

'''simple docstring'''

import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


SCREAMING_SNAKE_CASE							:				Any     	=			"sshleifer/mar_enro_6_3_student"






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def   a__					(    self       )     ->							List[str]:
											super().setUp()

											SCREAMING_SNAKE_CASE_				=				cached_path(
											    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz',     extract_compressed_file=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""



				@slow
				@require_torch_gpu
				def   a__					(    self       )     ->							int:
											MarianMTModel.from_pretrained(_lowercase       )





				@slow
				@require_torch_gpu
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				{
											    '$MAX_LEN': 64,
											    '$BS': 64,
											    '$GAS': 1,
											    '$ENRO_DIR': self.data_dir,
											    'facebook/mbart-large-cc25': MARIAN_MODEL,
											    # "val_check_interval=0.25": "val_check_interval=1.0",
											    '--learning_rate=3e-5': '--learning_rate 3e-4',
											    '--num_train_epochs 6': '--num_train_epochs 1',
											}

											# Clean up bash script
											SCREAMING_SNAKE_CASE_				=				(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'       )[1].strip()
											SCREAMING_SNAKE_CASE_				=				bash_script.replace('\\\n',     ''       ).strip().replace('"$@"',     ''       )
											for k, v in env_vars_to_replace.items():
																		SCREAMING_SNAKE_CASE_				=				bash_script.replace(_lowercase,     str(_lowercase       )       )
											SCREAMING_SNAKE_CASE_				=				self.get_auto_remove_tmp_dir()

											# bash_script = bash_script.replace("--fp16 ", "")
											SCREAMING_SNAKE_CASE_				=				f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
											# XXX: args.gpus > 1 : handle multi_gpu in the future

											SCREAMING_SNAKE_CASE_				=				['finetune.py'] + bash_script.split() + args
											with patch.object(_lowercase,     'argv',     _lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				argparse.ArgumentParser()
																		SCREAMING_SNAKE_CASE_				=				pl.Trainer.add_argparse_args(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				SummarizationModule.add_model_specific_args(_lowercase,     os.getcwd()       )
																		SCREAMING_SNAKE_CASE_				=				parser.parse_args()
																		SCREAMING_SNAKE_CASE_				=				main(_lowercase       )

											# Check metrics
											SCREAMING_SNAKE_CASE_				=				load_json(model.metrics_save_path       )
											SCREAMING_SNAKE_CASE_				=				metrics['val'][0]
											SCREAMING_SNAKE_CASE_				=				metrics['val'][-1]
											self.assertEqual(len(metrics['val']       ),     (args.max_epochs / args.val_check_interval)       )
											assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""],     _lowercase       )

											self.assertGreater(last_step_stats['val_avg_gen_time'],     0.01       )
											# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
											self.assertLessEqual(last_step_stats['val_avg_gen_time'],     1.0       )

											# test learning requirements:

											# 1. BLEU improves over the course of training by more than 2 pts
											self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'],     2       )

											# 2. BLEU finishes above 17
											self.assertGreater(last_step_stats['val_avg_bleu'],     17       )

											# 3. test BLEU and val BLEU within ~1.1 pt.
											self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu']       ),     1.1       )

											# check lightning ckpt can be loaded and has a reasonable statedict
											SCREAMING_SNAKE_CASE_				=				os.listdir(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				[x for x in contents if x.endswith('.ckpt'       )][0]
											SCREAMING_SNAKE_CASE_				=				os.path.join(args.output_dir,     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				torch.load(_lowercase,     map_location='cpu'       )
											SCREAMING_SNAKE_CASE_				=				'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
											assert expected_key in ckpt["state_dict"]
											assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

											# TODO: turn on args.do_predict when PL bug fixed.
											if args.do_predict:
																		SCREAMING_SNAKE_CASE_				=				{os.path.basename(_lowercase       ) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics['test']       ) == 1






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@timeout_decorator.timeout(600       )
				@slow
				@require_torch_gpu
				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
											SCREAMING_SNAKE_CASE_				=				{
											    '--fp16_opt_level=O1': '',
											    '$MAX_LEN': 128,
											    '$BS': 16,
											    '$GAS': 1,
											    '$ENRO_DIR': data_dir,
											    '$m': 'sshleifer/student_marian_en_ro_6_1',
											    'val_check_interval=0.25': 'val_check_interval=1.0',
											}

											# Clean up bash script
											SCREAMING_SNAKE_CASE_				=				(
											    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'       )[1].strip()
											)
											SCREAMING_SNAKE_CASE_				=				bash_script.replace('\\\n',     ''       ).strip().replace('"$@"',     ''       )
											SCREAMING_SNAKE_CASE_				=				bash_script.replace('--fp16 ',     ' '       )

											for k, v in env_vars_to_replace.items():
																		SCREAMING_SNAKE_CASE_				=				bash_script.replace(_lowercase,     str(_lowercase       )       )
											SCREAMING_SNAKE_CASE_				=				self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE_				=				bash_script.replace('--fp16',     ''       )
											SCREAMING_SNAKE_CASE_				=				6
											SCREAMING_SNAKE_CASE_				=				(
											    ['distillation.py']
											    + bash_script.split()
											    + [
											        f"""--output_dir={output_dir}""",
											        '--gpus=1',
											        '--learning_rate=1e-3',
											        f"""--num_train_epochs={epochs}""",
											        '--warmup_steps=10',
											        '--val_check_interval=1.0',
											        '--do_predict',
											    ]
											)
											with patch.object(_lowercase,     'argv',     _lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				argparse.ArgumentParser()
																		SCREAMING_SNAKE_CASE_				=				pl.Trainer.add_argparse_args(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				SummarizationDistiller.add_model_specific_args(_lowercase,     os.getcwd()       )
																		SCREAMING_SNAKE_CASE_				=				parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		SCREAMING_SNAKE_CASE_				=				distill_main(_lowercase       )

