



'''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)

lowerCAmelCase	:		List[str]				=   logging.getLogger()



def   __lowerCAmelCase					(							):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							):






	'''simple docstring'''




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


lowerCAmelCase	:		Dict				=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					@classmethod
					def        UpperCAmelCase_ (    cls     )     ->			str:
						# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
						__lowerCAmelCase												=  tempfile.mkdtemp()
						__lowerCAmelCase												=  os.path.join(cls.tmpdir			,				"default_config.yml"     )
						write_basic_config(save_location=cls.configPath     )
						__lowerCAmelCase												=  ["accelerate", "launch", "--config_file", cls.configPath]



					@classmethod
					def        UpperCAmelCase_ (    cls     )     ->			List[Any]:
						shutil.rmtree(cls.tmpdir     )



					@mock.patch.dict(os.environ			,				{"WANDB_MODE": "offline"}     )
					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						self.assertGreaterEqual(result["eval_accuracy"]			,				0.75     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"epoch_0"     )     )     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"glue_no_trainer"     )     )     )



					@mock.patch.dict(os.environ			,				{"WANDB_MODE": "offline"}     )
					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						self.assertLess(result["perplexity"]			,				100     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"epoch_0"     )     )     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"clm_no_trainer"     )     )     )



					@mock.patch.dict(os.environ			,				{"WANDB_MODE": "offline"}     )
					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						self.assertLess(result["perplexity"]			,				42     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"epoch_0"     )     )     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"mlm_no_trainer"     )     )     )



					@mock.patch.dict(os.environ			,				{"WANDB_MODE": "offline"}     )
					def        UpperCAmelCase_ (    self     )     ->			str:
						# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
						__lowerCAmelCase												=  7 if get_gpu_count() > 1 else 2

						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						self.assertGreaterEqual(result["eval_accuracy"]			,				0.75     )
						self.assertLess(result["train_loss"]			,				0.5     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"epoch_0"     )     )     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"ner_no_trainer"     )     )     )



					@unittest.skip(reason="Fix me @muellerzr"     )
					@mock.patch.dict(os.environ			,				{"WANDB_MODE": "offline"}     )
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						# 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(UpperCamelCase			,				"epoch_0"     )     )     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"qa_no_trainer"     )     )     )



					@mock.patch.dict(os.environ			,				{"WANDB_MODE": "offline"}     )
					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						self.assertGreaterEqual(result["eval_accuracy"]			,				0.8     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"swag_no_trainer"     )     )     )



					@slow
					@mock.patch.dict(os.environ			,				{"WANDB_MODE": "offline"}     )
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						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(UpperCamelCase			,				"epoch_0"     )     )     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"summarization_no_trainer"     )     )     )



					@slow
					@mock.patch.dict(os.environ			,				{"WANDB_MODE": "offline"}     )
					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						self.assertGreaterEqual(result["eval_bleu"]			,				30     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"epoch_0"     )     )     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"translation_no_trainer"     )     )     )



					@slow
					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  logging.StreamHandler(sys.stdout     )
						logger.addHandler(UpperCamelCase     )

						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						self.assertGreaterEqual(result["eval_overall_accuracy"]			,				0.10     )



					@mock.patch.dict(os.environ			,				{"WANDB_MODE": "offline"}     )
					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.get_auto_remove_tmp_dir()
						__lowerCAmelCase												=  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     )
						__lowerCAmelCase												=  get_results(UpperCamelCase     )
						# The base model scores a 25%
						self.assertGreaterEqual(result["eval_accuracy"]			,				0.6     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"step_1"     )     )     )
						self.assertTrue(os.path.exists(os.path.join(UpperCamelCase			,				"image_classification_no_trainer"     )     )     )




'''simple docstring'''



from __future__ import annotations



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




	if not nums:
		raise ValueError("List is empty"					)
	return sum(lowerCamelCase					) / len(lowerCamelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()