





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      lowerCAmelCase_ (       )     ->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =argparse.ArgumentParser()
  parser.add_argument("-f"		)
  lowerCamelCase__:      Any			  =parser.parse_args()
  return args.f





def      lowerCAmelCase_ (       __a		)     ->		str:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  ={}
  lowerCamelCase__:      str			  =os.path.join(__a	,    "all_results.json"		)
  if os.path.exists(__a		):
    with open(__a	,    "r"		) as f:
      lowerCamelCase__:      Optional[Any]			  =json.load(__a		)
  else:
    raise ValueError(F"""can't find {path}"""		)
  return results





def      lowerCAmelCase_ (       )     ->		str:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  =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    _SCREAMING_SNAKE_CASE						(	UpperCAmelCase_    ):







 '''simple docstring'''

 @classmethod
 def 							SCREAMING_SNAKE_CASE_					(cls				:   str)       ->int:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =tempfile.mkdtemp()
   lowerCamelCase__:      List[Any]			  =os.path.join(cls.tmpdir       , "default_config.yml")
   write_basic_config(save_location=cls.configPath)
   lowerCamelCase__:      int			  =['''accelerate''', '''launch''', '''--config_file''', cls.configPath]

 @classmethod
 def 							SCREAMING_SNAKE_CASE_					(cls				:   List[Any])       ->Union[str, Any]:



   '''simple docstring'''



   shutil.rmtree(cls.tmpdir)

 @mock.patch.dict(os.environ       , {"WANDB_MODE": "offline"})
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      Any			  =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__:      Optional[Any]			  =get_results(__lowercase)
   self.assertGreaterEqual(result["eval_accuracy"]       , 0.75)
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "epoch_0")))
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "glue_no_trainer")))

 @mock.patch.dict(os.environ       , {"WANDB_MODE": "offline"})
 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->int:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      List[str]			  =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__:      Dict			  =get_results(__lowercase)
   self.assertLess(result["perplexity"]       , 100)
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "epoch_0")))
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "clm_no_trainer")))

 @mock.patch.dict(os.environ       , {"WANDB_MODE": "offline"})
 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      List[Any]			  =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__:      Union[str, Any]			  =get_results(__lowercase)
   self.assertLess(result["perplexity"]       , 42)
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "epoch_0")))
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "mlm_no_trainer")))

 @mock.patch.dict(os.environ       , {"WANDB_MODE": "offline"})
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =7 if get_gpu_count() > 1 else 2

   lowerCamelCase__:      List[Any]			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      List[Any]			  =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__:      Optional[Any]			  =get_results(__lowercase)
   self.assertGreaterEqual(result["eval_accuracy"]       , 0.75)
   self.assertLess(result["train_loss"]       , 0.5)
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "epoch_0")))
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "ner_no_trainer")))

 @unittest.skip(reason="Fix me @muellerzr")
 @mock.patch.dict(os.environ       , {"WANDB_MODE": "offline"})
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      int			  =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__:      Any			  =get_results(__lowercase)
   # 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(__lowercase       , "epoch_0")))
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "qa_no_trainer")))

 @mock.patch.dict(os.environ       , {"WANDB_MODE": "offline"})
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      int			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      int			  =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__:      Dict			  =get_results(__lowercase)
   self.assertGreaterEqual(result["eval_accuracy"]       , 0.8)
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "swag_no_trainer")))

 @slow
 @mock.patch.dict(os.environ       , {"WANDB_MODE": "offline"})
 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->str:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      Dict			  =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__:      Tuple			  =get_results(__lowercase)
   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(__lowercase       , "epoch_0")))
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "summarization_no_trainer")))

 @slow
 @mock.patch.dict(os.environ       , {"WANDB_MODE": "offline"})
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      Dict			  =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__:      Optional[int]			  =get_results(__lowercase)
   self.assertGreaterEqual(result["eval_bleu"]       , 30)
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "epoch_0")))
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "translation_no_trainer")))

 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =logging.StreamHandler(sys.stdout)
   logger.addHandler(__lowercase)

   lowerCamelCase__:      int			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      Any			  =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__:      List[str]			  =get_results(__lowercase)
   self.assertGreaterEqual(result["eval_overall_accuracy"]       , 0.10)







 @mock.patch.dict(os.environ       , {"WANDB_MODE": "offline"})
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =self.get_auto_remove_tmp_dir()
   lowerCamelCase__:      Optional[Any]			  =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__:      Optional[Any]			  =get_results(__lowercase)
   # The base model scores a 25%
   self.assertGreaterEqual(result["eval_accuracy"]       , 0.6)
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "step_1")))
   self.assertTrue(os.path.exists(os.path.join(__lowercase       , "image_classification_no_trainer")))


import random



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		a[left_index]
    __UpperCamelCase     :Any			  =		left_index + 1
    for j in range(left_index + 1  ,			SCREAMING_SNAKE_CASE	):
        if a[j] < pivot:
            __UpperCamelCase  ,       __UpperCamelCase     :str			  =		a[i], a[j]
            i += 1
    __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		a[i - 1], a[left_index]
    return i - 1



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    if left < right:
        __UpperCamelCase     :int			  =		random.randint(SCREAMING_SNAKE_CASE  ,			right - 1	)
        __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        __UpperCamelCase     :Dict			  =		partition(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        quick_sort_random(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            SCREAMING_SNAKE_CASE  ,			pivot_index + 1  ,			SCREAMING_SNAKE_CASE	)  # recursive quicksort to the right of the pivot point



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		input('''Enter numbers separated by a comma:\n'''	).strip()
    __UpperCamelCase     :Union[str, Any]			  =		[int(SCREAMING_SNAKE_CASE	) for item in user_input.split(''','''	)]

    quick_sort_random(SCREAMING_SNAKE_CASE  ,			0  ,			len(SCREAMING_SNAKE_CASE	)	)

    print(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    main()





