


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






def 							__lowercase     (				__lowerCAmelCase							:       nn.ModuleList      ,					__lowerCAmelCase							:       nn.ModuleList      ,					__lowerCAmelCase							:       List[int]     ):
							a__    =  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()     )


snake_case							: Any      =   {
    # 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							: 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     (				__lowerCAmelCase							:       Dict      ,					__lowerCAmelCase							:       Dict     ):
							try:
														a__    =  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 							__lowercase     (				__lowerCAmelCase							:       Dict      ,					__lowerCAmelCase							:       Dict     ):
							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 							__lowercase     (				__lowerCAmelCase							:       Union[str, PreTrainedModel]      ,					__lowerCAmelCase							:       Union[str, Path] = "student"      ,					__lowerCAmelCase							:       Union[int, None] = None      ,					__lowerCAmelCase							:       Union[int, None] = None      ,					__lowerCAmelCase							:       List[str]=False      ,					__lowerCAmelCase							:       List[str]=None      ,					__lowerCAmelCase							:       Optional[int]=None      ,					**__lowerCAmelCase							:       Any      ,					):
							a__    =  '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__    =  AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase     ).eval()
							else:
														assert isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ), F'teacher must be a model or string got type {type(__lowerCAmelCase     )}'
							a__    =  teacher.config.to_diff_dict()

							try:
														a__   ,      a__    =  teacher.config.encoder_layers, teacher.config.decoder_layers
														if e is None:
																					a__    =  teacher_e
														if d is None:
																					a__    =  teacher_d
														init_kwargs.update({'encoder_layers': e, 'decoder_layers': d}     )
							except AttributeError:  # T5
														if hasattr(teacher.config      ,					'num_encoder_layers'     ):
																					a__   ,      a__    =  teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
														else:
																					a__   ,      a__    =  teacher.config.num_layers, teacher.config.num_decoder_layers
														if e is None:
																					a__    =  teacher_e
														if d is None:
																					a__    =  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__    =  teacher.config_class(**__lowerCAmelCase     )
							a__    =  AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase     )
							# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
							a__    =  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__    =  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__    =  pick_layers_to_copy(__lowerCAmelCase      ,					__lowerCAmelCase     )
							if d_layers_to_copy is None:
														a__    =  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__    =  {
							    '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)








import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)






def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Any=1_0     ):
							a__    =  []
							for _ in range(__lowerCAmelCase     ):
														lrs.append(scheduler.get_lr()[0]     )
														scheduler.step()
							return lrs






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       List[str]=1_0     ):
							a__    =  []
							for step in range(__lowerCAmelCase     ):
														lrs.append(scheduler.get_lr()[0]     )
														scheduler.step()
														if step == num_steps // 2:
																					with tempfile.TemporaryDirectory() as tmpdirname:
																												a__    =  os.path.join(__lowerCAmelCase      ,					'schedule.bin'     )
																												torch.save(scheduler.state_dict()      ,					__lowerCAmelCase     )

																												a__    =  torch.load(__lowerCAmelCase     )
																												scheduler.load_state_dict(__lowerCAmelCase     )
							return lrs







@require_torch
class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :List[Any]    ,__snake_case     :int    ,__snake_case     :Union[str, Any]   )      ->					int:
												self.assertEqual(len(__snake_case   )    ,len(__snake_case   )   )
												for a, b in zip(__snake_case    ,__snake_case   ):
																			self.assertAlmostEqual(__snake_case    ,__snake_case    ,delta=__snake_case   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					str:
												a__    =  torch.tensor([0.1, -0.2, -0.1]    ,requires_grad=__snake_case   )
												a__    =  torch.tensor([0.4, 0.2, -0.5]   )
												a__    =  nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												a__    =  AdamW(params=[w]    ,lr=2E-1    ,weight_decay=0.0   )
												for _ in range(1_00   ):
																			a__    =  criterion(__snake_case    ,__snake_case   )
																			loss.backward()
																			optimizer.step()
																			w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																			w.grad.zero_()
												self.assertListAlmostEqual(w.tolist()    ,[0.4, 0.2, -0.5]    ,tol=1E-2   )



