"""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)

__UpperCamelCase									=			logging.getLogger()







def 					UpperCAmelCase    (		)				->      List[str]:
     snake_case_					       =					argparse.ArgumentParser()
     parser.add_argument('-f'					)
     snake_case_					       =					parser.parse_args()
     return args.f







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Optional[int]:
     snake_case_					       =					{}
     snake_case_					       =					os.path.join(UpperCAmelCase						,					'all_results.json'					)
     if os.path.exists(UpperCAmelCase					):
          with open(UpperCAmelCase						,					'r'					) as f:
               snake_case_					       =					json.load(UpperCAmelCase					)
     else:
          raise ValueError(f'can\'t find {path}'					)
     return results







def 					UpperCAmelCase    (		)				->      Tuple:
     snake_case_					       =					torch.cuda.is_available() and torch_device == 'cuda'
     return is_using_cuda and is_apex_available()


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




class  UpperCamelCase					(				lowerCAmelCase__   ):







       @classmethod
       def 		a_						( cls)      ->      Optional[int]:
            # 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 		a_						( cls)      ->      Optional[int]:
            shutil.rmtree(cls.tmpdir)







       @mock.patch.dict(os.environ,							{'WANDB_MODE': 'offline'})
       def 		a_						( self)      ->      Union[str, Any]:
            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(lowerCAmelCase__)
            self.assertGreaterEqual(result['eval_accuracy'],							0.75)
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'epoch_0')))
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'glue_no_trainer')))







       @mock.patch.dict(os.environ,							{'WANDB_MODE': 'offline'})
       def 		a_						( self)      ->      Union[str, Any]:
            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(lowerCAmelCase__)
            self.assertLess(result['perplexity'],							100)
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'epoch_0')))
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'clm_no_trainer')))







       @mock.patch.dict(os.environ,							{'WANDB_MODE': 'offline'})
       def 		a_						( self)      ->      List[Any]:
            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(lowerCAmelCase__)
            self.assertLess(result['perplexity'],							42)
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'epoch_0')))
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'mlm_no_trainer')))







       @mock.patch.dict(os.environ,							{'WANDB_MODE': 'offline'})
       def 		a_						( self)      ->      List[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'\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(lowerCAmelCase__)
            self.assertGreaterEqual(result['eval_accuracy'],							0.75)
            self.assertLess(result['train_loss'],							0.5)
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'epoch_0')))
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'ner_no_trainer')))







       @unittest.skip(reason='Fix me @muellerzr')
       @mock.patch.dict(os.environ,							{'WANDB_MODE': 'offline'})
       def 		a_						( self)      ->      Optional[int]:
            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(lowerCAmelCase__)
            # 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(lowerCAmelCase__,							'epoch_0')))
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'qa_no_trainer')))







       @mock.patch.dict(os.environ,							{'WANDB_MODE': 'offline'})
       def 		a_						( self)      ->      Union[str, Any]:
            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(lowerCAmelCase__)
            self.assertGreaterEqual(result['eval_accuracy'],							0.8)
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'swag_no_trainer')))







       @slow
       @mock.patch.dict(os.environ,							{'WANDB_MODE': 'offline'})
       def 		a_						( self)      ->      Any:
            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(lowerCAmelCase__)
            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(lowerCAmelCase__,							'epoch_0')))
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'summarization_no_trainer')))







       @slow
       @mock.patch.dict(os.environ,							{'WANDB_MODE': 'offline'})
       def 		a_						( self)      ->      str:
            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(lowerCAmelCase__)
            self.assertGreaterEqual(result['eval_bleu'],							30)
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'epoch_0')))
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'translation_no_trainer')))







       @slow
       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					logging.StreamHandler(sys.stdout)
            logger.addHandler(lowerCAmelCase__)

            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(lowerCAmelCase__)
            self.assertGreaterEqual(result['eval_overall_accuracy'],							0.10)







       @mock.patch.dict(os.environ,							{'WANDB_MODE': 'offline'})
       def 		a_						( self)      ->      List[Any]:
            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(lowerCAmelCase__)
            # The base model scores a 25%
            self.assertGreaterEqual(result['eval_accuracy'],							0.6)
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'step_1')))
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__,							'image_classification_no_trainer')))






A__  :  Any								=					'''Tobias Carryer'''

from time import time



class 				__snake_case						:




							def __init__(       self     :       Any				,				A_     :       Tuple				,				A_     :       Dict				,				A_     :       Tuple				,				A_     :       str=int(time())):  # noqa: B008
									lowerCAmelCase_			:     int									= multiplier
									lowerCAmelCase_			:     int									= increment
									lowerCAmelCase_			:     str									= modulo
									lowerCAmelCase_			:     str									= seed







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Optional[int]									= (self.multiplier * self.seed + self.increment) % self.modulo
									return self.seed


if __name__ == "__main__":
						# Show the LCG in action.
						A__  :  Union[str, Any]								=					LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
						while True:
												print(lcg.next_number())





