






import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_a       =    16
_a       =    32


def __A       ( __lowerCAmelCase      )->    Dict:





						"""simple docstring"""



						return int(x / 2**20      )





class __lowerCamelCase					:





	"""simple docstring"""






	def __enter__(				self       ):
							"""simple docstring"""







							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							_UpperCAmelCase							   =     torch.cuda.memory_allocated()
							_UpperCAmelCase							   =     psutil.Process()

							_UpperCAmelCase							   =     self.cpu_mem_used()
							_UpperCAmelCase							   =     True
							_UpperCAmelCase							   =     threading.Thread(target=self.peak_monitor_func       )
							_UpperCAmelCase							   =     True
							peak_monitor_thread.start()
							return self

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return self.process.memory_info().rss

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     -1

							while True:
													_UpperCAmelCase							   =     max(self.cpu_mem_used()			,       self.cpu_peak       )

													# can't sleep or will not catch the peak right (this comment is here on purpose)
													# time.sleep(0.001) # 1msec

													if not self.peak_monitoring:
																			break

	def __exit__(				self			,       *UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     False

							gc.collect()
							torch.cuda.empty_cache()
							_UpperCAmelCase							   =     torch.cuda.memory_allocated()
							_UpperCAmelCase							   =     torch.cuda.max_memory_allocated()
							_UpperCAmelCase							   =     bamb(self.end - self.begin       )
							_UpperCAmelCase							   =     bamb(self.peak - self.begin       )

							_UpperCAmelCase							   =     self.cpu_mem_used()
							_UpperCAmelCase							   =     bamb(self.cpu_end - self.cpu_begin       )
							_UpperCAmelCase							   =     bamb(self.cpu_peak - self.cpu_begin       )
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		_a       =    mocked_dataloaders  # noqa: F811


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						if os.environ.get('TESTING_MOCKED_DATALOADERS'					,						__lowerCAmelCase      ) == "1":
												_UpperCAmelCase							   =     2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						_UpperCAmelCase							   =     FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=__lowerCAmelCase					,						ranka_only=__lowerCAmelCase      )					,						optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=__lowerCAmelCase					,						ranka_only=__lowerCAmelCase      )					,						)

						# Initialize accelerator
						if args.with_tracking:
												_UpperCAmelCase							   =     Accelerator(
												    cpu=args.cpu					,						mixed_precision=args.mixed_precision					,						log_with='wandb'					,						project_dir=args.logging_dir					,						fsdp_plugin=__lowerCAmelCase					,						)
						else:
												_UpperCAmelCase							   =     Accelerator(fsdp_plugin=__lowerCAmelCase      )
						accelerator.print(accelerator.distributed_type      )

						if hasattr(args.checkpointing_steps					,						'isdigit'      ):
												if args.checkpointing_steps == "epoch":
																		_UpperCAmelCase							   =     args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		_UpperCAmelCase							   =     int(args.checkpointing_steps      )
												else:
																		raise ValueError(
																		    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
						else:
												_UpperCAmelCase							   =     None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_UpperCAmelCase							   =     config['lr']
						_UpperCAmelCase							   =     int(config['num_epochs']      )
						_UpperCAmelCase							   =     int(config['seed']      )
						_UpperCAmelCase							   =     int(config['batch_size']      )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												_UpperCAmelCase							   =     vars(__lowerCAmelCase      )
												accelerator.init_trackers('fsdp_glue_no_trainer'					,						__lowerCAmelCase      )

						_UpperCAmelCase							   =     AutoTokenizer.from_pretrained(args.model_name_or_path      )
						_UpperCAmelCase							   =     load_dataset('glue'					,						'mrpc'      )
						_UpperCAmelCase							   =     evaluate.load('glue'					,						'mrpc'      )

						def tokenize_function(__lowerCAmelCase      ):
												# max_length=None => use the model max length (it's actually the default)
												_UpperCAmelCase							   =     tokenizer(examples['sentence1']					,						examples['sentence2']					,						truncation=__lowerCAmelCase					,						max_length=__lowerCAmelCase      )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												_UpperCAmelCase							   =     datasets.map(
												    __lowerCAmelCase					,						batched=__lowerCAmelCase					,						remove_columns=['idx', 'sentence1', 'sentence2']					,						)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						_UpperCAmelCase							   =     tokenized_datasets.rename_column('label'					,						'labels'      )

