




'''simple docstring'''





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

_SCREAMING_SNAKE_CASE							:       List[Any]						=			logging.getLogger()


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				argparse.ArgumentParser()
       parser.add_argument("-f"					)
       snake_case_      =				parser.parse_args()
       return args.f


def 					UpperCamelCase_(     snake_case							:	str					):

       '''simple docstring'''






       snake_case_      =				{}
       snake_case_      =				os.path.join(lowerCAmelCase__							,    "all_results.json"					)
       if os.path.exists(lowerCAmelCase__					):
              with open(lowerCAmelCase__							,    "r"					) as f:
                     snake_case_      =				json.load(lowerCAmelCase__					)
       else:
              raise ValueError(f'can\'t find {path}'					)
       return results


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				torch.cuda.is_available() and torch_device == "cuda"
       return is_using_cuda and is_apex_available()


_SCREAMING_SNAKE_CASE							:       Tuple						=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 			_snake_case		(     _SCREAMING_SNAKE_CASE							):




   @classmethod
   def        lowerCAmelCase__    ( cls							)    ->    List[Any]:

          '''simple docstring'''




          snake_case_      =				tempfile.mkdtemp()
          snake_case_      =				os.path.join(cls.tmpdir    ,							"default_config.yml"							)
          write_basic_config(save_location=cls.configPath							)
          snake_case_      =				["accelerate", "launch", "--config_file", cls.configPath]




   @classmethod
   def        lowerCAmelCase__    ( cls							)    ->    List[str]:

          '''simple docstring'''




          shutil.rmtree(cls.tmpdir							)




   @mock.patch.dict(os.environ    ,							{"WANDB_MODE": "offline"}							)
   def        lowerCAmelCase__    ( self							)    ->    Any:

          '''simple docstring'''




          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

          if is_cuda_and_apex_available():
                 testargs.append("--fp16"							)

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          self.assertGreaterEqual(result["eval_accuracy"]    ,							0.7_5							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"epoch_0"							)							)							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"glue_no_trainer"							)							)							)




   @mock.patch.dict(os.environ    ,							{"WANDB_MODE": "offline"}							)
   def        lowerCAmelCase__    ( self							)    ->    Tuple:

          '''simple docstring'''




          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

          if torch.cuda.device_count() > 1:
                 # Skipping because there are not enough batches to train the model + would need a drop_last to work.
                 return

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          self.assertLess(result["perplexity"]    ,							100							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"epoch_0"							)							)							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"clm_no_trainer"							)							)							)




   @mock.patch.dict(os.environ    ,							{"WANDB_MODE": "offline"}							)
   def        lowerCAmelCase__    ( self							)    ->    Any:

          '''simple docstring'''




          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          self.assertLess(result["perplexity"]    ,							42							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"epoch_0"							)							)							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"mlm_no_trainer"							)							)							)




   @mock.patch.dict(os.environ    ,							{"WANDB_MODE": "offline"}							)
   def        lowerCAmelCase__    ( self							)    ->    Dict:

          '''simple docstring'''




          snake_case_      =				7 if get_gpu_count() > 1 else 2

          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          self.assertGreaterEqual(result["eval_accuracy"]    ,							0.7_5							)
          self.assertLess(result["train_loss"]    ,							0.5							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"epoch_0"							)							)							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"ner_no_trainer"							)							)							)




   @unittest.skip(reason="Fix me @muellerzr"							)
   @mock.patch.dict(os.environ    ,							{"WANDB_MODE": "offline"}							)
   def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

          '''simple docstring'''




          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
          self.assertGreaterEqual(result["eval_f1"]    ,							28							)
          self.assertGreaterEqual(result["eval_exact"]    ,							28							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"epoch_0"							)							)							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"qa_no_trainer"							)							)							)




   @mock.patch.dict(os.environ    ,							{"WANDB_MODE": "offline"}							)
   def        lowerCAmelCase__    ( self							)    ->    int:

          '''simple docstring'''




          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          self.assertGreaterEqual(result["eval_accuracy"]    ,							0.8							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"swag_no_trainer"							)							)							)




   @slow
   @mock.patch.dict(os.environ    ,							{"WANDB_MODE": "offline"}							)
   def        lowerCAmelCase__    ( self							)    ->    str:

