






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__	:			Dict			   = logging.get_logger(__name__)






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			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()				)


A__	:			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))},
}
A__	:			int			   = {
    # 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_				):






				'''simple docstring'''
				try:
								lowercase__    =			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_				):






				'''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(__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_		,   ):






				'''simple docstring'''
				lowercase__    =			'''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
								lowercase__    =			AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase				).eval()
				else:
								assert isinstance(__lowerCAmelCase		,   __lowerCAmelCase				), F"""teacher must be a model or string got type {type(__lowerCAmelCase				)}"""
				lowercase__    =			teacher.config.to_diff_dict()

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

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


snake_case							:       List[Any]     			=      logging.getLogger(__name__)






def 							__lowercase   ( __lowerCAmelCase							:				str=2					,    __lowerCAmelCase							:				Optional[int]=3					,    __lowerCAmelCase							:				List[str]=1_6					,    __lowerCAmelCase							:				int = 1_0					,    __lowerCAmelCase							:				int = 2   ):

							def get_dataset(__lowerCAmelCase							:				Dict   ):
														a__             =     torch.randn(batch_size * n_batches					,    1   )
														return TensorDataset(__lowerCAmelCase					,    a * x + b + 0.1 * torch.randn(batch_size * n_batches					,    1   )   )

							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     get_dataset(__lowerCAmelCase   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							a__             =     DataLoader(__lowerCAmelCase					,    shuffle=__lowerCAmelCase					,    batch_size=__lowerCAmelCase					,    num_workers=4   )
							return (train_dataloader, valid_dataloader)






def 							__lowercase   ( __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Optional[int]=None   ):
							a__             =     []
							for epoch in range(__lowerCAmelCase   ):
														# Train quickly
														model.train()
														for batch in dataloader:
																					a__      ,					a__             =     batch
																					a__             =     model(__lowerCAmelCase   )
																					a__             =     torch.nn.functional.mse_loss(__lowerCAmelCase					,    __lowerCAmelCase   )
																					accelerator.backward(__lowerCAmelCase   )
																					optimizer.step()
																					optimizer.zero_grad()
														rands.append(random.random()   )  # Introduce some randomness
														if scheduler is not None:
																					scheduler.step()
							return rands







class        snake_case_   (nn.Module      ):
					def __init__(      self   :Any      )					->    Union[str, Any]:
												super().__init__()
												a__             =     nn.Parameter(torch.randn(1      )      )
												a__             =     nn.Parameter(torch.randn(1      )      )



					def  lowerCamelCase__(      self   :List[str]			,__snake_case   :Union[str, Any]      )					->    str:
												return x * self.a + self.b







class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(total_limit=1			,project_dir=__snake_case			,automatic_checkpoint_naming=__snake_case      )
																			# Train baseline
																			a__             =     Accelerator(project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			accelerator.save_state()

																			# Save second state
																			accelerator.save_state()
																			self.assertEqual(len(os.listdir(accelerator.project_dir      )      )			,1      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    str:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			# Train baseline
																			a__             =     Accelerator()
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			a__             =     os.path.join(__snake_case			,'initial'      )
																			accelerator.save_state(__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			a__             =     train(3			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()

																			# Train partially
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     Accelerator()
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			accelerator.load_state(__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )

																			a__             =     train(2			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save everything
																			a__             =     os.path.join(__snake_case			,'checkpoint'      )
																			accelerator.save_state(__snake_case      )

																			# Load everything back in and make sure all states work
																			accelerator.load_state(__snake_case      )
																			test_rands += train(1			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :str      )					->    List[str]:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case      )

																			# Train baseline
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			accelerator.save_state()
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			a__             =     train(3			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()

																			# Train partially
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(iteration=1			,automatic_checkpoint_naming=__snake_case      )
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			accelerator.load_state(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_0'      )      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )

																			a__             =     train(2			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save everything
																			accelerator.save_state()