						# If the batch size is too big we use gradient accumulation
						_UpperCAmelCase							   =     1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												_UpperCAmelCase							   =     batch_size // MAX_GPU_BATCH_SIZE
												_UpperCAmelCase							   =     MAX_GPU_BATCH_SIZE

						def collate_fn(__lowerCAmelCase      ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												_UpperCAmelCase							   =     128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		_UpperCAmelCase							   =     16
												elif accelerator.mixed_precision != "no":
																		_UpperCAmelCase							   =     8
												else:
																		_UpperCAmelCase							   =     None

												return tokenizer.pad(
												    __lowerCAmelCase					,						padding='longest'					,						max_length=__lowerCAmelCase					,						pad_to_multiple_of=__lowerCAmelCase					,						return_tensors='pt'					,						)

						# Instantiate dataloaders.
						_UpperCAmelCase							   =     DataLoader(
						    tokenized_datasets['train']					,						shuffle=__lowerCAmelCase					,						collate_fn=__lowerCAmelCase					,						batch_size=__lowerCAmelCase      )
						_UpperCAmelCase							   =     DataLoader(
						    tokenized_datasets['validation']					,						shuffle=__lowerCAmelCase					,						collate_fn=__lowerCAmelCase					,						batch_size=__lowerCAmelCase      )

						set_seed(__lowerCAmelCase      )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_UpperCAmelCase							   =     AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path					,						return_dict=__lowerCAmelCase					,						low_cpu_mem_usage=__lowerCAmelCase      )

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						_UpperCAmelCase							   =     accelerator.prepare(__lowerCAmelCase      )
						accelerator.print(__lowerCAmelCase      )

						# Instantiate optimizer
						# New Code #
						# For FSDP feature, at present it doesn't support multiple parameter groups,
						# so we need to create a single parameter group for the whole model
						_UpperCAmelCase							   =     torch.optim.AdamW(params=model.parameters()					,						lr=__lowerCAmelCase					,						weight_decay=2E-4      )

						# Instantiate scheduler
						_UpperCAmelCase							   =     get_linear_schedule_with_warmup(
						    optimizer=__lowerCAmelCase					,						num_warmup_steps=10					,						num_training_steps=(len(__lowerCAmelCase      ) * num_epochs) // gradient_accumulation_steps					,						)

						# New Code #
						# For FSDP feature, prepare everything except the model as we have already prepared the model
						# before creating the optimizer
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase							   =     accelerator.prepare(
						    __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

						_UpperCAmelCase							   =     0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""      )
																		accelerator.load_state(args.resume_from_checkpoint      )
																		_UpperCAmelCase							   =     os.path.basename(args.resume_from_checkpoint      )
												else:
																		# Get the most recent checkpoint
																		_UpperCAmelCase							   =     [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime      )
																		_UpperCAmelCase							   =     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												_UpperCAmelCase							   =     os.path.splitext(__lowerCAmelCase      )[0]

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace('epoch_'					,						''      )      )
																		_UpperCAmelCase							   =     None
												else:
																		_UpperCAmelCase							   =     int(training_difference.replace('step_'					,						''      )      )
																		num_epochs -= resume_step // len(__lowerCAmelCase      )
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		_UpperCAmelCase							   =     (num_epochs * len(__lowerCAmelCase      )) - resume_step

    # Now we train the model
						for epoch in range(__lowerCAmelCase      ):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								_UpperCAmelCase							   =     0
																		for step, batch in enumerate(__lowerCAmelCase      ):
																								# We need to skip steps until we reach the resumed step
																								if args.resume_from_checkpoint and epoch == 0:
																														if resume_step is not None and step < resume_step:
																																				pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device      )
																								_UpperCAmelCase							   =     model(**__lowerCAmelCase      )
																								_UpperCAmelCase							   =     outputs.loss
																								_UpperCAmelCase							   =     loss / gradient_accumulation_steps
																								# We keep track of the loss at each epoch
																								if args.with_tracking:
																														total_loss += loss.detach().float()
																								accelerator.backward(__lowerCAmelCase      )
																								if step % gradient_accumulation_steps == 0:
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# accelerator.print(lr_scheduler.get_lr())