					def 				lowerCamelCase__(   self     :Tuple   )      ->					int:
												a__    =  torch.tensor([0.1, -0.2, -0.1]    ,requires_grad=__snake_case   )
												a__    =  torch.tensor([0.4, 0.2, -0.5]   )
												a__    =  nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												a__    =  Adafactor(
												    params=[w]    ,lr=1E-2    ,eps=(1E-30, 1E-3)    ,clip_threshold=1.0    ,decay_rate=-0.8    ,betaa=__snake_case    ,weight_decay=0.0    ,relative_step=__snake_case    ,scale_parameter=__snake_case    ,warmup_init=__snake_case    ,)
												for _ in range(10_00   ):
																			a__    =  criterion(__snake_case    ,__snake_case   )
																			loss.backward()
																			optimizer.step()
																			w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																			w.grad.zero_()
												self.assertListAlmostEqual(w.tolist()    ,[0.4, 0.2, -0.5]    ,tol=1E-2   )







@require_torch
class  snake_case_     (unittest.TestCase   ):
					UpperCAmelCase__      :					str					   =    nn.Linear(5_0			,		5_0   ) if is_torch_available() else None
					UpperCAmelCase__      :					Dict					   =    AdamW(m.parameters()			,		lr=1_0.0   ) if is_torch_available() else None
					UpperCAmelCase__      :					Optional[Any]					   =    1_0
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Tuple    ,__snake_case     :int    ,__snake_case     :Any=None   )      ->					Optional[Any]:
												self.assertEqual(len(__snake_case   )    ,len(__snake_case   )   )
												for a, b in zip(__snake_case    ,__snake_case   ):
																			self.assertAlmostEqual(__snake_case    ,__snake_case    ,delta=__snake_case    ,msg=__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple   )      ->					List[Any]:
												a__    =  {'num_warmup_steps': 2, 'num_training_steps': 10}
												# schedulers doct format
												# function: (sched_args_dict, expected_learning_rates)
												a__    =  {
												    get_constant_schedule: ({}, [10.0] * self.num_steps),
												    get_constant_schedule_with_warmup: (
												        {'num_warmup_steps': 4},
												        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
												    ),
												    get_linear_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
												    ),
												    get_cosine_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
												    ),
												    get_cosine_with_hard_restarts_schedule_with_warmup: (
												        {**common_kwargs, 'num_cycles': 2},
												        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
												    ),
												    get_polynomial_decay_schedule_with_warmup: (
												        {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
												        [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
												    ),
												    get_inverse_sqrt_schedule: (
												        {'num_warmup_steps': 2},
												        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
												    ),
												}

												for scheduler_func, data in scheds.items():
																			a__   ,      a__    =  data

																			a__    =  scheduler_func(self.optimizer    ,**__snake_case   )
																			self.assertEqual(len([scheduler.get_lr()[0]]   )    ,1   )
																			a__    =  unwrap_schedule(__snake_case    ,self.num_steps   )
																			self.assertListAlmostEqual(
																			    __snake_case    ,__snake_case    ,tol=1E-2    ,msg=F'failed for {scheduler_func} in normal scheduler'    ,)

																			a__    =  scheduler_func(self.optimizer    ,**__snake_case   )
																			if scheduler_func.__name__ != "get_constant_schedule":
																										LambdaScheduleWrapper.wrap_scheduler(__snake_case   )  # wrap to test picklability of the schedule
																			a__    =  unwrap_and_save_reload_schedule(__snake_case    ,self.num_steps   )
																			self.assertListEqual(__snake_case    ,__snake_case    ,msg=F'failed for {scheduler_func} in save and reload'   )







class  snake_case_     :
					def __init__(   self     :Tuple    ,__snake_case     :str   )      ->					Any:
												a__    =  fn
					def __call__(   self     :List[str]    ,*__snake_case     :Optional[Any]    ,**__snake_case     :Optional[int]   )      ->					Union[str, Any]:
												return self.fn(*__snake_case    ,**__snake_case   )



					@classmethod
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union[str, Any]   )      ->					Dict:
												a__    =  list(map(self    ,scheduler.lr_lambdas   )   )