																			# Load everything back in and make sure all states work
																			accelerator.load_state(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_1'      )      )
																			test_rands += train(1			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			((a__)      ,					(a__))             =     model.a.item(), model.b.item()
																			a__             =     optimizer.state_dict()
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
																			self.assertEqual(__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[str]:
												a__             =     torch.tensor([1, 2, 3]      )
												a__             =     torch.tensor([2, 3, 4]      )
												a__             =     DummyModel()
												a__             =     torch.optim.Adam(net.parameters()      )
												a__             =     Accelerator()
												with self.assertRaises(__snake_case      ) as ve:
																			accelerator.register_for_checkpointing(__snake_case			,__snake_case			,__snake_case			,__snake_case      )
												a__             =     str(ve.exception      )
												self.assertTrue('Item at index 0' in message      )
												self.assertTrue('Item at index 1' in message      )
												self.assertFalse('Item at index 2' in message      )
												self.assertFalse('Item at index 3' in message      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Dict:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     torch.optim.Adam(params=model.parameters()			,lr=1E-3      )
																			a__             =     torch.optim.lr_scheduler.StepLR(__snake_case			,step_size=1			,gamma=0.99      )
																			a__      ,					a__             =     dummy_dataloaders()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case      )
																			# Train baseline
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__      ,					a__      ,					a__      ,					a__      ,					a__             =     accelerator.prepare(
																			    __snake_case			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			# Save initial
																			accelerator.save_state()
																			a__             =     scheduler.state_dict()
																			train(3			,__snake_case			,__snake_case			,__snake_case			,__snake_case			,__snake_case      )
																			self.assertNotEqual(__snake_case			,scheduler.state_dict()      )

																			# Load everything back in and make sure all states work
																			accelerator.load_state(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_0'      )      )
																			self.assertEqual(__snake_case			,scheduler.state_dict()      )
					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												with tempfile.TemporaryDirectory() as tmpdir:
																			set_seed(42      )
																			a__             =     DummyModel()
																			a__             =     ProjectConfiguration(automatic_checkpoint_naming=__snake_case			,total_limit=2      )
																			# Train baseline
																			a__             =     Accelerator(project_dir=__snake_case			,project_config=__snake_case      )
																			a__             =     accelerator.prepare(__snake_case      )
																			# Save 3 states:
																			for _ in range(11      ):
																										accelerator.save_state()
																			self.assertTrue(not os.path.exists(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_0'      )      )      )
																			self.assertTrue(os.path.exists(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_9'      )      )      )
																			self.assertTrue(os.path.exists(os.path.join(__snake_case			,'checkpoints'			,'checkpoint_10'      )      )      )



					@require_cuda
					def  lowerCamelCase__(      self   :Dict      )					->    str:
												a__             =     ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
												execute_subprocess_async(__snake_case			,env=os.environ.copy()      )


if __name__ == "__main__":
						snake_case							:       Tuple     			=      '''/tmp/accelerate/state_checkpointing'''
						snake_case							:       str     			=      DummyModel()
						snake_case							:       List[Any]     			=      torch.optim.Adam(params=model.parameters(), lr=1e-3)
						snake_case							:       Union[str, Any]     			=      torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
						snake_case			,     snake_case							:       str     			=      dummy_dataloaders()
						snake_case							:       Union[str, Any]     			=      ProjectConfiguration(automatic_checkpoint_naming=True)
						# Train baseline
						snake_case							:       Dict     			=      Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
						if accelerator.process_index == 0:
												if os.path.exists(savedir):
																		shutil.rmtree(savedir)
												os.makedirs(savedir)
						snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case							:       List[str]     			=      accelerator.prepare(
						    model, optimizer, train_dataloader, valid_dataloader, scheduler
						)
						snake_case			,     snake_case							:       Any     			=      accelerator.prepare(model, optimizer)
						train(3, model, train_dataloader, optimizer, accelerator, scheduler)
						# Check that the intial optimizer is loaded on the GPU
						for group in optimizer.param_groups:
												snake_case							:       Any     			=      group['''params'''][0].device
												break
						assert param_device.type == accelerator.device.type
						snake_case							:       Union[str, Any]     			=      model.cpu()
						accelerator.wait_for_everyone()
						accelerator.save_state()
						accelerator.wait_for_everyone()

						# Check CPU state
						accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
						for group in optimizer.param_groups:
												snake_case							:       int     			=      group['''params'''][0].device
												break
						assert (
						    param_device.type == torch.device('''cpu''').type
						), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

						# Check device state
						model.to(accelerator.device)
						accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
						for group in optimizer.param_groups:
												snake_case							:       Optional[int]     			=      group['''params'''][0].device
												break
						assert (
						    param_device.type == accelerator.device.type
						), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

						# Check error
						with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
												accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
						accelerator.wait_for_everyone()
						if accelerator.process_index == 0:
												shutil.rmtree(savedir)
						accelerator.wait_for_everyone()




