleList([src_layers[i] for i in layers_to_copy]						)
   assert len(SCREAMING_SNAKE_CASE_						) == len(SCREAMING_SNAKE_CASE_						), F"{len(SCREAMING_SNAKE_CASE_						)} != {len(SCREAMING_SNAKE_CASE_						)}"
   dest_layers.load_state_dict(layers_to_copy.state_dict()						)


UpperCamelCase__      :		str       							=  {
    # 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__      :		Union[str, Any]       							=  {
    # 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_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   try:
      _SCREAMING_SNAKE_CASE      =				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(SCREAMING_SNAKE_CASE_						)						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     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(SCREAMING_SNAKE_CASE_						)						)
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = "student"    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_    ,      )     ->     Tuple[PreTrainedModel, List[int], List[int]]:




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

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

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

   try:
      if hasattr(
          SCREAMING_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    ,      SCREAMING_SNAKE_CASE_						)
         copy_layers(teacher.prophetnet.decoder.layers    ,      student.prophetnet.decoder.layers    ,      SCREAMING_SNAKE_CASE_						)
      else:
         copy_layers(teacher.model.encoder.layers    ,      student.model.encoder.layers    ,      SCREAMING_SNAKE_CASE_						)
         copy_layers(teacher.model.decoder.layers    ,      student.model.decoder.layers    ,      SCREAMING_SNAKE_CASE_						)
   except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
      copy_layers(teacher.encoder.block    ,      student.encoder.block    ,      SCREAMING_SNAKE_CASE_						)
      copy_layers(teacher.decoder.block    ,      student.decoder.block    ,      SCREAMING_SNAKE_CASE_						)
   logger.info(
       F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"						)
   _SCREAMING_SNAKE_CASE      =				{
       """teacher_type""": teacher.config.model_type,
       """copied_encoder_layers""": e_layers_to_copy,
       """copied_decoder_layers""": d_layers_to_copy,
   }
   student.save_pretrained(SCREAMING_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
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Tuple:




   """simple docstring"""
   print(F"Vertex\tShortest Distance from vertex {src}"						)
   for i, d in enumerate(SCREAMING_SNAKE_CASE_						):
      print(F"{i}\t\t{d}"						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   for j in range(SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				(graph[j][k] for k in ["""src""", """dst""", """weight"""])
      if distance[u] != float("""inf"""						) and distance[u] + w < distance[v]:
         return True
   return False
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     list[float]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[float("""inf"""						)] * vertex_count
   _SCREAMING_SNAKE_CASE      =				0.0

   for _ in range(vertex_count - 1						):
      for j in range(SCREAMING_SNAKE_CASE_						):
         _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				(graph[j][k] for k in ["""src""", """dst""", """weight"""])

         if distance[u] != float("""inf"""						) and distance[u] + w < distance[v]:
            _SCREAMING_SNAKE_CASE      =				distance[u] + w

   _SCREAMING_SNAKE_CASE      =				check_negative_cycle(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   if negative_cycle_exists:
      raise Exception("""Negative cycle found"""						)

   return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     UpperCamelCase__      :		int       							=  int(input("Enter number of vertices: ").strip())
     UpperCamelCase__      :		int       							=  int(input("Enter number of edges: ").strip())

     UpperCamelCase__      :		list[dict[str, int]]       							=  [{} for _ in range(E)]

     for i in range(E):
          print("Edge ", i + 1)
          UpperCamelCase__							,      UpperCamelCase__							,      UpperCamelCase__      :		Dict       							=  (
              int(x)
              for x in input("Enter source, destination, weight: ").strip().split(" ")
          )
          UpperCamelCase__      :		Optional[Any]       							=  {"src": src, "dst": dest, "weight": weight}

     UpperCamelCase__      :		Optional[Any]       							=  int(input("\nEnter shortest path source:").strip())
     UpperCamelCase__      :		Any       							=  bellman_ford(graph, V, E, source)
     print_distance(shortest_distance, 0)