          '''simple docstring'''




          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          self.assertGreaterEqual(result["eval_rouge1"]    ,							10							)
          self.assertGreaterEqual(result["eval_rouge2"]    ,							2							)
          self.assertGreaterEqual(result["eval_rougeL"]    ,							7							)
          self.assertGreaterEqual(result["eval_rougeLsum"]    ,							7							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"epoch_0"							)							)							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"summarization_no_trainer"							)							)							)




   @slow
   @mock.patch.dict(os.environ    ,							{"WANDB_MODE": "offline"}							)
   def        lowerCAmelCase__    ( self							)    ->    Dict:

          '''simple docstring'''




          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          self.assertGreaterEqual(result["eval_bleu"]    ,							30							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"epoch_0"							)							)							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"translation_no_trainer"							)							)							)




   @slow
   def        lowerCAmelCase__    ( self							)    ->    int:

          '''simple docstring'''




          snake_case_      =				logging.StreamHandler(sys.stdout							)
          logger.addHandler(a__							)

          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          self.assertGreaterEqual(result["eval_overall_accuracy"]    ,							0.1_0							)




   @mock.patch.dict(os.environ    ,							{"WANDB_MODE": "offline"}							)
   def        lowerCAmelCase__    ( self							)    ->    str:

          '''simple docstring'''




          snake_case_      =				self.get_auto_remove_tmp_dir()
          snake_case_      =				F'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

          if is_cuda_and_apex_available():
                 testargs.append("--fp16"							)

          run_command(self._launch_args + testargs							)
          snake_case_      =				get_results(a__							)
          # The base model scores a 25%
          self.assertGreaterEqual(result["eval_accuracy"]    ,							0.6							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"step_1"							)							)							)
          self.assertTrue(os.path.exists(os.path.join(a__    ,							"image_classification_no_trainer"							)							)							)










'''simple docstring'''





import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
		import torch.distributed.checkpoint as dist_cp
		from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
		from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
		from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


_SCREAMING_SNAKE_CASE							:       Tuple						=			get_logger(__name__)


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]							,    snake_case							:	int							,    snake_case							:	List[Any]=0					):

							'''simple docstring'''






							os.makedirs(snake_case							,    exist_ok=snake_case					)
							with FSDP.state_dict_type(
							    snake_case							,    fsdp_plugin.state_dict_type							,    fsdp_plugin.state_dict_config							,    fsdp_plugin.optim_state_dict_config					):
														snake_case_      =				model.state_dict()
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					snake_case_      =				f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					if accelerator.process_index == 0:
																												logger.info(f'Saving model to {output_model_file}'					)
																												torch.save(snake_case							,    snake_case					)
																												logger.info(f'Model saved to {output_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																					snake_case_      =				(
																					    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
																					    if model_index == 0
																					    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
																					)
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Saving model to {output_model_file}'					)
																					torch.save(snake_case							,    snake_case					)
																					logger.info(f'Model saved to {output_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																					snake_case_      =				os.path.join(snake_case							,    f'{MODEL_NAME}_{model_index}'					)
																					os.makedirs(snake_case							,    exist_ok=snake_case					)
																					logger.info(f'Saving model to {ckpt_dir}'					)
																					snake_case_      =				{"model": state_dict}

																					dist_cp.save_state_dict(
																					    state_dict=snake_case							,    storage_writer=dist_cp.FileSystemWriter(snake_case					)							,    planner=DefaultSavePlanner()							,    )
																					logger.info(f'Model saved to {ckpt_dir}'					)


def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	Optional[Any]							,    snake_case							:	int							,    snake_case							:	Union[str, Any]							,    snake_case							:	Any=0					):

							'''simple docstring'''