																								overall_step += 1

																								if isinstance(__lowerCAmelCase					,						__lowerCAmelCase      ):
																														_UpperCAmelCase							   =     F"""step_{overall_step}"""
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										_UpperCAmelCase							   =     os.path.join(args.output_dir					,						__lowerCAmelCase      )
																																				accelerator.save_state(__lowerCAmelCase      )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin      )      )      )
												accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used      )      )
												accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked      )      )
												accelerator.print(
												    'Total Peak Memory consumed during the train (max): {}'.format(
												        tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin      ),
																		    }					,						step=__lowerCAmelCase					,						)

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		model.eval()
																		for step, batch in enumerate(__lowerCAmelCase      ):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device      )
																								with torch.no_grad():
																														_UpperCAmelCase							   =     model(**__lowerCAmelCase      )
																								_UpperCAmelCase							   =     outputs.logits.argmax(dim=-1      )
																								_UpperCAmelCase   ,					_UpperCAmelCase							   =     accelerator.gather_for_metrics((predictions, batch['labels'])      )
																								metric.add_batch(
																								    predictions=__lowerCAmelCase					,						references=__lowerCAmelCase					,						)

																		_UpperCAmelCase							   =     metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F"""epoch {epoch}:"""					,						__lowerCAmelCase      )
																		if args.with_tracking:
																								accelerator.log(
																								    {
																								        'accuracy': eval_metric['accuracy'],
																								        'f1': eval_metric['f1'],
																								        'train_loss': total_loss.item() / len(__lowerCAmelCase      ),
																								    }					,						step=__lowerCAmelCase					,						)

																		if checkpointing_steps == "epoch":
																								_UpperCAmelCase							   =     F"""epoch_{epoch}"""
																								if args.output_dir is not None:
																														_UpperCAmelCase							   =     os.path.join(args.output_dir					,						__lowerCAmelCase      )
																								accelerator.save_state(__lowerCAmelCase      )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin      )      )      )
												accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used      )      )
												accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked      )      )
												accelerator.print(
												    'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin      ),
																		    }					,						step=__lowerCAmelCase					,						)

						if args.with_tracking:
												accelerator.end_training()


def __A       ( )->    Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase							   =     argparse.ArgumentParser(description='Simple example of training script.'      )
						parser.add_argument(
						    '--mixed_precision'					,						type=__lowerCAmelCase					,						default=__lowerCAmelCase					,						choices=['no', 'fp16', 'bf16', 'fp8']					,						help='Whether to use mixed precision. Choose'
						    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
						    'and an Nvidia Ampere GPU.'					,						)
						parser.add_argument('--cpu'					,						action='store_true'					,						help='If passed, will train on the CPU.'      )
						parser.add_argument(
						    '--checkpointing_steps'					,						type=__lowerCAmelCase					,						default=__lowerCAmelCase					,						help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'					,						)
						parser.add_argument(
						    '--resume_from_checkpoint'					,						type=__lowerCAmelCase					,						default=__lowerCAmelCase					,						help='If the training should continue from a checkpoint folder.'					,						)
						parser.add_argument(
						    '--with_tracking'					,						action='store_true'					,						help='Whether to load in all available experiment trackers from the environment and use them for logging.'					,						)
						parser.add_argument(
						    '--output_dir'					,						type=__lowerCAmelCase					,						default='.'					,						help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,						)
						parser.add_argument(
						    '--logging_dir'					,						type=__lowerCAmelCase					,						default='logs'					,						help='Location on where to store experiment tracking logs`'					,						)
						parser.add_argument(
						    '--model_name_or_path'					,						type=__lowerCAmelCase					,						help='Path to pretrained model or model identifier from huggingface.co/models.'					,						required=__lowerCAmelCase					,						)
						_UpperCAmelCase							   =     parser.parse_args()
						_UpperCAmelCase							   =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(__lowerCAmelCase					,						__lowerCAmelCase      )


if __name__ == "__main__":
		main()








import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_a       =    logging.getLogger()
_a       =    torch.cuda.is_available()
_a       =    {
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Dict:





						"""simple docstring"""



						_UpperCAmelCase							   =     '\n'.join(__lowerCAmelCase      )
						Path(__lowerCAmelCase      ).open('w'      ).writelines(__lowerCAmelCase      )


