






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


SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		logging.get_logger(__name__)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   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()			)


SCREAMING_SNAKE_CASE__   :      List[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))},
}
SCREAMING_SNAKE_CASE__   :      List[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   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				try:
								__magic_name__		:List[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(snake_case			)			)

def   __lowercase       (			snake_case,	snake_case			):







				"""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   __lowercase       (			snake_case,	snake_case = "student",	snake_case = None,	snake_case = None,	snake_case=False,	snake_case=None,	snake_case=None,	**snake_case,	):







				"""simple docstring"""






				__magic_name__		:Union[str, 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(snake_case,	snake_case			):
								AutoTokenizer.from_pretrained(snake_case			).save_pretrained(snake_case			)  # purely for convenience
								__magic_name__		:str   =   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			)}'''
				__magic_name__		:List[str]   =   teacher.config.to_diff_dict()

				try:
								__magic_name__  ,					__magic_name__		:List[str]   =   teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												__magic_name__		:Union[str, Any]   =   teacher_e
								if d is None:
												__magic_name__		:int   =   teacher_d
								init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}			)
				except AttributeError:  # T5
								if hasattr(teacher.config,	'''num_encoder_layers'''			):
												__magic_name__  ,					__magic_name__		:List[Any]   =   teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												__magic_name__  ,					__magic_name__		:List[Any]   =   teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												__magic_name__		:Optional[Any]   =   teacher_e
								if d is None:
												__magic_name__		:Dict   =   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
				__magic_name__		:Dict   =   teacher.config_class(**snake_case			)
				__magic_name__		:Optional[Any]   =   AutoModelForSeqaSeqLM.from_config(snake_case			)
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				__magic_name__		:List[str]   =   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
								__magic_name__  ,					__magic_name__		:int   =   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:
								__magic_name__		:List[int]   =   pick_layers_to_copy(snake_case,	snake_case			)
				if d_layers_to_copy is None:
								__magic_name__		:List[int]   =   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}'''			)
				__magic_name__		: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(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)














from __future__ import annotations

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				print(f'''Vertex\tShortest Distance from vertex {src}'''			)
				for i, d in enumerate(snake_case			):
								print(f'''{i}\t\t{d}'''			)

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				for j in range(snake_case			):
								__magic_name__  ,					__magic_name__  ,					__magic_name__		:Tuple   =   (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   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[Any]   =   [float('''inf'''			)] * vertex_count
				__magic_name__		:Tuple   =   0.0

				for _ in range(vertex_count - 1			):
								for j in range(snake_case			):
												__magic_name__  ,					__magic_name__  ,					__magic_name__		:Dict   =   (graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

												if distance[u] != float('''inf'''			) and distance[u] + w < distance[v]:
																__magic_name__		:Tuple   =   distance[u] + w

				__magic_name__		:Tuple   =   check_negative_cycle(snake_case,	snake_case,	snake_case			)
				if negative_cycle_exists:
								raise Exception('''Negative cycle found'''			)

				return distance


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE__   :      Tuple				=		int(input("""Enter number of vertices: """).strip())
					SCREAMING_SNAKE_CASE__   :      Any				=		int(input("""Enter number of edges: """).strip())

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

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

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