							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    snake_case							,    fsdp_plugin.state_dict_type							,    fsdp_plugin.state_dict_config							,    fsdp_plugin.optim_state_dict_config					):
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					if type(snake_case					) != FSDP and accelerator.process_index != 0:
																												if not fsdp_plugin.sync_module_states:
																																			raise ValueError(
																																			    "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
																																			    "initializing FSDP object"					)
																												return
																					snake_case_      =				f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Loading model from {input_model_file}'					)
																					snake_case_      =				torch.load(snake_case					)
																					logger.info(f'Model loaded from {input_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																					snake_case_      =				(
																					    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
																					    if model_index == 0
																					    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
																					)
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Loading model from {input_model_file}'					)
																					snake_case_      =				torch.load(snake_case					)
																					logger.info(f'Model loaded from {input_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																					snake_case_      =				(
																					    os.path.join(snake_case							,    f'{MODEL_NAME}_{model_index}'					)
																					    if f'{MODEL_NAME}' not in input_dir
																					    else input_dir
																					)
																					logger.info(f'Loading model from {ckpt_dir}'					)
																					snake_case_      =				{"model": model.state_dict()}
																					dist_cp.load_state_dict(
																					    state_dict=snake_case							,    storage_reader=dist_cp.FileSystemReader(snake_case					)							,    planner=DefaultLoadPlanner()							,    )
																					snake_case_      =				state_dict["model"]
																					logger.info(f'Model loaded from {ckpt_dir}'					)
														model.load_state_dict(snake_case					)


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	List[str]							,    snake_case							:	Any							,    snake_case							:	Tuple							,    snake_case							:	Optional[Any]							,    snake_case							:	Tuple=0					):

							'''simple docstring'''






							os.makedirs(snake_case							,    exist_ok=snake_case					)
							with FSDP.state_dict_type(
							    snake_case							,    fsdp_plugin.state_dict_type							,    fsdp_plugin.state_dict_config							,    fsdp_plugin.optim_state_dict_config					):
														snake_case_      =				FSDP.optim_state_dict(snake_case							,    snake_case					)
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					if accelerator.process_index == 0:
																												snake_case_      =				(
																												    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																												)
																												snake_case_      =				os.path.join(snake_case							,    snake_case					)
																												logger.info(f'Saving Optimizer state to {output_optimizer_file}'					)
																												torch.save(snake_case							,    snake_case					)
																												logger.info(f'Optimizer state saved in {output_optimizer_file}'					)
														else:
																					snake_case_      =				os.path.join(snake_case							,    f'{OPTIMIZER_NAME}_{optimizer_index}'					)
																					os.makedirs(snake_case							,    exist_ok=snake_case					)
																					logger.info(f'Saving Optimizer state to {ckpt_dir}'					)
																					dist_cp.save_state_dict(
																					    state_dict={"optimizer": optim_state}							,    storage_writer=dist_cp.FileSystemWriter(snake_case					)							,    planner=DefaultSavePlanner()							,    )
																					logger.info(f'Optimizer state saved in {ckpt_dir}'					)




def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	List[str]							,    snake_case							:	Union[str, Any]							,    snake_case							:	int							,    snake_case							:	Optional[int]							,    snake_case							:	Union[str, Any]=0					):

							'''simple docstring'''






							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    snake_case							,    fsdp_plugin.state_dict_type							,    fsdp_plugin.state_dict_config							,    fsdp_plugin.optim_state_dict_config					):
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					snake_case_      =				None
																					# below check should work but currently it isn't working (mostly opytorch issue),
																					# in the meantime disabling it at the cost of excess memory usage
																					# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
																					snake_case_      =				(
																					    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																					)
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Loading Optimizer state from {input_optimizer_file}'					)
																					snake_case_      =				torch.load(snake_case					)
																					logger.info(f'Optimizer state loaded from {input_optimizer_file}'					)
														else:
																					snake_case_      =				(
																					    os.path.join(snake_case							,    f'{OPTIMIZER_NAME}_{optimizer_index}'					)
																					    if f'{OPTIMIZER_NAME}' not in input_dir
																					    else input_dir
																					)
																					logger.info(f'Loading Optimizer from {ckpt_dir}'					)
																					snake_case_      =				load_sharded_optimizer_state_dict(
																					    model_state_dict=model.state_dict()							,    optimizer_key="optimizer"							,    storage_reader=dist_cp.FileSystemReader(snake_case					)							,    )
																					snake_case_      =				optim_state["optimizer"]
																					logger.info(f'Optimizer loaded from {ckpt_dir}'					)
														snake_case_      =				FSDP.optim_state_dict_to_load(snake_case							,    snake_case							,    snake_case					)
														optimizer.load_state_dict(snake_case					)