_a       =    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_a       =    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_a       =    '''patrickvonplaten/t5-tiny-random'''
_a       =    '''sshleifer/t5-tinier-random'''
_a       =    '''sshleifer/bart-tiny-random'''
_a       =    '''sshleifer/tiny-mbart'''
_a       =    '''sshleifer/tiny-marian-en-de'''
_a       =    '''stas/tiny-wmt19-en-de'''


_a       =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def __A       ( __lowerCAmelCase      )->    Tuple:





						"""simple docstring"""



						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(__lowerCAmelCase					,						F"""{split}.source"""      )					,						__lowerCAmelCase      )
												_dump_articles(os.path.join(__lowerCAmelCase					,						F"""{split}.target"""      )					,						__lowerCAmelCase      )
						return tmp_dir





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	@classmethod
	def 			UpperCamelCase       (				cls       ):
							"""simple docstring"""







							logging.disable(logging.CRITICAL       )  # remove noisy download output from tracebacks
							return cls

	@slow
	@require_torch_gpu
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     list_models()
							_UpperCAmelCase							   =     'sshleifer'
							_UpperCAmelCase							   =     [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase       )]
							_UpperCAmelCase							   =     ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
							_UpperCAmelCase							   =     []
							for m in model_ids:
													if m in allowed_to_be_broken:
																			continue
													try:
																			AutoConfig.from_pretrained(UpperCAmelCase       )
													except Exception:
																			failures.append(UpperCAmelCase       )
							assert not failures, F"""The following models could not be loaded through AutoConfig: {failures}"""

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
							self._test_distiller_cli(UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'student_encoder_layers': 2,
							    'student_decoder_layers': 1,
							    'max_epochs': 4,
							    'val_check_interval': 0.25,
							    'alpha_hid': 2.0,
							    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
							}
							_UpperCAmelCase							   =     self._test_distiller_cli(UpperCAmelCase			,       check_contents=UpperCAmelCase       )

							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('*.ckpt'       )       )
							self.assertEqual(1			,       len(UpperCAmelCase       )       )
							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('**/*.bin'       )       )
							self.assertEqual(len(UpperCAmelCase       )			,       2       )
							_UpperCAmelCase							   =     lmap(str.strip			,       Path(model.hparams.data_dir       ).joinpath('test.source'       ).open().readlines()       )
							_UpperCAmelCase							   =     tempfile.mktemp()  # XXX: not being cleaned up
							generate_summaries_or_translations(UpperCAmelCase			,       UpperCAmelCase			,       str(model.output_dir / 'best_tfmr'       )       )
							self.assertTrue(Path(UpperCAmelCase       ).exists()       )

							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							convert_pl_to_hf(ckpts[0]			,       transformer_ckpts[0].parent			,       UpperCAmelCase       )
							assert os.path.exists(os.path.join(UpperCAmelCase			,       'pytorch_model.bin'       )       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase       )
							_UpperCAmelCase   ,					_UpperCAmelCase							   =     model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
							_UpperCAmelCase							   =     torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]			,       dtype=torch.long			,       device=model.device       )
							_UpperCAmelCase							   =     target_ids[:, :-1].contiguous()  # Why this line?
							_UpperCAmelCase							   =     target_ids[:, 1:].clone()  # why clone?
							_UpperCAmelCase							   =     model(
							    UpperCAmelCase			,       attention_mask=UpperCAmelCase			,       decoder_input_ids=UpperCAmelCase			,       labels=UpperCAmelCase			,       use_cache=UpperCAmelCase       ).loss

							_UpperCAmelCase							   =     model(UpperCAmelCase			,       attention_mask=UpperCAmelCase			,       decoder_input_ids=UpperCAmelCase			,       use_cache=UpperCAmelCase       ).logits

							_UpperCAmelCase							   =     nn.functional.log_softmax(UpperCAmelCase			,       dim=-1       )
							_UpperCAmelCase   ,					_UpperCAmelCase							   =     label_smoothed_nll_loss(
							    UpperCAmelCase			,       UpperCAmelCase			,       0.1			,       ignore_index=model.config.pad_token_id       )
							with self.assertRaises(UpperCAmelCase       ):
													# TODO: understand why this breaks
													self.assertEqual(UpperCAmelCase			,       UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'student_encoder_layers': 2,
							    'student_decoder_layers': 1,
							    'num_train_epochs': 4,
							    'val_check_interval': 0.25,
							    'alpha_hid': 2.0,
							    'task': 'translation',
							    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
							    'tokenizer_name': MBART_TINY,
							    'teacher': MBART_TINY,
							    'src_lang': 'en_XX',
							    'tgt_lang': 'ro_RO',
							}
							_UpperCAmelCase							   =     self._test_distiller_cli(UpperCAmelCase			,       check_contents=UpperCAmelCase       )
							assert model.model.config.model_type == "mbart"

