






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)

lowercase    				=   logging.getLogger()





def 		__UpperCAmelCase     (	):
							snake_case_           =				argparse.ArgumentParser()
							parser.add_argument('-f')
							snake_case_           =				parser.parse_args()
							return args.f





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





def 		__UpperCAmelCase     (	):
							snake_case_           =				torch.cuda.is_available() and torch_device == 'cuda'
							return is_using_cuda and is_apex_available()


lowercase    				=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	@classmethod
	def 				_UpperCamelCase     (			cls   )			->       str:
								# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
								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 				_UpperCamelCase     (			cls   )			->       Any:
								shutil.rmtree(cls.tmpdir   )




	@mock.patch.dict(os.environ			,     {'WANDB_MODE': 'offline'}   )
	def 				_UpperCamelCase     (			self   )			->       str:
								snake_case_           =				self.get_auto_remove_tmp_dir()
								snake_case_           =				F'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.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']			,     0.75   )
								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 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_           =				self.get_auto_remove_tmp_dir()
								snake_case_           =				F'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.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']			,     1_00   )
								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 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				self.get_auto_remove_tmp_dir()
								snake_case_           =				F'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.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 				_UpperCamelCase     (			self   )			->       Any:
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								snake_case_           =				7 if get_gpu_count() > 1 else 2

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

								run_command(self._launch_args + testargs   )
								snake_case_           =				get_results(a   )
								self.assertGreaterEqual(result['eval_accuracy']			,     0.75   )
								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 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								snake_case_           =				self.get_auto_remove_tmp_dir()
								snake_case_           =				F'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.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 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				self.get_auto_remove_tmp_dir()
								snake_case_           =				F'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.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 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				self.get_auto_remove_tmp_dir()
								snake_case_           =				F'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.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 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				self.get_auto_remove_tmp_dir()
								snake_case_           =				F'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.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 				_UpperCamelCase     (			self   )			->       Tuple:
								snake_case_           =				logging.StreamHandler(sys.stdout   )
								logger.addHandler(a   )

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

								run_command(self._launch_args + testargs   )
								snake_case_           =				get_results(a   )
								self.assertGreaterEqual(result['eval_overall_accuracy']			,     0.10   )






	@mock.patch.dict(os.environ			,     {'WANDB_MODE': 'offline'}   )
	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				self.get_auto_remove_tmp_dir()
								snake_case_           =				F'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.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'   )   )   )













import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					0
	lowerCAmelCase						=					False
	lowerCAmelCase						=					3.0






class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								self.assertDictEqual(MockClass().to_kwargs()			,     {}   )
								self.assertDictEqual(MockClass(a=2   ).to_kwargs()			,     {'a': 2}   )
								self.assertDictEqual(MockClass(a=2			,     b=a   ).to_kwargs()			,     {'a': 2, 'b': True}   )
								self.assertDictEqual(MockClass(a=2			,     c=2.25   ).to_kwargs()			,     {'a': 2, 'c': 2.25}   )




	@require_cuda
	def 				_UpperCamelCase     (			self   )			->       str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								snake_case_           =				GradScalerKwargs(init_scale=10_24			,     growth_factor=2   )
								AcceleratorState._reset_state()
								snake_case_           =				Accelerator(mixed_precision='fp16'			,     kwargs_handlers=[scaler_handler]   )
								print(accelerator.use_fpaa   )
								snake_case_           =				accelerator.scaler

								# Check the kwargs have been applied
								self.assertEqual(scaler._init_scale			,     1_024.0   )
								self.assertEqual(scaler._growth_factor			,     2.0   )

								# Check the other values are at the default
								self.assertEqual(scaler._backoff_factor			,     0.5   )
								self.assertEqual(scaler._growth_interval			,     20_00   )
								self.assertEqual(scaler._enabled			,     a   )






	@require_multi_gpu
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_           =				['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
								execute_subprocess_async(a			,     env=os.environ.copy()   )


if __name__ == "__main__":
	lowercase    				=   DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	lowercase    				=   Accelerator(kwargs_handlers=[ddp_scaler])
	lowercase    				=   torch.nn.Linear(100, 200)
	lowercase    				=   accelerator.prepare(model)

	# Check the values changed in kwargs
	lowercase    				=   ""
	lowercase    				=   model.bucket_bytes_cap // (1024 * 1024)
	if observed_bucket_cap_map != 15:
		error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
	if model.find_unused_parameters is not True:
		error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

	# Check the values of the defaults
	if model.dim != 0:
		error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
	if model.broadcast_buffers is not True:
		error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
	if model.gradient_as_bucket_view is not False:
		error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)