											# Check metrics
											SCREAMING_SNAKE_CASE_				=				load_json(model.metrics_save_path       )
											SCREAMING_SNAKE_CASE_				=				metrics['val'][0]
											SCREAMING_SNAKE_CASE_				=				metrics['val'][-1]
											assert len(metrics['val']       ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

											assert last_step_stats["val_avg_gen_time"] >= 0.01

											assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
											assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
											assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""],     _lowercase       )

											# check lightning ckpt can be loaded and has a reasonable statedict
											SCREAMING_SNAKE_CASE_				=				os.listdir(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				[x for x in contents if x.endswith('.ckpt'       )][0]
											SCREAMING_SNAKE_CASE_				=				os.path.join(args.output_dir,     _lowercase       )
											SCREAMING_SNAKE_CASE_				=				torch.load(_lowercase,     map_location='cpu'       )
											SCREAMING_SNAKE_CASE_				=				'model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
											assert expected_key in ckpt["state_dict"]
											assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

											# TODO: turn on args.do_predict when PL bug fixed.
											if args.do_predict:
																		SCREAMING_SNAKE_CASE_				=				{os.path.basename(_lowercase       ) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics['test']       ) == 1








'''simple docstring'''

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


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




def 							_UpperCamelCase				(   )      -> List[Any]:
							# Get the sagemaker specific mp parameters from smp_options variable.
							SCREAMING_SNAKE_CASE_				=				os.getenv('SM_HP_MP_PARAMETERS'		,'{}'   )
							try:
														# Parse it and check the field "partitions" is included, it is required for model parallel.
														SCREAMING_SNAKE_CASE_				=				json.loads(lowerCAmelCase__   )
														if "partitions" not in smp_options:
																					return False
							except json.JSONDecodeError:
														return False

							# Get the sagemaker specific framework parameters from mpi_options variable.
							SCREAMING_SNAKE_CASE_				=				os.getenv('SM_FRAMEWORK_PARAMS'		,'{}'   )
							try:
														# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
														SCREAMING_SNAKE_CASE_				=				json.loads(lowerCAmelCase__   )
														if not mpi_options.get('sagemaker_mpi_enabled'		,lowerCAmelCase__   ):
																					return False
							except json.JSONDecodeError:
														return False
							# Lastly, check if the `smdistributed` module is present.
							return importlib.util.find_spec('smdistributed'   ) is not None


if is_sagemaker_model_parallel_available():
							import smdistributed.modelparallel.torch as smp

							smp.init()






@dataclass
class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     field(
				    default="""""",						metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""},						)



				def   a__					(    self       )     ->							Union[str, Any]:
											super().__post_init__()
											warnings.warn(
											    '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
											    '`TrainingArguments` instead.',     _lowercase,     )



				@cached_property
				def   a__					(    self       )     ->							"torch.device":
											logger.info('PyTorch: setting up devices'       )
											if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
																		logger.warning(
																		    'torch.distributed process group is initialized, but local_rank == -1. '
																		    'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'       )
											if self.no_cuda:
																		SCREAMING_SNAKE_CASE_				=				torch.device('cpu'       )
																		SCREAMING_SNAKE_CASE_				=				0
											elif is_sagemaker_model_parallel_available():
																		SCREAMING_SNAKE_CASE_				=				smp.local_rank()
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda',     _lowercase       )
																		SCREAMING_SNAKE_CASE_				=				1
											elif is_sagemaker_dp_enabled():
																		import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

																		torch.distributed.init_process_group(backend='smddp',     timeout=self.ddp_timeout_delta       )
																		SCREAMING_SNAKE_CASE_				=				int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'       )       )
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda',     self.local_rank       )
																		SCREAMING_SNAKE_CASE_				=				1
											elif self.local_rank == -1:
																		# if n_gpu is > 1 we'll use nn.DataParallel.
																		# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																		# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
																		# trigger an error that a device index is missing. Index 0 takes into account the
																		# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
																		# will use the first GPU in that env, i.e. GPU#1
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'       )
																		# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
																		# the default value.
																		SCREAMING_SNAKE_CASE_				=				torch.cuda.device_count()
											else:
																		# Here, we'll use torch.distributed.
																		# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
																		if not torch.distributed.is_initialized():
																									torch.distributed.init_process_group(backend='nccl',     timeout=self.ddp_timeout_delta       )
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda',     self.local_rank       )
																		SCREAMING_SNAKE_CASE_				=				1

											if device.type == "cuda":
																		torch.cuda.set_device(_lowercase       )

											return device



				@property
				def   a__					(    self       )     ->							Optional[Any]:
											if is_sagemaker_model_parallel_available():
																		return smp.dp_size()

											return super().world_size



				@property
				def   a__					(    self       )     ->							Optional[Any]:
											return not is_sagemaker_model_parallel_available()





				@property
				def   a__					(    self       )     ->							Tuple:
											return False