							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('*.ckpt'       )       )
							self.assertEqual(1			,       len(UpperCAmelCase       )       )
							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('**/*.bin'       )       )
							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('best_tfmr/*'       )       )
							assert len(UpperCAmelCase       ) > 2
							self.assertEqual(len(UpperCAmelCase       )			,       2       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'student_encoder_layers': 1,
							    'student_decoder_layers': 1,
							    'alpha_hid': 2.0,
							    'teacher': T5_TINY,
							    'model_name_or_path': T5_TINY,
							    'tokenizer_name': T5_TINY,
							}
							self._test_distiller_cli(UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'teacher': T5_TINY,
							    'student': T5_TINIER,
							    'model_name_or_path': T5_TINIER,
							    'tokenizer_name': T5_TINIER,
							}
							self._test_distiller_cli(UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase=True       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'label_smoothing': 0.0,
							    'early_stopping_patience': -1,
							    'train_batch_size': 1,
							    'eval_batch_size': 2,
							    'max_epochs': 2,
							    'alpha_mlm': 0.2,
							    'alpha_ce': 0.8,
							    'do_predict': True,
							    'model_name_or_path': 'sshleifer/tinier_bart',
							    'teacher': CHEAP_ARGS['model_name_or_path'],
							    'val_check_interval': 0.5,
							}
							default_updates.update(UpperCAmelCase       )
							_UpperCAmelCase							   =     CHEAP_ARGS.copy()
							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       **UpperCAmelCase       )
							_UpperCAmelCase							   =     distill_main(argparse.Namespace(**UpperCAmelCase       )       )
							if not check_contents:
													return model
							_UpperCAmelCase							   =     os.listdir(UpperCAmelCase       )
							_UpperCAmelCase							   =     {os.path.basename(UpperCAmelCase       ) for p in contents}
							_UpperCAmelCase							   =     [p for p in contents if p.endswith('ckpt'       )]
							assert len(UpperCAmelCase       ) > 0

							self.assertIn('test_generations.txt'			,       UpperCAmelCase       )
							self.assertIn('test_results.txt'			,       UpperCAmelCase       )

							_UpperCAmelCase							   =     load_json(model.metrics_save_path       )
							_UpperCAmelCase							   =     metrics['val'][-1]
							self.assertGreaterEqual(last_step_stats['val_avg_gen_time']			,       0.01       )
							self.assertGreaterEqual(1.0			,       last_step_stats['val_avg_gen_time']       )
							self.assertIsInstance(last_step_stats[F"""val_avg_{model.val_metric}"""]			,       UpperCAmelCase       )
							_UpperCAmelCase							   =     int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1       )
							self.assertEqual(len(metrics['val']       )			,       UpperCAmelCase       )
							self.assertEqual(len(metrics['test']       )			,       1       )
							return model





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,       )
	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()
							_UpperCAmelCase							   =     'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
							_UpperCAmelCase							   =     0.1 if task == 'translation' else 0

							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							args_d.update(
							    data_dir=UpperCAmelCase			,       model_name_or_path=UpperCAmelCase			,       tokenizer_name=UpperCAmelCase			,       train_batch_size=2			,       eval_batch_size=2			,       output_dir=UpperCAmelCase			,       do_predict=UpperCAmelCase			,       task=UpperCAmelCase			,       src_lang='en_XX'			,       tgt_lang='ro_RO'			,       freeze_encoder=UpperCAmelCase			,       freeze_embeds=UpperCAmelCase			,       )
							assert "n_train" in args_d
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )

