





'''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)

a__							:							Any =logging.getLogger()
def 				lowercase__   (   ) ->      List[Any]:



       """simple docstring"""






       __UpperCamelCase       =  argparse.ArgumentParser()
       parser.add_argument('-f'     )
       __UpperCamelCase       =  parser.parse_args()
       return args.f
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  {}
       __UpperCamelCase       =  os.path.join(__lowercase				,  'all_results.json'     )
       if os.path.exists(__lowercase     ):
              with open(__lowercase				,  'r'     ) as f:
                     __UpperCamelCase       =  json.load(__lowercase     )
       else:
              raise ValueError(F'''can\'t find {path}'''     )
       return results
def 				lowercase__   (   ) ->      Dict:



       """simple docstring"""






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


a__							:							str =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 @classmethod
 def        _lowerCamelCase					(       cls       :							List[Any]	):
        # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
        __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        _lowerCamelCase					(       cls       :							int	):
        shutil.rmtree(cls.tmpdir	)






 @mock.patch.dict(os.environ							,	{'WANDB_MODE': 'offline'}	)
 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __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(__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        _lowerCamelCase					(       self       :							List[str]	):
        __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(__A	)
        self.assertLess(result['perplexity']							,	1_0_0	)
        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       :							Dict	):
        __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(__A	)
        self.assertLess(result['perplexity']							,	4_2	)
        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       :							List[str]	):
        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
        __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(__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        _lowerCamelCase					(       self       :							Optional[Any]	):
        __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(__A	)
        # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
        self.assertGreaterEqual(result['eval_f1']							,	2_8	)
        self.assertGreaterEqual(result['eval_exact']							,	2_8	)
        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       :							Optional[Any]	):
        __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(__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       :							List[Any]	):
        __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(__A	)
        self.assertGreaterEqual(result['eval_rouge1']							,	1_0	)
        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       :							int	):
        __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(__A	)
        self.assertGreaterEqual(result['eval_bleu']							,	3_0	)
        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	):
        __UpperCamelCase       =  logging.StreamHandler(sys.stdout	)
        logger.addHandler(__A	)

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






 @mock.patch.dict(os.environ							,	{'WANDB_MODE': 'offline'}	)
 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __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(__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 random
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  Optional[Any]     ) ->      tuple:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  [], [], []
       for element in data:
              if element < pivot:
                     less.append(__lowercase     )
              elif element > pivot:
                     greater.append(__lowercase     )
              else:
                     equal.append(__lowercase     )
       return less, equal, greater


def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       if index >= len(__lowercase     ) or index < 0:
              return None

       __UpperCamelCase       =  items[random.randint(0				,  len(__lowercase     ) - 1     )]
       __UpperCamelCase       =  0
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _partition(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(__lowercase				,  __lowercase     )
       # must be in larger
       else:
              return quick_select(__lowercase				,  index - (m + count)     )
