






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)

_a      	=  logging.getLogger()


def __A    (      )->  Dict:





						"""simple docstring"""



						_UpperCAmelCase									=			argparse.ArgumentParser()
						parser.add_argument('-f'	)
						_UpperCAmelCase									=			parser.parse_args()
						return args.f


def __A    (      __lowerCAmelCase	)->  Dict:





						"""simple docstring"""



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


def __A    (      )->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			torch.cuda.is_available() and torch_device == 'cuda'
						return is_using_cuda and is_apex_available()


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





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






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







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

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







							shutil.rmtree(cls.tmpdir    )

	@mock.patch.dict(os.environ       ,    {'WANDB_MODE': 'offline'}    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			7 if get_gpu_count() > 1 else 2

							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			logging.StreamHandler(sys.stdout    )
							logger.addHandler(UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			get_results(UpperCAmelCase    )
							self.assertGreaterEqual(result['eval_overall_accuracy']       ,    0.10    )

	@mock.patch.dict(os.environ       ,    {'WANDB_MODE': 'offline'}    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			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    )
							_UpperCAmelCase									=			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'    )    )    )








from __future__ import annotations


def __A    (      __lowerCAmelCase	)->  list[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			2
						_UpperCAmelCase									=			[]
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(__lowerCAmelCase	)
						if n > 1:
												factors.append(__lowerCAmelCase	)
						return factors


if __name__ == "__main__":
		import doctest

		doctest.testmod()