							_UpperCAmelCase							   =     module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
													_UpperCAmelCase							   =     module.model.lm_head
													assert not lm_head.weight.requires_grad
													assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
													_UpperCAmelCase							   =     module.model.model
													_UpperCAmelCase							   =     fsmt.decoder.embed_positions
													assert not embed_pos.weight.requires_grad
													assert not fsmt.decoder.embed_tokens.weight.requires_grad
													# check that embeds are not the same
													assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
							else:
													_UpperCAmelCase							   =     module.model.model
													_UpperCAmelCase							   =     bart.decoder.embed_positions
													assert not embed_pos.weight.requires_grad
													assert not bart.shared.weight.requires_grad
													# check that embeds are the same
													assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
													assert bart.decoder.embed_tokens == bart.shared

							_UpperCAmelCase							   =     load_json(module.output_dir / 'text_batch.json'       )
							assert isinstance(UpperCAmelCase			,       UpperCAmelCase       )
							assert len(UpperCAmelCase       ) >= 4

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()

							_UpperCAmelCase							   =     'summarization'
							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )

							args_d.update(
							    data_dir=UpperCAmelCase			,       tokenizer_name=UpperCAmelCase			,       train_batch_size=2			,       eval_batch_size=2			,       do_predict=UpperCAmelCase			,       task=UpperCAmelCase			,       src_lang='en_XX'			,       tgt_lang='ro_RO'			,       freeze_encoder=UpperCAmelCase			,       freeze_embeds=UpperCAmelCase			,       )

							# test models whose config includes the extra_model_args
							_UpperCAmelCase							   =     BART_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       )
							_UpperCAmelCase							   =     ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
							for p in extra_model_params:
													_UpperCAmelCase							   =     0.5
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )
							for p in extra_model_params:
													assert getattr(model.config			,       UpperCAmelCase       ) == 0.5, F"""failed to override the model config for param {p}"""

							# test models whose config doesn't include the extra_model_args
							_UpperCAmelCase							   =     T5_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       )
							_UpperCAmelCase							   =     'encoder_layerdrop'
							_UpperCAmelCase							   =     0.5
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													_UpperCAmelCase							   =     main(UpperCAmelCase       )
							assert str(excinfo.value       ) == F"""model config doesn't have a `{unsupported_param}` attribute"""

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()

							_UpperCAmelCase							   =     'summarization'
							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )

							_UpperCAmelCase							   =     BART_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()

							args_d.update(
							    data_dir=UpperCAmelCase			,       model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       tokenizer_name=UpperCAmelCase			,       train_batch_size=2			,       eval_batch_size=2			,       do_predict=UpperCAmelCase			,       task=UpperCAmelCase			,       src_lang='en_XX'			,       tgt_lang='ro_RO'			,       freeze_encoder=UpperCAmelCase			,       freeze_embeds=UpperCAmelCase			,       )

							# emulate finetune.py
							_UpperCAmelCase							   =     argparse.ArgumentParser()
							_UpperCAmelCase							   =     pl.Trainer.add_argparse_args(UpperCAmelCase       )
							_UpperCAmelCase							   =     SummarizationModule.add_model_specific_args(UpperCAmelCase			,       os.getcwd()       )
							_UpperCAmelCase							   =     {'--help': True}

							# --help test
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													with CaptureStdout() as cs:
																			_UpperCAmelCase							   =     parser.parse_args(UpperCAmelCase       )
													assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							_UpperCAmelCase							   =     lightning_base.arg_to_scheduler_metavar
							assert expected in cs.out, "--help is expected to list the supported schedulers"

							# --lr_scheduler=non_existing_scheduler test
							_UpperCAmelCase							   =     'non_existing_scheduler'
							_UpperCAmelCase							   =     {F"""--lr_scheduler={unsupported_param}"""}
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													with CaptureStderr() as cs:
																			_UpperCAmelCase							   =     parser.parse_args(UpperCAmelCase       )
													assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							_UpperCAmelCase							   =     F"""invalid choice: '{unsupported_param}'"""
							assert expected in cs.err, F"""should have bailed on invalid choice of scheduler {unsupported_param}"""

							# --lr_scheduler=existing_scheduler test
							_UpperCAmelCase							   =     'cosine'
							_UpperCAmelCase							   =     args_d.copy()
							_UpperCAmelCase							   =     supported_param
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )
							assert (
							    getattr(model.hparams			,       'lr_scheduler'       ) == supported_param
							), F"""lr_scheduler={supported_param} shouldn't fail"""
